diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..bed0738c7eeb449bca98b5d2f33c89a1ee56349a --- /dev/null +++ b/.gitattributes @@ -0,0 +1,60 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.avro filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.lz4 filter=lfs diff=lfs merge=lfs -text +*.mds filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +# Audio files - uncompressed +*.pcm filter=lfs diff=lfs merge=lfs -text +*.sam filter=lfs diff=lfs merge=lfs -text +*.raw filter=lfs diff=lfs merge=lfs -text +# Audio files - compressed +*.aac filter=lfs diff=lfs merge=lfs -text +*.flac filter=lfs diff=lfs merge=lfs -text +*.mp3 filter=lfs diff=lfs merge=lfs -text +*.ogg filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text +# Image files - uncompressed +*.bmp filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.tiff filter=lfs diff=lfs merge=lfs -text +# Image files - compressed +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text +*.webp filter=lfs diff=lfs merge=lfs -text +# Video files - compressed +*.mp4 filter=lfs diff=lfs merge=lfs -text +*.webm filter=lfs diff=lfs merge=lfs -text diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..32eaa5aeaebce82e6a6564ac4e821bd2ef71c3ba --- /dev/null +++ b/LICENSE @@ -0,0 +1 @@ +Unless otherwise noted, TraceBench benchmark data and public artifacts are released for research use. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ab6ac6a120cf973efda2418d362f5784b1c6b5ee --- /dev/null +++ b/README.md @@ -0,0 +1,34 @@ +--- +license: other +pretty_name: TraceBench +--- + +# TraceBench + +This dataset contains the canonical public benchmark data, synchronized aggregate results, trajectories, website data, and accepted submission artifacts for TraceBench. + +The matching evaluation and reproduction code is available at [TommasoBendinelli/TraceBench](https://github.com/TommasoBendinelli/TraceBench). + +## Layout + +- `questions/BallDrop/`, `questions/BounceBall/`, and `questions/MassSlide/` contain the three canonical benchmark environments. +- `results.csv` and `results.parquet` contain the same 420 aggregate result rows and 47 columns. +- `submissions/` contains the four accepted submission bundles rebuilt from the exact 420 source trajectories selected by the paper. +- `website/` contains the static website data. +- `release_manifest.json` records hashes and row coverage for the synchronized release. + +## Results provenance + +Accuracy and resource metrics are synchronized to the paper's 420-row execution record, which contains 360 `PAPER` rows and 60 `CALIBRATION` rows. + +The four deprecated `llm_*` classifier columns were removed, and the paper-backed `python_statements`, `total_time_seconds`, `elapsed_time_seconds`, `session_limit_wait_seconds`, and `total_time_method` fields were added. + +Console-output shares use the paper's raw-ATIF recalculation for the 240 main-condition rows, while the remaining trajectory metrics use the authoritative evaluation selected for each paper row. + +GPT-5.5 costs use the standard text-token rates of $5.00 input, $0.50 cached input, and $30.00 output per million tokens from the [official OpenAI documentation](https://developers.openai.com/api/docs/models/gpt-5.5). + +## Privacy and 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parameter changed", + "status": "success", + "timestamp": "2026-05-01T12:22:57.531271", + "end_time_simulation": 9.9975004196167 + }, + "cfc1dde0ab3fe2803be3d63dce48d931": { + "parameters_hash": "819df5dc57387bd138a5a6b2f731d574", + "run_type": "intervention", + "class_internal": "mass", + "class_agent_facing_name": "mass", + "status": "success", + "timestamp": "2026-05-01T12:22:59.781338", + "end_time_simulation": 9.9975004196167 + }, + "0e1a49d59cab45b388d69aa0723cedde": { + "parameters_hash": "6b7ac02ca33e0ef943b7845f0b3f5c83", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-01T12:23:01.842860", + "end_time_simulation": 9.9975004196167 + }, + "6c0a4bc64f2f4ff99f76cd41e1ab474d": { + "parameters_hash": "2fe840cf37fbab215afa443314133d86", + "run_type": "intervention", + "class_internal": "mass", + "class_agent_facing_name": "mass", + "status": "success", + "timestamp": "2026-05-02T19:24:02.267274", 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"f4dd9a2bec0b4738b60e5df230c36033": { + "parameters_hash": "1e0468810c6933ebdb199fca33dacb9c", + "run_type": "intervention", + "class_internal": "restitution", + "class_agent_facing_name": "restitution", + "status": "success", + "timestamp": "2026-05-01T12:23:12.678473", + "end_time_simulation": 9.9975004196167 + }, + "ef0b2f9ad39a5e36be60ad2080781e67": { + "parameters_hash": "6e37c79d91d023b349d1be7c3fde428a", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-01T12:23:14.663158", + "end_time_simulation": 9.9975004196167 + }, + "b7c15bc7416f4169b9f2a7166c4865e3": { + "parameters_hash": "04f9d1b0727010f396fb1684a5c41598", + "run_type": "intervention", + "class_internal": "gravity", + "class_agent_facing_name": "gravity", + "status": "success", + "timestamp": "2026-05-01T12:23:16.904164", + "end_time_simulation": 9.9975004196167 + }, + "8ea9c8749d735a8385f6a6ae2f277973": { + "parameters_hash": "d78d997c3cbfb515ea55be7a1a582626", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-01T12:23:18.976601", + "end_time_simulation": 9.9975004196167 + }, + "768257b526264466aff05fd9c41ab915": { + "parameters_hash": "63c775e5f88ff8d94dfea59a5b6eb0ea", + "run_type": "intervention", + "class_internal": "gravity", + "class_agent_facing_name": "gravity", + "status": "success", + "timestamp": "2026-05-01T12:23:21.176071", + "end_time_simulation": 9.9975004196167 + }, + "060804538b7c42faa451b30b3de7010a": { + "parameters_hash": "3abba47258caa06d30f7c0b5edbcd2c5", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-01T12:23:23.238477", + "end_time_simulation": 9.9975004196167 + }, + "03f1084bfb7149eaa6455d85da755ae7": { + "parameters_hash": "3eab57ef8c2354abef8985f8bd0d611b", + "run_type": "baseline", + "class_internal": "no_parameter_change", + "class_agent_facing_name": "no parameter changed", + "status": "failed", + "timestamp": "2026-05-01T02:46:27.202323", + "error": "\"Unknown BallDrop feature 'restitution_estimate'.\"\nTraceback (most recent call last):\n File \"/models/simulink/BallDrop/features.py\", line 1366, in _compute_feature_from_context\n compute = _FEATURE_COMPUTERS[str(feature_name)]\nKeyError: 'restitution_estimate'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1282, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 879, in simulate_recipe\n feature_dict = compute_problem_specific_features(\n File \"/shared/simulation.py\", line 539, in compute_problem_specific_features\n fn(all_signal_simulation, feature_names=requested_names)\n File \"/models/simulink/BallDrop/features.py\", line 1448, in compute_features\n return {\n File \"/models/simulink/BallDrop/features.py\", line 1449, in \n feature_name: _compute_feature_from_context(context, feature_name)\n File \"/models/simulink/BallDrop/features.py\", line 1368, in _compute_feature_from_context\n raise KeyError(f\"Unknown BallDrop feature '{feature_name}'.\") from exc\nKeyError: \"Unknown BallDrop feature 'restitution_estimate'.\"\n" + }, + "b13c1ce73cecae86b9e01a0556a66164": { + "parameters_hash": "482f35b042f49ff660b4d714e5d0afaf", + "run_type": "intervention", + "class_internal": "mass", + "class_agent_facing_name": "mass", + "status": "failed", + "timestamp": "2026-05-01T02:46:30.678758", + "error": "\"Unknown BallDrop feature 'restitution_estimate'.\"\nTraceback (most recent call last):\n File \"/models/simulink/BallDrop/features.py\", line 1366, in _compute_feature_from_context\n compute = _FEATURE_COMPUTERS[str(feature_name)]\nKeyError: 'restitution_estimate'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1282, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 879, in simulate_recipe\n feature_dict = compute_problem_specific_features(\n File \"/shared/simulation.py\", line 539, in compute_problem_specific_features\n fn(all_signal_simulation, feature_names=requested_names)\n File \"/models/simulink/BallDrop/features.py\", line 1448, in compute_features\n return {\n File \"/models/simulink/BallDrop/features.py\", line 1449, in \n feature_name: _compute_feature_from_context(context, feature_name)\n File \"/models/simulink/BallDrop/features.py\", line 1368, in _compute_feature_from_context\n raise KeyError(f\"Unknown BallDrop feature '{feature_name}'.\") from exc\nKeyError: \"Unknown BallDrop feature 'restitution_estimate'.\"\n" + }, + "98fec1b850e24f76bc59e0be4992e509": { + "parameters_hash": "09cf7d9724be516df21be29c1af100da", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "failed", + "timestamp": "2026-05-01T02:46:33.058981", + "error": "\"Unknown BallDrop feature 'restitution_estimate'.\"\nTraceback (most recent call last):\n File \"/models/simulink/BallDrop/features.py\", line 1366, in _compute_feature_from_context\n compute = _FEATURE_COMPUTERS[str(feature_name)]\nKeyError: 'restitution_estimate'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1282, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 879, in simulate_recipe\n feature_dict = compute_problem_specific_features(\n File \"/shared/simulation.py\", line 539, in compute_problem_specific_features\n fn(all_signal_simulation, feature_names=requested_names)\n File \"/models/simulink/BallDrop/features.py\", line 1448, in compute_features\n return {\n File \"/models/simulink/BallDrop/features.py\", line 1449, in \n feature_name: _compute_feature_from_context(context, feature_name)\n File \"/models/simulink/BallDrop/features.py\", line 1368, in _compute_feature_from_context\n raise KeyError(f\"Unknown BallDrop feature '{feature_name}'.\") from exc\nKeyError: \"Unknown BallDrop feature 'restitution_estimate'.\"\n" + }, + "8a2fa4f4d87b45d2990a254a78aaa20c": { + "parameters_hash": "1e0b6a58e2faad407f9bfb3c3cbd9922", + "run_type": "intervention", + "class_internal": "drag_coeff", + "class_agent_facing_name": "drag_coeff", + "status": "failed", + "timestamp": "2026-05-01T02:46:36.342435", + "error": "Unable to access ModelWorkspace for model 'simulink_model'\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 297, in apply_ModelWorkspace\n mle.eval(\n File \"/env/lib/python3.10/site-packages/matlab/engine/matlabengine.py\", line 71, in __call__\n _stderr, feval=True).result()\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: Invalid Simulink object name: 'simulink_model'.\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1282, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = sim_module._simulate_case_to_signal_dict(\n File \"/workflows/simulate_core.py\", line 1040, in _simulate_case_to_signal_dict\n apply_ModelWorkspace(\n File \"/workflows/simulate_core.py\", line 303, in apply_ModelWorkspace\n raise RuntimeError(\nRuntimeError: Unable to access ModelWorkspace for model 'simulink_model'\n" + }, + "eceb4dd37439452499bd677ceab1c1ea": { + "parameters_hash": "ef89459ac7993cc1cc3d3178f9a3050a", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "failed", + "timestamp": "2026-05-01T02:46:36.835066", + "error": "MATLAB simulation returned no result\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1282, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = sim_module._simulate_case_to_signal_dict(\n File \"/workflows/simulate_core.py\", line 1071, in _simulate_case_to_signal_dict\n res = run_simulation(\n File \"/workflows/simulate_core.py\", line 922, in run_simulation\n raise RuntimeError(\"MATLAB simulation returned no result\")\nRuntimeError: MATLAB simulation returned no result\n" + }, + "761b44fbee5e464badae4e94c2c8e0be": { + "parameters_hash": "bd94f94fd2672ce364e2d4843dd66565", + "run_type": "intervention", + "class_internal": "restitution", + "class_agent_facing_name": "restitution", + "status": "failed", + "timestamp": "2026-05-01T02:46:37.318351", + "error": "MATLAB simulation returned no result\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1282, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = sim_module._simulate_case_to_signal_dict(\n File \"/workflows/simulate_core.py\", line 1071, in _simulate_case_to_signal_dict\n res = run_simulation(\n File \"/workflows/simulate_core.py\", line 922, in run_simulation\n raise RuntimeError(\"MATLAB simulation returned no result\")\nRuntimeError: MATLAB simulation returned no result\n" + }, + "b02ee6b4abac447fbb757d9fcc70e402": { + 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"Hard_Stop_f" + + +def _scaled_noise_profile( + profile: dict[str, float], + *, + scale: float, + unscaled_keys: set[str] | None = None, +) -> dict[str, float]: + fixed = unscaled_keys or set() + return { + key: float(value) if key in fixed else float(value) * float(scale) + for key, value in profile.items() + } + + +LOW = { + "position_base_sigma_scale": 0.002, + "position_drift_sigma_scale": 0.002, + "position_event_sigma_scale": 0.002, + "position_quant_step_scale": 0.002, + "position_quant_step_floor": 1e-4, + "velocity_base_sigma_scale": 0.002, + "velocity_hetero_sigma_scale": 0.002, + "velocity_drift_sigma_scale": 0.002, + "velocity_event_sigma_scale": 0.002, + "force_base_sigma_scale": 0.002, + "force_hetero_sigma_scale": 0.002, + "force_event_sigma_scale": 0.002, +} + +HIGH_SCALE = 4.0 +HIGH = _scaled_noise_profile( + LOW, + scale=HIGH_SCALE, +) + +NOISE_DICT = {"low": LOW, "high": HIGH} +SNR_THR_DICT = { + "low": {"global": [-10000, -10000,-10000], "local": [-10000, -10000, -10000]}, + "high": {"global": [-10000, -10000,-10000], "local": [-10000, -10000, -10000]}, +} +_MAX_NOISE_RESAMPLE_ATTEMPTS = 25 + + +def _analysis_meets_thresholds( + noise_analysis: dict[str, list[float | str | None]], + *, + noise_level: str, +) -> bool: + thresholds = SNR_THR_DICT[noise_level] + for scope in ("global", "local"): + values = noise_analysis.get(scope, []) + limit_values = thresholds.get(scope, []) + for idx, raw_value in enumerate(values): + if raw_value is None or idx >= len(limit_values): + continue + value = float(raw_value) + if np.isfinite(value) and value < float(limit_values[idx]): + return False + return True + + +def _rng(seed: int, key: str) -> np.random.Generator: + derived = (int(seed) ^ hash_string(key)) & 0xFFFFFFFF + return np.random.default_rng(derived) + + +def _values(df: pd.DataFrame, column: str) -> np.ndarray: + return pd.to_numeric(df[column], errors="coerce").to_numpy(dtype=float) + + +def _finite_scale(values: np.ndarray) -> float: + finite = values[np.isfinite(values)] + if finite.size == 0: + return 1.0 + spread = float(np.nanmax(finite) - np.nanmin(finite)) + rms = float(np.sqrt(np.mean(finite**2))) + return max(spread, rms, 1e-6) + + +def _coefficients(profile: str) -> dict[str, float]: + normalized = str(profile or "low").strip().lower() + if normalized == "low": + return LOW + if normalized == "high": + return HIGH + raise ValueError(f"Unknown noise profile '{profile}'. Expected 'low' or 'high'.") + + +def _smooth(values: np.ndarray) -> np.ndarray: + kernel = np.array([0.2, 0.3, 0.3, 0.2], dtype=float) + return np.convolve(values, kernel, mode="same") + + +def _drift(rng: np.random.Generator, n: int, scale: float) -> np.ndarray: + if n <= 0 or scale <= 0.0: + return np.zeros(n, dtype=float) + return _smooth(_smooth(rng.normal(0.0, scale, size=n))) + + +def _bounce_mask(position: np.ndarray, velocity: np.ndarray) -> np.ndarray: + n = min(position.size, velocity.size) + out = np.zeros(n, dtype=bool) + if n == 0: + return out + floor = float(np.nanmin(position[np.isfinite(position)])) if np.isfinite(position).any() else 0.0 + for idx in range(1, n): + if not np.isfinite(velocity[idx - 1]) or not np.isfinite(velocity[idx]): + continue + if not np.isfinite(position[idx]): + continue + is_bounce = velocity[idx - 1] < 0.0 and velocity[idx] > 0.0 and position[idx] <= floor + 0.1 + if is_bounce: + lo = max(0, idx - 2) + hi = min(n, idx + 3) + out[lo:hi] = True + return out + + +def _add_noise_once(df: pd.DataFrame, seed: int = 0, profile: str = "low") -> pd.DataFrame: + coeffs = _coefficients(profile) + out = df.copy() + if ( + _POSITION_COLUMN not in out.columns + and _VELOCITY_COLUMN not in out.columns + and _FORCE_COLUMN not in out.columns + ): + return out + + position = ( + _values(out, _POSITION_COLUMN) + if _POSITION_COLUMN in out.columns + else np.array([], dtype=float) + ) + velocity = ( + _values(out, _VELOCITY_COLUMN) + if _VELOCITY_COLUMN in out.columns + else np.array([], dtype=float) + ) + bounces = _bounce_mask(position, velocity) + + if _POSITION_COLUMN in out.columns: + values = position + scale = _finite_scale(values) + rng = _rng(seed, _POSITION_COLUMN) + noisy = values.copy() + noisy += rng.normal( + 0.0, + coeffs["position_base_sigma_scale"] * scale, + size=values.size, + ) + noisy += _drift(rng, values.size, coeffs["position_drift_sigma_scale"] * scale) + if bounces.size == values.size: + noisy += ( + rng.normal( + 0.0, + coeffs["position_event_sigma_scale"] * scale, + size=values.size, + ) + * bounces.astype(float) + ) + quant_step = max( + coeffs["position_quant_step_scale"] * scale, + coeffs["position_quant_step_floor"], + ) + noisy = np.round(noisy / quant_step) * quant_step + out[_POSITION_COLUMN] = np.maximum(noisy, 0.0) + + if _VELOCITY_COLUMN in out.columns: + values = velocity + scale = _finite_scale(values) + rng = _rng(seed, _VELOCITY_COLUMN) + speed = np.abs(values) + ref = float(np.nanmedian(speed[np.isfinite(speed)])) if np.isfinite(speed).any() else 0.0 + sigma = ( + coeffs["velocity_base_sigma_scale"] * scale + + coeffs["velocity_hetero_sigma_scale"] * np.maximum(speed, ref) + ) + noisy = values.copy() + noisy += rng.normal(0.0, sigma, size=values.size) + noisy += _drift(rng, values.size, coeffs["velocity_drift_sigma_scale"] * scale) + if bounces.size == values.size: + noisy += ( + rng.normal( + 0.0, + coeffs["velocity_event_sigma_scale"] * scale, + size=values.size, + ) + * bounces.astype(float) + ) + out[_VELOCITY_COLUMN] = noisy + + if _FORCE_COLUMN in out.columns: + values = _values(out, _FORCE_COLUMN) + scale = _finite_scale(values) + rng = _rng(seed, _FORCE_COLUMN) + magnitude = np.abs(values) + ref = ( + float(np.nanmedian(magnitude[np.isfinite(magnitude)])) + if np.isfinite(magnitude).any() + else 0.0 + ) + sigma = ( + coeffs["force_base_sigma_scale"] * scale + + coeffs["force_hetero_sigma_scale"] * np.maximum(magnitude, ref) + ) + noisy = values.copy() + noisy += rng.normal(0.0, sigma, size=values.size) + if bounces.size == values.size: + noisy += ( + rng.normal( + 0.0, + coeffs["force_event_sigma_scale"] * scale, + size=values.size, + ) + * bounces.astype(float) + ) + out[_FORCE_COLUMN] = noisy + + return out + + +def quantify_noise( + clean: pd.DataFrame, + noisy: pd.DataFrame, + baseline: pd.DataFrame | None, +) -> dict[str, list[float | str | None]]: + first_diff = first_detectable_time_from_baseline(clean, baseline) + analysis = quantify_analysis( + clean, + noisy, + reference_df=baseline, + first_diff=first_diff, + local_pre_rows=DOCUMENTED_LOCAL_NOISE_ANALYSIS_PRE_ROWS, + local_post_rows=DOCUMENTED_LOCAL_NOISE_ANALYSIS_POST_ROWS, + ) + if first_diff is None or "local" not in analysis: + analysis["local"] = [None] * len(analysis.get("global", [])) + return analysis + + +def add_noise( + clean: pd.DataFrame, + baseline: pd.DataFrame | None, + seed: int = 0, + noise_level: str = "low", +) -> tuple[pd.DataFrame, dict[str, list[float | str | None]]]: + normalized = str(noise_level or "low").strip().lower() + if normalized not in NOISE_DICT: + raise ValueError(f"Unknown noise level '{noise_level}'. Expected 'low' or 'high'.") + current_seed = int(seed) + for _attempt in range(_MAX_NOISE_RESAMPLE_ATTEMPTS + 1): + noisy_df = _add_noise_once(clean, seed=current_seed, profile=normalized) + noise_analysis = quantify_noise(clean, noisy_df, baseline) + if _analysis_meets_thresholds(noise_analysis, noise_level=normalized): + return noisy_df, noise_analysis + current_seed += 1000 + raise RuntimeError( + f"Could not satisfy minimum SNR thresholds for noise level '{normalized}' " + f"after {_MAX_NOISE_RESAMPLE_ATTEMPTS + 1} attempts." + ) + + +__all__ = ["HIGH", "LOW", "NOISE_DICT", "SNR_THR_DICT", "add_noise", "quantify_noise"] diff --git a/questions/BallDrop/questions.json b/questions/BallDrop/questions.json new file mode 100644 index 0000000000000000000000000000000000000000..3f28c6b830c7407e44d63b39b076fe975acc2fab --- /dev/null +++ b/questions/BallDrop/questions.json @@ -0,0 +1,28555 @@ +{ + "version": 8, + "questions": { + "frost_01234-anchor_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "frost_01234-anchor_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "frost_01234-anchor_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "frost_01234-anchor_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "frost_01234-anchor_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "fern_01234-anchor_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "fern_01234-anchor_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "fern_01234-anchor_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "fern_01234-anchor_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "fern_01234-anchor_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "gentle_01234-anchor_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "gentle_01234-anchor_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "gentle_01234-anchor_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "gentle_01234-anchor_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "gentle_01234-anchor_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "frost_01234-cloud_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "frost_01234-cloud_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "frost_01234-cloud_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "frost_01234-cloud_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "frost_01234-cloud_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "fern_01234-cloud_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "fern_01234-cloud_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "fern_01234-cloud_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "fern_01234-cloud_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "fern_01234-cloud_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "gentle_01234-cloud_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "gentle_01234-cloud_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "gentle_01234-cloud_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "gentle_01234-cloud_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "gentle_01234-cloud_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "frost_01234-pine_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "frost_01234-pine_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "frost_01234-pine_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "frost_01234-pine_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "frost_01234-pine_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "fern_01234-pine_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "fern_01234-pine_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "fern_01234-pine_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "fern_01234-pine_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "fern_01234-pine_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "gentle_01234-pine_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "gentle_01234-pine_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "gentle_01234-pine_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "gentle_01234-pine_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "gentle_01234-pine_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "frost_01234-prairie_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "frost_01234-prairie_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "frost_01234-prairie_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "frost_01234-prairie_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "frost_01234-prairie_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "fern_01234-prairie_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "fern_01234-prairie_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "fern_01234-prairie_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "fern_01234-prairie_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "fern_01234-prairie_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "gentle_01234-prairie_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "gentle_01234-prairie_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "gentle_01234-prairie_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "gentle_01234-prairie_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "gentle_01234-prairie_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "frost_01234-spruce_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "frost_01234-spruce_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "frost_01234-spruce_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "frost_01234-spruce_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "frost_01234-spruce_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "fern_01234-spruce_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "fern_01234-spruce_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "fern_01234-spruce_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "fern_01234-spruce_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "fern_01234-spruce_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "gentle_01234-spruce_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "gentle_01234-spruce_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "gentle_01234-spruce_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "gentle_01234-spruce_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "gentle_01234-spruce_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "frost_01234-comet_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "frost_01234-comet_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "frost_01234-comet_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "frost_01234-comet_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "frost_01234-comet_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "fern_01234-comet_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "fern_01234-comet_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "fern_01234-comet_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "fern_01234-comet_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "fern_01234-comet_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "gentle_01234-comet_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "gentle_01234-comet_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "gentle_01234-comet_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "gentle_01234-comet_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "gentle_01234-comet_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "frost_01234-meadow_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "frost_01234-meadow_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "frost_01234-meadow_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "frost_01234-meadow_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "frost_01234-meadow_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "fern_01234-meadow_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "fern_01234-meadow_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "fern_01234-meadow_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "fern_01234-meadow_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "fern_01234-meadow_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "gentle_01234-meadow_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "gentle_01234-meadow_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "gentle_01234-meadow_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "gentle_01234-meadow_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "gentle_01234-meadow_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "frost_01234-river_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "frost_01234-river_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "frost_01234-river_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "frost_01234-river_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "frost_01234-river_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "fern_01234-river_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "fern_01234-river_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "fern_01234-river_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "fern_01234-river_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "fern_01234-river_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "gentle_01234-river_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "gentle_01234-river_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "gentle_01234-river_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "gentle_01234-river_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "gentle_01234-river_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "frost_01234-harbor_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "frost_01234-harbor_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "frost_01234-harbor_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "frost_01234-harbor_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "frost_01234-harbor_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "fern_01234-harbor_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "fern_01234-harbor_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "fern_01234-harbor_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "fern_01234-harbor_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "fern_01234-harbor_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "gentle_01234-harbor_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "gentle_01234-harbor_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "gentle_01234-harbor_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "gentle_01234-harbor_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "gentle_01234-harbor_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "frost_01234-willow_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "frost_01234-willow_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "frost_01234-willow_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "frost_01234-willow_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "frost_01234-willow_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "fern_01234-willow_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "fern_01234-willow_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "fern_01234-willow_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "fern_01234-willow_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "fern_01234-willow_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "gentle_01234-willow_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "gentle_01234-willow_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "gentle_01234-willow_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "gentle_01234-willow_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "gentle_01234-willow_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "frost_01234-flame_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "frost_01234-flame_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "frost_01234-flame_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "frost_01234-flame_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "frost_01234-flame_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "fern_01234-flame_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "fern_01234-flame_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "fern_01234-flame_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "fern_01234-flame_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "fern_01234-flame_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "gentle_01234-flame_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "gentle_01234-flame_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "gentle_01234-flame_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "gentle_01234-flame_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "gentle_01234-flame_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "frost_01234-orbit_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "frost_01234-orbit_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "frost_01234-orbit_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "frost_01234-orbit_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "frost_01234-orbit_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "fern_01234-orbit_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "fern_01234-orbit_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "fern_01234-orbit_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "fern_01234-orbit_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "fern_01234-orbit_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "gentle_01234-orbit_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "gentle_01234-orbit_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "gentle_01234-orbit_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "gentle_01234-orbit_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "gentle_01234-orbit_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "frost_01234-trail_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "frost_01234-trail_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "frost_01234-trail_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "frost_01234-trail_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "frost_01234-trail_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "fern_01234-trail_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "fern_01234-trail_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "fern_01234-trail_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "fern_01234-trail_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "fern_01234-trail_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "gentle_01234-trail_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "gentle_01234-trail_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "gentle_01234-trail_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "gentle_01234-trail_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "gentle_01234-trail_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "frost_01234-island_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "frost_01234-island_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "frost_01234-island_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "frost_01234-island_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "frost_01234-island_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "fern_01234-island_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "fern_01234-island_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "fern_01234-island_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "fern_01234-island_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "fern_01234-island_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "gentle_01234-island_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "gentle_01234-island_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "gentle_01234-island_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "gentle_01234-island_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "gentle_01234-island_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "frost_01234-glade_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "frost_01234-glade_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "frost_01234-glade_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "frost_01234-glade_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "frost_01234-glade_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "fern_01234-glade_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "fern_01234-glade_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "fern_01234-glade_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "fern_01234-glade_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "fern_01234-glade_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "gentle_01234-glade_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "gentle_01234-glade_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "gentle_01234-glade_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "gentle_01234-glade_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "gentle_01234-glade_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "frost_01234-canyon_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "frost_01234-canyon_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "frost_01234-canyon_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "frost_01234-canyon_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "frost_01234-canyon_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "fern_01234-canyon_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "fern_01234-canyon_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "fern_01234-canyon_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "fern_01234-canyon_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "fern_01234-canyon_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "gentle_01234-canyon_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "gentle_01234-canyon_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "gentle_01234-canyon_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "gentle_01234-canyon_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "gentle_01234-canyon_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "frost_01234-ember_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "frost_01234-ember_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "frost_01234-ember_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "frost_01234-ember_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "frost_01234-ember_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "fern_01234-ember_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "fern_01234-ember_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "fern_01234-ember_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "fern_01234-ember_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "fern_01234-ember_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "gentle_01234-ember_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "gentle_01234-ember_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "gentle_01234-ember_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "gentle_01234-ember_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "gentle_01234-ember_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "frost_01234-tide_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "frost_01234-tide_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "frost_01234-tide_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "frost_01234-tide_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "frost_01234-tide_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "fern_01234-tide_0": { + "question_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "train_test_sample_hash": "37daf54b39a161a3aa07b50ce8602f3e73c4842d37736f4507c9ce5e32acf031", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "fern_01234-tide_1": { + "question_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "train_test_sample_hash": "cc4086b420e04ce3fde53470a9af6f7e0aa8ef65beada93471150e145cef0f78", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "fern_01234-tide_2": { + "question_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "train_test_sample_hash": "7b6e88464e899753dc56ed1bdd5f533d36fdbe43782068550b68f5b9f8f164c8", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "fern_01234-tide_3": { + "question_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "train_test_sample_hash": "e602de54114b9722633ceb57592b1a1470ed0793432fd3c586d9d3e411da0ce5", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "fern_01234-tide_4": { + "question_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "train_test_sample_hash": "03809ec07caa89653526f89ff39f741d7359a485cc91619bba8ec54e45369e54", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "gentle_01234-tide_0": { + "question_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "train_test_sample_hash": "b339fec75bc5ee04724ae000b411dad4dc0ca43ecfe6282e187886b7526c1859", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "gentle_01234-tide_1": { + "question_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "train_test_sample_hash": "17756883d39570e7b0565b3bb72f7e6cef4635d36106fcda0bfdbc16f584cae1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "gentle_01234-tide_2": { + "question_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "train_test_sample_hash": "35387e92771a8892c43272680404c5461093fc52c58e374a68799a088b27f54e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "gentle_01234-tide_3": { + "question_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "train_test_sample_hash": "287a50567f251427ca23137233fb5a606d0f79c0e0f0bbb9c1b54bbc2f211287", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "gentle_01234-tide_4": { + "question_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "train_test_sample_hash": "79e93bab7a0362f6ebf9f8d9d529ef9d7093cee1ff9e1ea9b6499a56ced44d98", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "frost_01234-crest_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-crest_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-crest_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-crest_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-crest_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-star_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-star_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-star_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-star_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-star_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-forest_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-forest_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-forest_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-forest_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-forest_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a 1D vertical point-mass ball model under gravity.\nWhile the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity.\nGround interaction is modeled with a restitution-based hard-stop law.\nWhen the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1].\nThe rebound points upward and its magnitude equals e times the pre-impact speed.\nWhen the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-lagoon_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-lagoon_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-lagoon_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-lagoon_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-lagoon_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-quartz_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-quartz_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-quartz_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-quartz_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-quartz_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-dawn_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-dawn_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-dawn_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-dawn_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-dawn_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: ball height\ncol2: ball velocity\ncol3: contact impulse (N*s)\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution", + "mass", + "drag coefficient", + "gravity acceleration", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution\", \"mass\", \"drag coefficient\", \"gravity acceleration\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-summit_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-summit_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-summit_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-summit_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-summit_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-aurora_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-aurora_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-aurora_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-aurora_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-aurora_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-valley_0": { + "question_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "train_test_sample_hash": "d72cb57930e8be0d8f09dbae1019b244181e0efd66e9ef6e6c54fc5105f73668", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-valley" + } + }, + "frost_01234-valley_1": { + "question_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "train_test_sample_hash": "efbe2c6821a0b59af799d44d069ea4ca5356457977bbd3a2a632a3de823dd8c2", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-valley" + } + }, + "frost_01234-valley_2": { + "question_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "train_test_sample_hash": "4ed450dd5a54875e751918e41943c4ba0a7717bd72b357b8dc1a9b360c914cad", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-valley" + } + }, + "frost_01234-valley_3": { + "question_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "train_test_sample_hash": "83a62d9c2d795ac172c9a2f9330534b9e95afa2c1c43be23caf6f58cd5aaa439", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-valley" + } + }, + "frost_01234-valley_4": { + "question_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "train_test_sample_hash": "78130909faa5c81019f6b515c46fb0b41b51ce8c1f41448e15f9ac09ddf88199", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-valley" + } + } + }, + "label_int_mapping": { + "coefficient of restitution": 0, + "mass": 1, + "drag coefficient": 2, + "gravity acceleration": 3, + "no parameter change": 4 + }, + "ground_truth_information": { + "pattern_observed": { + "bouncing_events": "List of impact times when the ball bounces on the ground.", + "apex_heights": "List of all the apex heights after each impact.", + "is_terminal_velocity": "Time intervals during which the ball is approximately at terminal velocity.", + "ball_on_the_ground": "Time from which the ball remains at rest on the ground, or JSON null if this never occurs.", + "pre_and_post_impact_velocities": "List of estimated pre-impact and post-impact velocity pairs for each detected bounce.", + "bounce_split_free_flight_parameter_estimates": "Per-bounce free-flight parameter estimates before and after a detected bounce.", + "impulse_over_diff_speed": "Ground reaction force divided by output velocity minus input velocity.", + "gravity_coefficient_estimates": "List of gravity and drag-coefficient estimates before and after free-flight splits.", + "discontinuity_in_speed_during_trajectory": "Change-point dictionaries detected during free flight where the velocity pattern changes abruptly." + }, + "interventions": { + "4d36b3bd1e524a49b337e2ac9a97206b": { + "initial_parameters": { + "drag_coeff": 0, + "restitution": 0.6, + "initial_height": 5.087829611649644, + "mass": 0.5, + "gravity": 5, + "initial_velocity": -6 + }, + "intervention_time": 2.55, + "changed_parameter": "no_parameter_change", + "new_value": null, + "first_diff": [ + null, + null, + null + ] + }, + "00ae112c60ec4dd1acd1bacbec2560c9": { + "initial_parameters": { + "drag_coeff": 0, + "restitution": 0.6, + "initial_height": 5.087829611649644, + "mass": 0.5, + "gravity": 5, + "initial_velocity": -6 + }, + "intervention_time": 2.55, + "changed_parameter": "restitution", + "new_value": 0.9175361531158589, + "first_diff": [ + 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"position_event_sigma_scale": 0.04, + "position_quant_step_scale": 0.0025, + "position_quant_step_floor": 0.0005, + "speed_base_sigma_scale": 0.00088, + "speed_hetero_sigma_scale": 0.0022, + "speed_drift_sigma_scale": 0.0001056, + "speed_event_sigma_scale": 0.00704, + "speed_post_event_sigma_scale": 0.00308, + "right_force_base_sigma_scale": 0.0001, + "right_force_event_sigma_scale": 0.004, +} +HIGH_SCALE = 4.0 +HIGH = {key: float(value) * HIGH_SCALE for key, value in LOW.items()} + + +NOISE_DICT = {"low": LOW, "high": HIGH} +SNR_THR_DICT = { + "low": {"global": [-1.0e9, -1.0e9, -1.0e9], "local": [-1.0e9, -1.0e9, -1.0e9]}, + "high": {"global": [-1.0e9, -1.0e9, -1.0e9], "local": [-1.0e9, -1.0e9, -1.0e9]}, +} +_MAX_NOISE_RESAMPLE_ATTEMPTS = 25 + + +def _analysis_meets_thresholds( + noise_analysis: dict[str, list[float | str | None]], + *, + noise_level: str, +) -> bool: + thresholds = SNR_THR_DICT[noise_level] + for scope in ("global", "local"): + values = noise_analysis.get(scope, []) + limit_values = thresholds.get(scope, []) + for idx, raw_value in enumerate(values): + if raw_value is None or idx >= len(limit_values): + continue + value = float(raw_value) + if np.isfinite(value) and value < float(limit_values[idx]): + return False + return True + + +def _rng(seed: int, key: str) -> np.random.Generator: + derived = (int(seed) ^ hash_string(key)) & 0xFFFFFFFF + return np.random.default_rng(derived) + + +def _values(df: pd.DataFrame, column: str) -> np.ndarray: + return pd.to_numeric(df[column], errors="coerce").to_numpy(dtype=float) + + +def _finite_scale(values: np.ndarray) -> float: + finite = values[np.isfinite(values)] + if finite.size == 0: + return 1.0 + spread = float(np.nanmax(finite) - np.nanmin(finite)) + rms = float(np.sqrt(np.mean(finite**2))) + return max(spread, rms, 1e-6) + + +def _coefficients(profile: str) -> dict[str, float]: + normalized = str(profile or "low").strip().lower() + if normalized == "low": + return LOW + if normalized == "high": + return HIGH + raise ValueError(f"Unknown noise profile '{profile}'. Expected 'low' or 'high'.") + + +def _smooth_series(values: np.ndarray) -> np.ndarray: + kernel = np.array([0.15, 0.35, 0.35, 0.15], dtype=float) + return np.convolve(values, kernel, mode="same") + + +def _drift(rng: np.random.Generator, n: int, scale: float) -> np.ndarray: + if n <= 0 or scale <= 0.0: + return np.zeros(n, dtype=float) + raw = rng.normal(0.0, scale, size=n) + return _smooth_series(_smooth_series(raw)) + + +def _event_mask(speed: np.ndarray) -> np.ndarray: + n = speed.size + if n == 0: + return np.zeros(0, dtype=bool) + out = np.zeros(n, dtype=bool) + finite = np.isfinite(speed) + for idx in range(1, n): + if not (finite[idx] and finite[idx - 1]): + continue + if abs(float(speed[idx])) < 1e-6 or abs(float(speed[idx - 1])) < 1e-6: + continue + if np.sign(speed[idx]) != np.sign(speed[idx - 1]): + lo = max(0, idx - 2) + hi = min(n, idx + 3) + out[lo:hi] = True + return out + + +def _force_event_mask(force: np.ndarray) -> np.ndarray: + n = force.size + if n == 0: + return np.zeros(0, dtype=bool) + out = np.zeros(n, dtype=bool) + finite = np.isfinite(force) + if not finite.any(): + return out + peak = float(np.nanmax(np.abs(force[finite]))) + if peak <= 0.0: + return out + impact_indices = np.flatnonzero(finite & (np.abs(force) > 0.01 * peak)) + for idx in impact_indices: + lo = max(0, int(idx) - 1) + hi = min(n, int(idx) + 2) + out[lo:hi] = True + return out + + +def _add_noise_once(df: pd.DataFrame, seed: int = 0, profile: str = "low") -> pd.DataFrame: + coeffs = _coefficients(profile) + out = df.copy() + if ( + _POSITION_COLUMN not in out.columns + and _SPEED_COLUMN not in out.columns + and _FORCE_COLUMN not in out.columns + ): + return out + + speed = _values(out, _SPEED_COLUMN) if _SPEED_COLUMN in out.columns else np.array([], dtype=float) + events = _event_mask(speed) + + if _POSITION_COLUMN in out.columns: + values = _values(out, _POSITION_COLUMN) + scale = _finite_scale(values) + rng = _rng(seed, _POSITION_COLUMN) + noisy = values.copy() + noisy += rng.normal( + 0.0, + coeffs["position_base_sigma_scale"] * scale, + size=values.size, + ) + noisy += _drift(rng, values.size, coeffs["position_drift_sigma_scale"] * scale) + if events.size == values.size: + noisy += ( + rng.normal( + 0.0, + coeffs["position_event_sigma_scale"] * scale, + size=values.size, + ) + * events.astype(float) + ) + quant_step = max( + coeffs["position_quant_step_scale"] * scale, + coeffs["position_quant_step_floor"], + ) + noisy = np.round(noisy / quant_step) * quant_step + out[_POSITION_COLUMN] = noisy + + if _SPEED_COLUMN in out.columns: + values = _values(out, _SPEED_COLUMN) + scale = _finite_scale(values) + rng = _rng(seed, _SPEED_COLUMN) + local_mag = np.abs(values) + ref = float(np.nanmedian(local_mag[np.isfinite(local_mag)])) if np.isfinite(local_mag).any() else 0.0 + noisy = values.copy() + sigma = ( + coeffs["speed_base_sigma_scale"] * scale + + coeffs["speed_hetero_sigma_scale"] * np.maximum(local_mag, ref) + ) + noisy += rng.normal(0.0, sigma, size=values.size) + noisy += _drift(rng, values.size, coeffs["speed_drift_sigma_scale"] * scale) + if events.size == values.size: + noisy += ( + rng.normal( + 0.0, + coeffs["speed_event_sigma_scale"] * scale, + size=values.size, + ) + * events.astype(float) + ) + for idx in np.flatnonzero(events): + if idx + 1 < noisy.size: + noisy[idx + 1] += rng.normal( + 0.0, + coeffs["speed_post_event_sigma_scale"] * scale, + ) + out[_SPEED_COLUMN] = noisy + + if _FORCE_COLUMN in out.columns: + values = _values(out, _FORCE_COLUMN) + scale = _finite_scale(values) + rng = _rng(seed, _FORCE_COLUMN) + force_events = _force_event_mask(values) + noisy = values.copy() + noisy += rng.normal( + 0.0, + coeffs["right_force_base_sigma_scale"] * scale, + size=values.size, + ) + if force_events.size == values.size: + noisy += ( + rng.normal( + 0.0, + coeffs["right_force_event_sigma_scale"] * scale, + size=values.size, + ) + * force_events.astype(float) + ) + out[_FORCE_COLUMN] = noisy + + return out + + +def quantify_noise( + clean: pd.DataFrame, + noisy: pd.DataFrame, + baseline: pd.DataFrame, +) -> dict[str, list[float | str | None]]: + first_diff = first_detectable_time_from_baseline(clean, baseline) + analysis = quantify_analysis( + clean, + noisy, + reference_df=baseline, + first_diff=first_diff, + local_pre_rows=DOCUMENTED_LOCAL_NOISE_ANALYSIS_PRE_ROWS, + local_post_rows=DOCUMENTED_LOCAL_NOISE_ANALYSIS_POST_ROWS, + ) + if first_diff is None or "local" not in analysis: + analysis["local"] = [None] * len(analysis.get("global", [])) + return analysis + + +def add_noise( + clean: pd.DataFrame, + baseline: pd.DataFrame, + seed: int = 0, + noise_level: str = "low", +) -> tuple[pd.DataFrame, dict[str, list[float | str | None]]]: + normalized = str(noise_level or "low").strip().lower() + if normalized not in NOISE_DICT: + raise ValueError(f"Unknown noise level '{noise_level}'. Expected 'low' or 'high'.") + current_seed = int(seed) + for _attempt in range(_MAX_NOISE_RESAMPLE_ATTEMPTS + 1): + noisy_df = _add_noise_once(clean, seed=current_seed, profile=normalized) + noise_analysis = quantify_noise(clean, noisy_df, baseline) + if _analysis_meets_thresholds(noise_analysis, noise_level=normalized): + return noisy_df, noise_analysis + current_seed += 1000 + raise RuntimeError( + f"Could not satisfy minimum SNR thresholds for noise level '{normalized}' " + f"after {_MAX_NOISE_RESAMPLE_ATTEMPTS + 1} attempts." + ) + + +__all__ = ["HIGH", "LOW", "NOISE_DICT", "SNR_THR_DICT", "add_noise", "quantify_noise"] diff --git a/questions/BounceBall/questions.json b/questions/BounceBall/questions.json new file mode 100644 index 0000000000000000000000000000000000000000..6aa9698c340b54ee5c82886aaad99140a152ef75 --- /dev/null +++ b/questions/BounceBall/questions.json @@ -0,0 +1,29274 @@ +{ + "version": 11, + "questions": { + "frost_01234-anchor_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "frost_01234-anchor_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "frost_01234-anchor_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "frost_01234-anchor_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "frost_01234-anchor_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "fern_01234-anchor_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "fern_01234-anchor_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "fern_01234-anchor_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "fern_01234-anchor_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "fern_01234-anchor_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "gentle_01234-anchor_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "gentle_01234-anchor_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "gentle_01234-anchor_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "gentle_01234-anchor_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "gentle_01234-anchor_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "frost_01234-cloud_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "frost_01234-cloud_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "frost_01234-cloud_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "frost_01234-cloud_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "frost_01234-cloud_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "fern_01234-cloud_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "fern_01234-cloud_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "fern_01234-cloud_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "fern_01234-cloud_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "fern_01234-cloud_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "gentle_01234-cloud_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "gentle_01234-cloud_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "gentle_01234-cloud_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "gentle_01234-cloud_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "gentle_01234-cloud_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "frost_01234-pine_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "frost_01234-pine_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "frost_01234-pine_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "frost_01234-pine_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "frost_01234-pine_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "fern_01234-pine_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "fern_01234-pine_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "fern_01234-pine_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "fern_01234-pine_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "fern_01234-pine_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "gentle_01234-pine_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "gentle_01234-pine_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "gentle_01234-pine_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "gentle_01234-pine_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "gentle_01234-pine_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "frost_01234-prairie_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "frost_01234-prairie_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "frost_01234-prairie_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "frost_01234-prairie_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "frost_01234-prairie_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "fern_01234-prairie_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "fern_01234-prairie_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "fern_01234-prairie_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "fern_01234-prairie_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "fern_01234-prairie_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "gentle_01234-prairie_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "gentle_01234-prairie_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "gentle_01234-prairie_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "gentle_01234-prairie_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "gentle_01234-prairie_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "frost_01234-spruce_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "frost_01234-spruce_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "frost_01234-spruce_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "frost_01234-spruce_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "frost_01234-spruce_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "fern_01234-spruce_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "fern_01234-spruce_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "fern_01234-spruce_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "fern_01234-spruce_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "fern_01234-spruce_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "gentle_01234-spruce_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "gentle_01234-spruce_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "gentle_01234-spruce_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "gentle_01234-spruce_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "gentle_01234-spruce_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "frost_01234-comet_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "frost_01234-comet_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "frost_01234-comet_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "frost_01234-comet_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "frost_01234-comet_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "fern_01234-comet_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "fern_01234-comet_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "fern_01234-comet_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "fern_01234-comet_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "fern_01234-comet_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "gentle_01234-comet_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "gentle_01234-comet_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "gentle_01234-comet_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "gentle_01234-comet_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "gentle_01234-comet_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "frost_01234-meadow_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "frost_01234-meadow_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "frost_01234-meadow_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "frost_01234-meadow_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "frost_01234-meadow_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "fern_01234-meadow_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "fern_01234-meadow_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "fern_01234-meadow_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "fern_01234-meadow_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "fern_01234-meadow_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "gentle_01234-meadow_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "gentle_01234-meadow_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "gentle_01234-meadow_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "gentle_01234-meadow_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "gentle_01234-meadow_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "frost_01234-river_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "frost_01234-river_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "frost_01234-river_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "frost_01234-river_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "frost_01234-river_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "fern_01234-river_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "fern_01234-river_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "fern_01234-river_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "fern_01234-river_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "fern_01234-river_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "gentle_01234-river_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "gentle_01234-river_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "gentle_01234-river_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "gentle_01234-river_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "gentle_01234-river_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "frost_01234-harbor_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "frost_01234-harbor_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "frost_01234-harbor_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "frost_01234-harbor_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "frost_01234-harbor_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "fern_01234-harbor_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "fern_01234-harbor_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "fern_01234-harbor_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "fern_01234-harbor_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "fern_01234-harbor_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "gentle_01234-harbor_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "gentle_01234-harbor_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "gentle_01234-harbor_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "gentle_01234-harbor_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "gentle_01234-harbor_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "frost_01234-willow_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "frost_01234-willow_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "frost_01234-willow_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "frost_01234-willow_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "frost_01234-willow_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "fern_01234-willow_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "fern_01234-willow_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "fern_01234-willow_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "fern_01234-willow_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "fern_01234-willow_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "gentle_01234-willow_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "gentle_01234-willow_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "gentle_01234-willow_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "gentle_01234-willow_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "gentle_01234-willow_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "frost_01234-flame_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "frost_01234-flame_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "frost_01234-flame_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "frost_01234-flame_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "frost_01234-flame_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "fern_01234-flame_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "fern_01234-flame_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "fern_01234-flame_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "fern_01234-flame_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "fern_01234-flame_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "gentle_01234-flame_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "gentle_01234-flame_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "gentle_01234-flame_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "gentle_01234-flame_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "gentle_01234-flame_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "frost_01234-orbit_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "frost_01234-orbit_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "frost_01234-orbit_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "frost_01234-orbit_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "frost_01234-orbit_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "fern_01234-orbit_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "fern_01234-orbit_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "fern_01234-orbit_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "fern_01234-orbit_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "fern_01234-orbit_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "gentle_01234-orbit_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "gentle_01234-orbit_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "gentle_01234-orbit_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "gentle_01234-orbit_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "gentle_01234-orbit_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "frost_01234-trail_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "frost_01234-trail_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "frost_01234-trail_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "frost_01234-trail_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "frost_01234-trail_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "fern_01234-trail_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "fern_01234-trail_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "fern_01234-trail_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "fern_01234-trail_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "fern_01234-trail_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "gentle_01234-trail_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "gentle_01234-trail_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "gentle_01234-trail_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "gentle_01234-trail_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "gentle_01234-trail_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "frost_01234-island_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "frost_01234-island_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "frost_01234-island_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "frost_01234-island_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "frost_01234-island_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "fern_01234-island_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "fern_01234-island_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "fern_01234-island_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "fern_01234-island_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "fern_01234-island_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "gentle_01234-island_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "gentle_01234-island_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "gentle_01234-island_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "gentle_01234-island_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "gentle_01234-island_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "frost_01234-glade_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "frost_01234-glade_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "frost_01234-glade_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "frost_01234-glade_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "frost_01234-glade_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "fern_01234-glade_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "fern_01234-glade_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "fern_01234-glade_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "fern_01234-glade_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "fern_01234-glade_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "gentle_01234-glade_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "gentle_01234-glade_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "gentle_01234-glade_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "gentle_01234-glade_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "gentle_01234-glade_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "frost_01234-canyon_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "frost_01234-canyon_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "frost_01234-canyon_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "frost_01234-canyon_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "frost_01234-canyon_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "fern_01234-canyon_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "fern_01234-canyon_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "fern_01234-canyon_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "fern_01234-canyon_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "fern_01234-canyon_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "gentle_01234-canyon_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "gentle_01234-canyon_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "gentle_01234-canyon_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "gentle_01234-canyon_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "gentle_01234-canyon_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "frost_01234-ember_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "frost_01234-ember_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "frost_01234-ember_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "frost_01234-ember_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "frost_01234-ember_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "fern_01234-ember_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "fern_01234-ember_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "fern_01234-ember_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "fern_01234-ember_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "fern_01234-ember_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "gentle_01234-ember_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "gentle_01234-ember_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "gentle_01234-ember_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "gentle_01234-ember_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "gentle_01234-ember_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "frost_01234-tide_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "frost_01234-tide_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "frost_01234-tide_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "frost_01234-tide_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "frost_01234-tide_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "fern_01234-tide_0": { + "question_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "train_test_sample_hash": "1de0f05d901cd2262af371976539b3b986205d71ef6add839874421f294348a4", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "fern_01234-tide_1": { + "question_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "train_test_sample_hash": "731e0ac46bb9427cd3802e15884346ae531c97cf8ee63a133c6f8c9603f7b722", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "fern_01234-tide_2": { + "question_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "train_test_sample_hash": "fdad063ad4044f77ebabb52c3c2048f845087c68c0e47f3694207b5b2d2f1a19", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "fern_01234-tide_3": { + "question_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "train_test_sample_hash": "161a98da89089f46594fd44e3a9f87138302f5d1520905d4a3b52aa9e54c01f1", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "fern_01234-tide_4": { + "question_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "train_test_sample_hash": "77649db0d754977f491027ad553aafec7d3f94252764508aad545d26f39c3926", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "gentle_01234-tide_0": { + "question_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "train_test_sample_hash": "c4182e71c46d98f15d8f3c3a1ea329d76b3e013dfc0a4aad269d58f36f80e462", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "gentle_01234-tide_1": { + "question_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "train_test_sample_hash": "be90ad7b0a702e35a642422ad8f69c8eefb5f107015263e21ab7e54d940f734c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "gentle_01234-tide_2": { + "question_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "train_test_sample_hash": "b52e60ee00536897ae764501cb148fd5fd30148339a341e72bb4f61920fee41d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "gentle_01234-tide_3": { + "question_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "train_test_sample_hash": "2a4aad740cda8db3c743d3785955861791ad44e6fb487e6459bf888dc1c9267f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "gentle_01234-tide_4": { + "question_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "train_test_sample_hash": "371820c4b19e3a37da9b26b859a20d0d643339ce9e075b872762b77453d0240d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "frost_01234-crest_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-crest_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-crest_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-crest_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-crest_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-star_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-star_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-star_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-star_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-star_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-forest_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-forest_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-forest_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-forest_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-forest_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R.\nThe rail may be tilted by a fixed inclination angle. \nWhen the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail.\nWhen the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-lagoon_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-lagoon_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-lagoon_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-lagoon_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-lagoon_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-quartz_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-quartz_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-quartz_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-quartz_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-quartz_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-dawn_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-dawn_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-dawn_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-dawn_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-dawn_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: mass position along the rail\ncol2: mass velocity along the rail\ncol3: force on the right wall\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "coefficient of restitution of the left wall", + "coefficient of restitution of the right wall", + "coefficient of viscous damping", + "inclination angle of the rail", + "weight of the mass", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"coefficient of restitution of the left wall\", \"coefficient of restitution of the right wall\", \"coefficient of viscous damping\", \"inclination angle of the rail\", \"weight of the mass\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-summit_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-summit_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-summit_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-summit_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-summit_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-aurora_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-aurora_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-aurora_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-aurora_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-aurora_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-valley_0": { + "question_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "train_test_sample_hash": "3ebf9b15a2d2c7a674f4931e93289d86e92772e40768276bcc90ada7bf7d765f", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-valley" + } + }, + "frost_01234-valley_1": { + "question_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "train_test_sample_hash": "251c9e9d6c7f1d2432a7859586530735e5b616805aebc545831ea47e6a16e733", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-valley" + } + }, + "frost_01234-valley_2": { + "question_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "train_test_sample_hash": "de527794b8e5af99d78d21c133542e454991eaeb18bf7fb8df68e06d8f1bb33e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-valley" + } + }, + "frost_01234-valley_3": { + "question_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "train_test_sample_hash": "a14fd4de2d8744107c4bc136b418bff031d3821dfe49941f41ff10b2307649ca", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-valley" + } + }, + "frost_01234-valley_4": { + "question_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "train_test_sample_hash": "2e36527196f792115cc69f11e7b680047b165f4dafc687ebcc22612a27a9ff9b", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4", + "label_5" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\"] denote different parameter changes, while \"label_5\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": 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9.979999542236328 + }, + "d10362314f4443adb722048f9dc07a4f": { + "parameters_hash": "b6b66d4f686c27edb30950b842b75662", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-02T19:31:54.472505", + "end_time_simulation": 9.979999542236328 + }, + "c405fc845cb24b21a0541d929922f563": { + "parameters_hash": "d115e8a30335c1d7ca84713b7f4c3769", + "run_type": "intervention", + "class_internal": "coulomb_friction_coefficient", + "class_agent_facing_name": "coulomb_friction_coefficient", + "status": "failed", + "timestamp": "2026-05-05T19:21:42.547033", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = sim_module._simulate_case_to_signal_dict(\n File \"/workflows/simulate_core.py\", line 1071, in _simulate_case_to_signal_dict\n res = run_simulation(\n File \"/workflows/simulate_core.py\", line 972, in run_simulation\n raise MatlabSegmentFailure(\nshared.matlab_runtime.MatlabSegmentFailure: File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n" + }, + "fbb37dd51339582da246bc6607801e3e": { + "parameters_hash": "1db2d4a32cb0d58b889c8f90b6a268e0", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "failed", + "timestamp": "2026-05-05T19:21:44.265989", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = 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"status": "failed", + "timestamp": "2026-05-05T19:21:58.073932", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = sim_module._simulate_case_to_signal_dict(\n File \"/workflows/simulate_core.py\", line 1071, in _simulate_case_to_signal_dict\n res = run_simulation(\n File \"/workflows/simulate_core.py\", line 972, in run_simulation\n raise MatlabSegmentFailure(\nshared.matlab_runtime.MatlabSegmentFailure: File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n" + }, + "4be3786039f65607908b54586a590598": { + "parameters_hash": "0f33e07783571010f778eb82a4c7bf88", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "failed", + "timestamp": "2026-05-05T19:21:59.389045", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = 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9.979999542236328 + }, + "6f9506608c874f98aa9b36f9f2fad551": { + "parameters_hash": "e1feb89c3e6c1ab34bf218743da8ae7a", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-04T16:48:01.795783", + "end_time_simulation": 9.979999542236328 + }, + "499ee4d0d6704a5488c592033341bb51": { + "parameters_hash": "65c101cabfc333d4c36902b29aa6d952", + "run_type": "intervention", + "class_internal": "coulomb_friction_coefficient", + "class_agent_facing_name": "coulomb_friction_coefficient", + "status": "failed", + "timestamp": "2026-05-05T19:22:12.836696", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = sim_module._simulate_case_to_signal_dict(\n File \"/workflows/simulate_core.py\", line 1071, in _simulate_case_to_signal_dict\n res = run_simulation(\n File \"/workflows/simulate_core.py\", line 972, in run_simulation\n raise MatlabSegmentFailure(\nshared.matlab_runtime.MatlabSegmentFailure: File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n" + }, + "a323bcead7315cc8b07b5ffaf4378447": { + "parameters_hash": "ad98823ef3744c66a51ace4e1d6e8c31", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "failed", + "timestamp": "2026-05-05T19:22:14.235869", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = 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"5cade7432107a0f3f42e3bc283ecfe25", + "run_type": "intervention", + "class_internal": "coulomb_friction_coefficient", + "class_agent_facing_name": "coulomb_friction_coefficient", + "status": "success", + "timestamp": "2026-05-05T19:22:22.453059", + "end_time_simulation": 9.98 + }, + "d8b77f327ec251dba04babcfd96c3678": { + "parameters_hash": "a0092f972f0751ec402ff316093fa6f5", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-05T19:22:24.570087", + "end_time_simulation": 9.98 + }, + "89120e47aa19439992f66c03bf231af3": { + "parameters_hash": "e30cdadbf3dea837ce7927d5a48041ae", + "run_type": "intervention", + "class_internal": "coulomb_friction_coefficient", + "class_agent_facing_name": "coulomb_friction_coefficient", + "status": "success", + "timestamp": "2026-05-04T17:00:33.539575", + "end_time_simulation": 9.979999542236328 + }, + "8d59e9561cb95164a587bf58bb4b8fd4": { + "parameters_hash": "8a83213ad00c73056ea766ba6d59bd87", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-04T17:00:35.873501", + "end_time_simulation": 9.979999542236328 + }, + "59d90dcd97cb4b84ba9d4bc95bdf2985": { + "parameters_hash": "e62db7cbe306be7fbc470a4d2462fc96", + "run_type": "intervention", + "class_internal": "breakaway_friction_coefficient", + "class_agent_facing_name": "breakaway_friction_coefficient", + "status": "success", + "timestamp": "2026-05-04T17:00:38.332586", + "end_time_simulation": 9.979999542236328 + }, + "24dc598341aa4674afba02f25e262ce5": { + "parameters_hash": "7656301d2e0b15ab42441a38c0c482b3", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-04T17:00:40.625055", + "end_time_simulation": 9.979999542236328 + }, + "d2db848c867a4ffd9b6bc42d6df217d7": { + "parameters_hash": "33e9e6ab68ae56eae253c4f9412111cc", + "run_type": "intervention", + "class_internal": "breakaway_friction_coefficient", + "class_agent_facing_name": "breakaway_friction_coefficient", + "status": "failed", + "timestamp": "2026-05-05T19:22:37.104868", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = sim_module._simulate_case_to_signal_dict(\n File \"/workflows/simulate_core.py\", line 1071, in _simulate_case_to_signal_dict\n res = run_simulation(\n File \"/workflows/simulate_core.py\", line 972, in run_simulation\n raise MatlabSegmentFailure(\nshared.matlab_runtime.MatlabSegmentFailure: File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n" + }, + "a6dd49d400ce4a328d65184a0375930f": { + "parameters_hash": "831d973370f81a96f4218ab16a11c14e", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "failed", + "timestamp": "2026-05-05T19:22:38.185820", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = sim_module._simulate_case_to_signal_dict(\n File \"/workflows/simulate_core.py\", line 1071, in _simulate_case_to_signal_dict\n res = run_simulation(\n File \"/workflows/simulate_core.py\", line 972, in run_simulation\n raise MatlabSegmentFailure(\nshared.matlab_runtime.MatlabSegmentFailure: File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\n" + }, + "aec60d90f7ef4979a80c56f5c5bdeafa": { + "parameters_hash": "7dfdef65098f35fb6f5804a59777d9cf", + "run_type": "baseline", + "class_internal": "no_parameter_change", + "class_agent_facing_name": "no parameter changed", + "status": "success", + "timestamp": "2026-05-02T16:49:08.654932", + "end_time_simulation": 9.979999542236328 + }, + "1e7a9a8f686f49d38a1791a7d96016de": { + "parameters_hash": "c4860a63006f2e64518b5f10795c9ed2", + "run_type": "intervention", + "class_internal": "gravity_constant", + "class_agent_facing_name": "gravity_constant", + 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"b2dcb7ad324ae16e3753541bcfca5098", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-02T16:50:18.538187", + "end_time_simulation": 9.979999542236328 + }, + "cfcf113e827240a6b7e7a4be86078864": { + "parameters_hash": "c96b86d4ccfaacdef36a59b38587aae5", + "run_type": "intervention", + "class_internal": "plane_inclination", + "class_agent_facing_name": "plane_inclination", + "status": "success", + "timestamp": "2026-05-02T16:50:21.417780", + "end_time_simulation": 9.979999542236328 + }, + "be7a5990092d58d19aae77403ef84f4e": { + "parameters_hash": "c9b20542fa4b3c88b7b821b389f31ab6", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-02T16:50:23.820016", + "end_time_simulation": 9.979999542236328 + }, + "95357a192b404d9d942b699a96f3f91d": { + "parameters_hash": "9f0e10f22186a49d6e156a0074913a6c", + "run_type": "intervention", + "class_internal": "plane_inclination", + "class_agent_facing_name": "plane_inclination", + "status": "success", + "timestamp": "2026-05-02T16:50:27.017440", + "end_time_simulation": 9.979999542236328 + }, + "40cae27f4ade43fb8ea906cfb1cc6421": { + "parameters_hash": "6505a27f02b170977c89fda72c5241c3", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-02T16:50:29.627827", + "end_time_simulation": 9.979999542236328 + }, + "01ca63e4fb7d4648a1ced30b6a4ecfba": { + "parameters_hash": "afe46de9d1743013a555124c7bd443e8", + "run_type": "intervention", + "class_internal": "mass", + "class_agent_facing_name": "mass", + "status": "success", + "timestamp": "2026-05-02T16:50:32.179533", + "end_time_simulation": 9.979999542236328 + }, + "2485cdff05454c7483aa4a992a6ed1f6": { + "parameters_hash": "a78a832e092eec3455caabb8d16cbb76", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-02T16:50:35.246584", + "end_time_simulation": 9.979999542236328 + }, + "0faf91cbf206423294c8646f226f9b9e": { + "parameters_hash": "adfd8ba9186266b08be14a022b76a10c", + "run_type": "intervention", + "class_internal": "coulomb_friction_coefficient", + "class_agent_facing_name": "coulomb_friction_coefficient", + "status": "failed", + "timestamp": "2026-05-05T19:22:50.301912", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = sim_module._simulate_case_to_signal_dict(\n File \"/workflows/simulate_core.py\", line 1071, in _simulate_case_to_signal_dict\n res = run_simulation(\n File \"/workflows/simulate_core.py\", line 972, in run_simulation\n raise MatlabSegmentFailure(\nshared.matlab_runtime.MatlabSegmentFailure: File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n" + }, + "25c12ad712b159f9a0625e7ae851b8a4": { + "parameters_hash": "7ec9dca1c05f334cc7a72aec0a20b705", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "failed", + "timestamp": "2026-05-05T19:22:51.514628", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = 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"time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-02T16:53:14.877627", + "end_time_simulation": 9.979999542236328 + }, + "8bc590e7d4274e4db6c5ee5ef76a48d0": { + "parameters_hash": "3f979decd65737a4f9f13b0cd8a9ba9a", + "run_type": "intervention", + "class_internal": "coulomb_friction_coefficient", + "class_agent_facing_name": "coulomb_friction_coefficient", + "status": "failed", + "timestamp": "2026-05-05T19:23:03.870560", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = sim_module._simulate_case_to_signal_dict(\n File \"/workflows/simulate_core.py\", line 1071, in _simulate_case_to_signal_dict\n res = run_simulation(\n File \"/workflows/simulate_core.py\", line 972, in run_simulation\n raise MatlabSegmentFailure(\nshared.matlab_runtime.MatlabSegmentFailure: File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n" + }, + "92ed561f7a5653ea8567b7ea9093f23f": { + "parameters_hash": "e4cd499bcf6e8b0ab688b1da6f9e0b98", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "failed", + "timestamp": "2026-05-05T19:23:05.007234", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = 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Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: When trying to advance one time step, nonlinear solver failed to converge, residual norm too large.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Here is the set of components with unconverged equations:\n \n 'simulink_model/Mass With Friction (PB)'\n Equation location is:\n 'foundation.translational.elements.mass' (line 40)\n \n 'simulink_model/Mass With Friction (PB)'\n Equation location is:\n 'foundation.translational.elements.friction' (line 93)\n \n 'simulink_model/Mass With Friction (PB)'\n Equation location is:\n 'foundation.translational.translational' (line 19)\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 1 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: When trying to advance one time step, nonlinear solver failed to converge, residual norm too large.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Here is the set of components with unconverged equations:\n \n 'simulink_model/Mass With Friction (PB)'\n Equation location is:\n 'foundation.translational.elements.mass' (line 40)\n \n 'simulink_model/Mass With Friction (PB)'\n Equation location is:\n 'foundation.translational.elements.friction' (line 93)\n \n 'simulink_model/Mass With Friction (PB)'\n Equation location is:\n 'foundation.translational.translational' (line 19)\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = sim_module._simulate_case_to_signal_dict(\n File \"/workflows/simulate_core.py\", line 1071, in _simulate_case_to_signal_dict\n res = run_simulation(\n File \"/workflows/simulate_core.py\", line 972, in run_simulation\n raise MatlabSegmentFailure(\nshared.matlab_runtime.MatlabSegmentFailure: File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 1 failed. 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"timestamp": "2026-05-04T17:34:32.979898", + "end_time_simulation": 9.979999542236328 + }, + "af0d841e3c595d249fe38c90dfae9434": { + "parameters_hash": "8ee249115f114acd2a5d9b8213312116", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "success", + "timestamp": "2026-05-04T17:34:35.622918", + "end_time_simulation": 9.979999542236328 + }, + "7dab5d3431405b48ba484ebb00e67a1c": { + "parameters_hash": "36b52cae240579914cdda0ff0024d46d", + "run_type": "intervention", + "class_internal": "coulomb_friction_coefficient", + "class_agent_facing_name": "coulomb_friction_coefficient", + "status": "failed", + "timestamp": "2026-05-05T19:23:29.252988", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = sim_module._simulate_case_to_signal_dict(\n File \"/workflows/simulate_core.py\", line 1071, in _simulate_case_to_signal_dict\n res = run_simulation(\n File \"/workflows/simulate_core.py\", line 972, in run_simulation\n raise MatlabSegmentFailure(\nshared.matlab_runtime.MatlabSegmentFailure: File /models/simulink/InclinedPlane/sim_the_model.m, line 186, in sim_the_model\nSegment: 2 failed. Error using sim_the_model (line 183)\n['simulink_model/Solver Configuration']: At parameter initialization, one or more assertions are triggered. See causes for specific information.\nCaused by:\n Error using Simulink.Simulation.internal.DesktopSimHelper\n Coulomb friction coefficient must be less than or equal to Breakaway friction coefficient. The assertion comes from:\n Block path: simulink_model/Mass With Friction (PB)\n Assert location:\n o In between line: 62, column: 5 and line: 62, column: 11 in file: foundation.translational.elements.friction\n o In between line: 76, column: 15 and line: 83, column: 42 in file: foundation.translational.elements.mass_with_friction\n \n \n Error in Simulink.Simulation.internal.DesktopSimHelper.sim\n \n Error in Simulink.SimulationInput/sim\n \n Error in sim_the_model (line 183)\n tmp = evalc('so = sim(si);'); %#ok\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n" + }, + "a3d4298160c95986b47107a7994ddc55": { + "parameters_hash": "bb611ccdbffa21c079da16d92aff9d9e", + "run_type": "time0_baseline", + "class_internal": "", + "class_agent_facing_name": "", + "status": "failed", + "timestamp": "2026-05-05T19:23:30.720123", + "error": "File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\nTraceback (most recent call last):\n File \"/workflows/simulate_core.py\", line 912, in run_simulation\n result = future.result(timeout=_SIMULATION_TIMEOUT_SECONDS)\n File \"/env/lib/python3.10/site-packages/matlab/engine/futureresult.py\", line 62, in result\n return self.__future.result(timeout)\n File \"/env/lib/python3.10/site-packages/matlab/engine/fevalfuture.py\", line 76, in result\n self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)\nmatlab.engine.MatlabExecutionError: \n File /models/simulink/InclinedPlane/sim_the_model.m, line 129, in sim_the_model\nError compiling Simscape network for model simulink_model.\n\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/workflows/simulate/run_pending_sims.py\", line 1189, in _run_model_dir\n simulation_result = simulation_api.simulate_recipe(\n File \"/shared/simulation.py\", line 849, in simulate_recipe\n all_signal_dict = 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shared.noise_analysis import ( + DOCUMENTED_LOCAL_NOISE_ANALYSIS_POST_ROWS, + DOCUMENTED_LOCAL_NOISE_ANALYSIS_PRE_ROWS, + first_detectable_time_from_baseline, + quantify_analysis, +) +from shared.noise_snr import hash_string + + +_VELOCITY_COLUMN = "mass_velocity" +_FRICTION_COLUMN = "friction_force" +_NORMAL_COLUMN = "normal_force" +LOW = { + "velocity_base_sigma_scale": 0.003, + "velocity_hetero_sigma_scale": 0.003, + "velocity_drift_sigma_scale": 0.0005, + "velocity_event_sigma_scale": 0.01, + "friction_base_sigma_scale": 0.001, + "friction_drift_sigma_scale": 0.001, + "friction_bias_sigma_scale": 0.002, + "friction_event_sigma_scale": 0.02, + "normal_base_sigma_scale": 0.001, + "normal_drift_sigma_scale": 0.0002, +} +HIGH_SCALE = 4.0 +HIGH = {key: float(value) * HIGH_SCALE for key, value in LOW.items()} + + +NOISE_DICT = {"low": LOW, "high": HIGH} +SNR_THR_DICT = { + "low": {"global": [-1.0e9, -1.0e9, -1.0e9], "local": [-1.0e9, -1.0e9, -1.0e9]}, + "high": {"global": [-1.0e9, -1.0e9, -1.0e9], "local": [-1.0e9, -1.0e9, -1.0e9]}, +} +_MAX_NOISE_RESAMPLE_ATTEMPTS = 25 + + +def _analysis_meets_thresholds( + noise_analysis: dict[str, list[float | str | None]], + *, + noise_level: str, +) -> bool: + thresholds = SNR_THR_DICT[noise_level] + for scope in ("global", "local"): + values = noise_analysis.get(scope, []) + limit_values = thresholds.get(scope, []) + for idx, raw_value in enumerate(values): + if raw_value is None or idx >= len(limit_values): + continue + value = float(raw_value) + if np.isfinite(value) and value < float(limit_values[idx]): + return False + return True + + +def _rng(seed: int, key: str) -> np.random.Generator: + derived = (int(seed) ^ hash_string(key)) & 0xFFFFFFFF + return np.random.default_rng(derived) + + +def _values(df: pd.DataFrame, column: str) -> np.ndarray: + return pd.to_numeric(df[column], errors="coerce").to_numpy(dtype=float) + + +def _finite_scale(values: np.ndarray) -> float: + finite = values[np.isfinite(values)] + if finite.size == 0: + return 1.0 + spread = float(np.nanmax(finite) - np.nanmin(finite)) + rms = float(np.sqrt(np.mean(finite**2))) + return max(spread, rms, 1e-6) + + +def _coefficients(profile: str) -> dict[str, float]: + normalized = str(profile or "low").strip().lower() + if normalized == "low": + return LOW + if normalized == "high": + return HIGH + raise ValueError(f"Unknown noise profile '{profile}'. Expected 'low' or 'high'.") + + +def _smooth(values: np.ndarray) -> np.ndarray: + kernel = np.array([0.2, 0.3, 0.3, 0.2], dtype=float) + return np.convolve(values, kernel, mode="same") + + +def _drift(rng: np.random.Generator, n: int, scale: float) -> np.ndarray: + if n <= 0 or scale <= 0.0: + return np.zeros(n, dtype=float) + return _smooth(_smooth(rng.normal(0.0, scale, size=n))) + + +def _transition_mask(velocity: np.ndarray, friction: np.ndarray) -> np.ndarray: + n = max(velocity.size, friction.size) + out = np.zeros(n, dtype=bool) + for source in (velocity, friction): + if source.size <= 1: + continue + finite = np.isfinite(source) + scale = _finite_scale(source) + for idx in range(1, source.size): + if not (finite[idx] and finite[idx - 1]): + continue + sign_flip = np.sign(source[idx]) != np.sign(source[idx - 1]) + jump = abs(float(source[idx] - source[idx - 1])) > 0.08 * scale + near_zero = abs(float(source[idx])) < 0.02 * scale + if sign_flip or (jump and near_zero): + lo = max(0, idx - 2) + hi = min(out.size, idx + 3) + out[lo:hi] = True + return out + + +def _add_noise_once(df: pd.DataFrame, seed: int = 0, profile: str = "low") -> pd.DataFrame: + coeffs = _coefficients(profile) + out = df.copy() + velocity = _values(out, _VELOCITY_COLUMN) if _VELOCITY_COLUMN in out.columns else np.array([], dtype=float) + friction = _values(out, _FRICTION_COLUMN) if _FRICTION_COLUMN in out.columns else np.array([], dtype=float) + transitions = _transition_mask(velocity, friction) + + if _VELOCITY_COLUMN in out.columns: + values = velocity + scale = _finite_scale(values) + rng = _rng(seed, _VELOCITY_COLUMN) + local = np.abs(values) + ref = float(np.nanmedian(local[np.isfinite(local)])) if np.isfinite(local).any() else 0.0 + noisy = values.copy() + noisy += rng.normal( + 0.0, + coeffs["velocity_base_sigma_scale"] * scale + + coeffs["velocity_hetero_sigma_scale"] * np.maximum(local, ref), + size=values.size, + ) + noisy += _drift(rng, values.size, coeffs["velocity_drift_sigma_scale"] * scale) + if transitions.size == values.size: + noisy += ( + rng.normal( + 0.0, + coeffs["velocity_event_sigma_scale"] * scale, + size=values.size, + ) + * transitions.astype(float) + ) + out[_VELOCITY_COLUMN] = noisy + + if _FRICTION_COLUMN in out.columns: + values = friction + scale = _finite_scale(values) + rng = _rng(seed, _FRICTION_COLUMN) + noisy = values.copy() + noisy += rng.normal( + 0.0, + coeffs["friction_base_sigma_scale"] * scale, + size=values.size, + ) + noisy += _drift(rng, values.size, coeffs["friction_drift_sigma_scale"] * scale) + noisy += np.sign(np.nan_to_num(values, nan=0.0)) * rng.normal( + 0.0, + coeffs["friction_bias_sigma_scale"] * scale, + ) + if transitions.size == values.size: + noisy += ( + rng.normal( + 0.0, + coeffs["friction_event_sigma_scale"] * scale, + size=values.size, + ) + * transitions.astype(float) + ) + out[_FRICTION_COLUMN] = noisy + + if _NORMAL_COLUMN in out.columns: + values = _values(out, _NORMAL_COLUMN) + scale = _finite_scale(values) + rng = _rng(seed, _NORMAL_COLUMN) + noisy = values.copy() + noisy += rng.normal( + 0.0, + coeffs["normal_base_sigma_scale"] * scale, + size=values.size, + ) + noisy += _drift(rng, values.size, coeffs["normal_drift_sigma_scale"] * scale) + out[_NORMAL_COLUMN] = np.maximum(noisy, 0.0) + + return out + + +def quantify_noise( + clean: pd.DataFrame, + noisy: pd.DataFrame, + baseline: pd.DataFrame | None, +) -> dict[str, list[float | str | None]]: + first_diff = first_detectable_time_from_baseline(clean, baseline) + analysis = quantify_analysis( + clean, + noisy, + reference_df=baseline, + first_diff=first_diff, + local_pre_rows=DOCUMENTED_LOCAL_NOISE_ANALYSIS_PRE_ROWS, + local_post_rows=DOCUMENTED_LOCAL_NOISE_ANALYSIS_POST_ROWS, + ) + if first_diff is None or "local" not in analysis: + analysis["local"] = [None] * len(analysis.get("global", [])) + return analysis + + +def add_noise( + clean: pd.DataFrame, + baseline: pd.DataFrame | None, + seed: int = 0, + noise_level: str = "low", +) -> tuple[pd.DataFrame, dict[str, list[float | str | None]]]: + normalized = str(noise_level or "low").strip().lower() + if normalized not in NOISE_DICT: + raise ValueError(f"Unknown noise level '{noise_level}'. Expected 'low' or 'high'.") + current_seed = int(seed) + for _attempt in range(_MAX_NOISE_RESAMPLE_ATTEMPTS + 1): + noisy_df = _add_noise_once(clean, seed=current_seed, profile=normalized) + noise_analysis = quantify_noise(clean, noisy_df, baseline) + if _analysis_meets_thresholds(noise_analysis, noise_level=normalized): + return noisy_df, noise_analysis + current_seed += 1000 + raise RuntimeError( + f"Could not satisfy minimum SNR thresholds for noise level '{normalized}' " + f"after {_MAX_NOISE_RESAMPLE_ATTEMPTS + 1} attempts." + ) + + +__all__ = ["HIGH", "LOW", "NOISE_DICT", "SNR_THR_DICT", "add_noise", "quantify_noise"] diff --git a/questions/MassSlide/questions.json b/questions/MassSlide/questions.json new file mode 100644 index 0000000000000000000000000000000000000000..6cf6d043cebe0dcb534f26955d4db57fa8b9f33a --- /dev/null +++ b/questions/MassSlide/questions.json @@ -0,0 +1,29023 @@ +{ + "version": 8, + "questions": { + "frost_01234-anchor_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "frost_01234-anchor_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "frost_01234-anchor_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "frost_01234-anchor_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "frost_01234-anchor_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-anchor" + } + }, + "fern_01234-anchor_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "fern_01234-anchor_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "fern_01234-anchor_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "fern_01234-anchor_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "fern_01234-anchor_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-anchor" + } + }, + "gentle_01234-anchor_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "gentle_01234-anchor_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "gentle_01234-anchor_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "gentle_01234-anchor_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "gentle_01234-anchor_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-anchor" + } + }, + "frost_01234-cloud_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "frost_01234-cloud_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "frost_01234-cloud_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "frost_01234-cloud_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "frost_01234-cloud_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-cloud" + } + }, + "fern_01234-cloud_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "fern_01234-cloud_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "fern_01234-cloud_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "fern_01234-cloud_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "fern_01234-cloud_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-cloud" + } + }, + "gentle_01234-cloud_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "gentle_01234-cloud_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "gentle_01234-cloud_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "gentle_01234-cloud_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "gentle_01234-cloud_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-cloud" + } + }, + "frost_01234-pine_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "frost_01234-pine_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "frost_01234-pine_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "frost_01234-pine_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "frost_01234-pine_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-pine" + } + }, + "fern_01234-pine_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "fern_01234-pine_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "fern_01234-pine_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "fern_01234-pine_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "fern_01234-pine_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-pine" + } + }, + "gentle_01234-pine_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "gentle_01234-pine_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "gentle_01234-pine_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "gentle_01234-pine_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "gentle_01234-pine_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-pine" + } + }, + "frost_01234-prairie_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "frost_01234-prairie_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "frost_01234-prairie_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "frost_01234-prairie_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "frost_01234-prairie_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-prairie" + } + }, + "fern_01234-prairie_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "fern_01234-prairie_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "fern_01234-prairie_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "fern_01234-prairie_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "fern_01234-prairie_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-prairie" + } + }, + "gentle_01234-prairie_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "gentle_01234-prairie_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "gentle_01234-prairie_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "gentle_01234-prairie_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "gentle_01234-prairie_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-prairie" + } + }, + "frost_01234-spruce_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "frost_01234-spruce_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "frost_01234-spruce_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "frost_01234-spruce_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "frost_01234-spruce_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-spruce" + } + }, + "fern_01234-spruce_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "fern_01234-spruce_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "fern_01234-spruce_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "fern_01234-spruce_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "fern_01234-spruce_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-spruce" + } + }, + "gentle_01234-spruce_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "gentle_01234-spruce_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "gentle_01234-spruce_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "gentle_01234-spruce_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "gentle_01234-spruce_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-spruce" + } + }, + "frost_01234-comet_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "frost_01234-comet_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "frost_01234-comet_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "frost_01234-comet_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "frost_01234-comet_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "The time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-comet" + } + }, + "fern_01234-comet_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "fern_01234-comet_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "fern_01234-comet_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "fern_01234-comet_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "fern_01234-comet_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-comet" + } + }, + "gentle_01234-comet_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "gentle_01234-comet_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "gentle_01234-comet_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "gentle_01234-comet_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "gentle_01234-comet_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "The time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-comet" + } + }, + "frost_01234-meadow_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "frost_01234-meadow_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "frost_01234-meadow_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "frost_01234-meadow_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "frost_01234-meadow_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-meadow" + } + }, + "fern_01234-meadow_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "fern_01234-meadow_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "fern_01234-meadow_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "fern_01234-meadow_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "fern_01234-meadow_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-meadow" + } + }, + "gentle_01234-meadow_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "gentle_01234-meadow_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "gentle_01234-meadow_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "gentle_01234-meadow_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "gentle_01234-meadow_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-meadow" + } + }, + "frost_01234-river_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "frost_01234-river_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "frost_01234-river_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "frost_01234-river_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "frost_01234-river_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-river" + } + }, + "fern_01234-river_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "fern_01234-river_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "fern_01234-river_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "fern_01234-river_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "fern_01234-river_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-river" + } + }, + "gentle_01234-river_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "gentle_01234-river_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "gentle_01234-river_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "gentle_01234-river_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "gentle_01234-river_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-river" + } + }, + "frost_01234-harbor_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "frost_01234-harbor_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "frost_01234-harbor_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "frost_01234-harbor_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "frost_01234-harbor_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-harbor" + } + }, + "fern_01234-harbor_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "fern_01234-harbor_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "fern_01234-harbor_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "fern_01234-harbor_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "fern_01234-harbor_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-harbor" + } + }, + "gentle_01234-harbor_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "gentle_01234-harbor_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "gentle_01234-harbor_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "gentle_01234-harbor_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "gentle_01234-harbor_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled `train_samples/` directory. The corresponding labels are available in `train_labels.json` file.", + "task_artifact": "Task:\nCreate a file named results.json in the current working directory.", + "prediction_format": "For each file in test_samples/, return a ranked list of labels.\nThe first label is your final top-1 prediction and should be the single label you think is most likely correct.\nYou may include additional labels only when the evidence is genuinely ambiguous.\nAdditional labels are treated as lower-confidence alternatives.\nThe output must be valid JSON with exactly this structure:\n{\n\n \"\": [\"\"],\n \"\": [\"\", \"\"]\n\n}", + "mode_specific_requirements": "Requirements:\n- Include one entry for every Parquet file in test_samples/.\n- Every returned label must exactly match one of the allowed labels.\n- The order of labels matters: the first label is the top-1 prediction.\n- Do not include duplicate labels for a sample.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label in each returned list is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "direct", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-harbor" + } + }, + "frost_01234-willow_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "frost_01234-willow_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "frost_01234-willow_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "frost_01234-willow_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "frost_01234-willow_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-willow" + } + }, + "fern_01234-willow_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "fern_01234-willow_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "fern_01234-willow_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "fern_01234-willow_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "fern_01234-willow_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-willow" + } + }, + "gentle_01234-willow_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "gentle_01234-willow_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "gentle_01234-willow_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "gentle_01234-willow_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "gentle_01234-willow_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-willow" + } + }, + "frost_01234-flame_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "frost_01234-flame_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "frost_01234-flame_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "frost_01234-flame_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "frost_01234-flame_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-flame" + } + }, + "fern_01234-flame_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "fern_01234-flame_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "fern_01234-flame_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "fern_01234-flame_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "fern_01234-flame_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-flame" + } + }, + "gentle_01234-flame_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "gentle_01234-flame_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "gentle_01234-flame_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "gentle_01234-flame_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "gentle_01234-flame_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-flame" + } + }, + "frost_01234-orbit_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "frost_01234-orbit_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "frost_01234-orbit_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "frost_01234-orbit_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "frost_01234-orbit_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-orbit" + } + }, + "fern_01234-orbit_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "fern_01234-orbit_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "fern_01234-orbit_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "fern_01234-orbit_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "fern_01234-orbit_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-orbit" + } + }, + "gentle_01234-orbit_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "gentle_01234-orbit_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "gentle_01234-orbit_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "gentle_01234-orbit_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "gentle_01234-orbit_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-orbit" + } + }, + "frost_01234-trail_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "frost_01234-trail_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "frost_01234-trail_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "frost_01234-trail_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "frost_01234-trail_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-trail" + } + }, + "fern_01234-trail_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "fern_01234-trail_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "fern_01234-trail_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "fern_01234-trail_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "fern_01234-trail_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-trail" + } + }, + "gentle_01234-trail_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "gentle_01234-trail_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "gentle_01234-trail_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "gentle_01234-trail_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "gentle_01234-trail_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-trail" + } + }, + "frost_01234-island_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "frost_01234-island_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "frost_01234-island_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "frost_01234-island_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "frost_01234-island_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-island" + } + }, + "fern_01234-island_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "fern_01234-island_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "fern_01234-island_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "fern_01234-island_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "fern_01234-island_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-island" + } + }, + "gentle_01234-island_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "gentle_01234-island_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "gentle_01234-island_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "gentle_01234-island_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "gentle_01234-island_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-island" + } + }, + "frost_01234-glade_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "frost_01234-glade_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "frost_01234-glade_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "frost_01234-glade_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "frost_01234-glade_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-glade" + } + }, + "fern_01234-glade_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "fern_01234-glade_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "fern_01234-glade_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "fern_01234-glade_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "fern_01234-glade_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-glade" + } + }, + "gentle_01234-glade_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "gentle_01234-glade_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "gentle_01234-glade_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "gentle_01234-glade_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "gentle_01234-glade_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-glade" + } + }, + "frost_01234-canyon_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "frost_01234-canyon_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "frost_01234-canyon_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "frost_01234-canyon_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "frost_01234-canyon_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-canyon" + } + }, + "fern_01234-canyon_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "fern_01234-canyon_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "fern_01234-canyon_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "fern_01234-canyon_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "fern_01234-canyon_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-canyon" + } + }, + "gentle_01234-canyon_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "gentle_01234-canyon_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "gentle_01234-canyon_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "gentle_01234-canyon_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "gentle_01234-canyon_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-canyon" + } + }, + "frost_01234-ember_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "frost_01234-ember_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "frost_01234-ember_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "frost_01234-ember_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "frost_01234-ember_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-ember" + } + }, + "fern_01234-ember_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "fern_01234-ember_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "fern_01234-ember_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "fern_01234-ember_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "fern_01234-ember_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-ember" + } + }, + "gentle_01234-ember_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "gentle_01234-ember_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "gentle_01234-ember_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "gentle_01234-ember_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "gentle_01234-ember_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-ember" + } + }, + "frost_01234-tide_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "frost_01234-tide_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "frost_01234-tide_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "frost_01234-tide_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "frost_01234-tide_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-tide" + } + }, + "fern_01234-tide_0": { + "question_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "train_test_sample_hash": "11f7f8622d2a2d9d6464cf16959927fcaecd184d5e7b05fd7d645d5781f19d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "fern_01234-tide_1": { + "question_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "train_test_sample_hash": "0e177bc9873803361541361838e7944f83cc46fb206b3c526848a747074237fc", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "fern_01234-tide_2": { + "question_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "train_test_sample_hash": "fe08147ad280fcddb2271c2f949188cbcf2cf55805f9c0ad110f3b014d004767", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "fern_01234-tide_3": { + "question_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "train_test_sample_hash": "7a097682902340a904b33e388b7474b9e26ddf3f721c64c1d908e29d4104522c", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "fern_01234-tide_4": { + "question_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "train_test_sample_hash": "5eed0b0ab2b3e208cd44b2683b95802b7b1d25e67bedc993fd69e43efe777bc3", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 1, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "fern_01234", + "row_slug": "fern_01234-tide" + } + }, + "gentle_01234-tide_0": { + "question_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "train_test_sample_hash": "983a576ebeccac02ba07a78e54f26e7c4e2e22bbd9826b0e9bc930fdd875eac0", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "gentle_01234-tide_1": { + "question_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "train_test_sample_hash": "38d835e7db029355c5d68e32511a44f20b54c931336a0c1ebd0b4c0b2af62262", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "gentle_01234-tide_2": { + "question_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "train_test_sample_hash": "8d171b58be5ee86560e8c18f33af7894b0c14a92311bc37f6e9e1cba4a5fcd6a", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "gentle_01234-tide_3": { + "question_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "train_test_sample_hash": "4763b98b1f6f4b13d2b2952569a6924dd59dd917250eaabbfed7c7287750783d", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "gentle_01234-tide_4": { + "question_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "train_test_sample_hash": "1b25cc44bc43c8c5810d0bc5678187baef568a8ab3697fc11159a5ae9dc1af21", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ and train_samples/ folders were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "To help with this task, you can use the labeled train_samples/ directory while developing rule.py. The corresponding labels are available in train_labels.json.", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect train_samples/ and train_labels.json while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including train_samples/, test_samples/ or train_labels.json\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "code", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 3, + "number_test_samples": 10, + "is_adversarial": true, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "gentle_01234", + "row_slug": "gentle_01234-tide" + } + }, + "frost_01234-crest_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-crest_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-crest_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-crest_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-crest_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-crest" + } + }, + "frost_01234-star_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-star_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-star_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-star_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-star_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-star" + } + }, + "frost_01234-forest_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-forest_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-forest_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-forest_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-forest_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction.\nThe coordinate axis is aligned with the plane. \nThe block is also subject to an externally applied periodic force along the plane.\nThe friction force acts along the plane and opposes motion.\nWhen the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction.\nA breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "high", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-forest" + } + }, + "frost_01234-lagoon_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-lagoon_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-lagoon_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-lagoon_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-lagoon_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-lagoon" + } + }, + "frost_01234-quartz_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-quartz_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-quartz_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-quartz_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-quartz_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-quartz" + } + }, + "frost_01234-dawn_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-dawn_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-dawn_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-dawn_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-dawn_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated", + "environment_description": "by simulating a physical process.", + "observed_columns": "Observed Signals:\ncol1: velocity of the mass along the plane\ncol2: friction force\ncol3: normal force\ncol4: time", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter among the allowed labels changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "Coulomb friction coefficient", + "breakaway friction coefficient", + "gravity acceleration", + "plane inclination angle", + "no parameter change" + ], + "label_space": "Allowed labels:\n[\"Coulomb friction coefficient\", \"breakaway friction coefficient\", \"gravity acceleration\", \"plane inclination angle\", \"no parameter change\"]", + "no_change_guidance": "Use \"no parameter change\" if there is no evidence in the data of a parameter change during the observed interval.", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + " " + ], + [ + "environment_description", + "\n\n" + ], + [ + "observed_columns", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "no_change_guidance", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "low", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-dawn" + } + }, + "frost_01234-summit_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-summit_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-summit_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-summit_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-summit_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "none", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-summit" + } + }, + "frost_01234-aurora_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-aurora_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-aurora_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-aurora_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-aurora_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "low", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-aurora" + } + }, + "frost_01234-valley_0": { + "question_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "train_test_sample_hash": "6799c692b3dceef16296cfb8a7178d8d98cc7e63e91f9140c5fe1112397da8ed", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 0, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-valley" + } + }, + "frost_01234-valley_1": { + "question_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "train_test_sample_hash": "b4c5c674cb9dfb31a2dc34906c68c1aa66093a5a4985ecfc79000ddc391be981", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 1, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-valley" + } + }, + "frost_01234-valley_2": { + "question_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "train_test_sample_hash": "1795fff5c2b9368a7cb10f7502ee104b07ad2d607f06ece68da8613662af5aa6", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 2, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-valley" + } + }, + "frost_01234-valley_3": { + "question_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "train_test_sample_hash": "2c8709176b4f21a09212fc4778e6c95711fbef8ac49b595aa42b472938f02d36", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 3, + "test_set_slug": "home", + "shot_slug": "frost_01234", + "row_slug": "frost_01234-valley" + } + }, + "frost_01234-valley_4": { + "question_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "train_test_sample_hash": "22182ae00c573129e762df0c2c49c7379c5d9988f9ad56efde880e96238d6b0e", + "question_text": { + "sample_source": "Context:\nThe time series in the test_samples/ folder were generated by a simulator of an unknown physical phenomenon.", + "environment_description": "The column meanings are unknown, except for the last column, which represents time.", + "observed_columns": "", + "intervention_semantics": "For each simulation, either no parameter changes occur, or exactly one parameter corresponding to one of the allowed changes during the observed simulation interval.\nIf a parameter changes, it undergoes a single instantaneous step change at an unknown time during the observed interval.", + "allowed_labels": [ + "label_0", + "label_1", + "label_2", + "label_3", + "label_4" + ], + "label_space": "[\"label_0\", \"label_1\", \"label_2\", \"label_3\"] denote different parameter changes, while \"label_4\" denotes that no parameter changed.", + "no_change_guidance": "", + "fewshot_context": "", + "task_artifact": "Task:\nCreate a Python script named rule.py in the current working directory.\nThe script must define exactly this function:\ndef predict(df) -> list[str]:", + "prediction_format": "The input df is a pandas DataFrame containing one sample with columns col1, col2, col3, and col4.\nThe function will be called on samples inside test_samples/ and on additional held-out samples with the same schema and label set.\nFor each dataframe, predict(df) must return a ranked list of labels. The first label is the final top-1 prediction and should be the single label most likely to be correct. Additional labels are optional lower-confidence alternatives and should only be included when the evidence is genuinely ambiguous.", + "mode_specific_requirements": "Requirements for predict(df):\n- Return a Python list of strings.\n- Every returned label must exactly match one of the allowed labels.\n- The first returned label is the top-1 prediction.\n- Do not include duplicate labels.\n- Do not return all labels unless the evidence is genuinely ambiguous across all labels.\n- You may inspect any file while developing rule.py, but the final submitted rule.py must not read, open, import, or depend on any files at prediction time, including test_samples/\n- The final rule.py must be able to run on a dataframe alone.", + "evaluation": "Evaluation:\nThe primary evaluation metric is top-1 accuracy: the first label returned by predict(df) is compared with the hidden correct label.\nA secondary shortlist score may also be reported. For a returned list of length m, the sample receives score 1/m if the hidden correct label appears anywhere in the list, and 0 otherwise. Therefore, unnecessary extra labels reduce the secondary score.", + "runtime_constraints": "Additional requirements:\n- If you create intermediate files, images, scripts, or notes while solving the task, create them in the current working directory.\n- Internet access is disabled.", + "ordered_field_agent_prompt": [ + [ + "sample_source", + "\n" + ], + [ + "environment_description", + "\n\n" + ], + [ + "intervention_semantics", + "\n\n" + ], + [ + "label_space", + "\n\n" + ], + [ + "task_artifact", + "\n" + ], + [ + "prediction_format", + "\n\n" + ], + [ + "fewshot_context", + "\n\n" + ], + [ + "mode_specific_requirements", + "\n\n" + ], + [ + "evaluation", + "\n\n" + ], + [ + "runtime_constraints", + "" + ] + ] + }, + "recipe_info": { + "type_of_request": "open-ended", + "desc_level": "none", + "noise_level": "high", + "number_train_samples_per_class": 0, + "number_test_samples": 10, + "is_adversarial": null, + "question_seed": 4, + "test_set_slug": "home", + "shot_slug": 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chars,16.083333333333332,650.0,0,16,Model inspected features directly,0,[],51.435509117422775 +gemini_calibration_none_all_environments_all_seeds.json,gemini_calibration_none_all_environments_all_seeds,CALIBRATION,DONE,gemini_3_1_pro_high,BallDrop,frost_01234-anchor_4,frost_01234-anchor,4,direct,high,none,0,10,2026-05-06__13-58-39__gemini_3_1_pro_high__balldrop__frost_01234-anchor_4,1.0,1.0,1.0,,,,0.2585872,227498,9816,10,9,130.582,130.582,0.0,continuous_elapsed,7,109,0,0,1895,270.7142857142857,137,5203,137 lines + 5203 chars,19.571428571428573,743.2857142857143,0,8,Model inspected features directly,0,[],72.4629798192838 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+gemini_calibration_none_all_environments_all_seeds.json,gemini_calibration_none_all_environments_all_seeds,CALIBRATION,DONE,gemini_3_1_pro_high,BounceBall,frost_01234-anchor_1,frost_01234-anchor,1,direct,high,none,0,10,2026-05-06__13-58-39__gemini_3_1_pro_high__dampedmassbetweenwalls__frost_01234-anchor_1,1.0,1.0,1.0,,,,0.6123104,768562,25327,23,22,481.025,481.025,0.0,continuous_elapsed,19,746,0,0,1353,71.21052631578948,827,30114,827 lines + 30114 chars,43.526315789473685,1584.9473684210527,0,22,Algorithmic method refined,0,[],39.69399706802239 +gemini_calibration_none_all_environments_all_seeds.json,gemini_calibration_none_all_environments_all_seeds,CALIBRATION,DONE,gemini_3_1_pro_high,BounceBall,frost_01234-anchor_2,frost_01234-anchor,2,direct,high,none,0,10,2026-05-06__13-58-39__gemini_3_1_pro_high__dampedmassbetweenwalls__frost_01234-anchor_2,0.9,0.9,1.0,,,,0.7114516,986444,27822,26,25,449.268,449.268,0.0,continuous_elapsed,21,403,0,0,766,36.476190476190474,488,19921,488 lines + 19921 chars,23.238095238095237,948.6190476190476,0,25,Model inspected features directly,0,[],42.128894378762034 +gemini_calibration_none_all_environments_all_seeds.json,gemini_calibration_none_all_environments_all_seeds,CALIBRATION,DONE,gemini_3_1_pro_high,BounceBall,frost_01234-anchor_3,frost_01234-anchor,3,direct,high,none,0,10,2026-05-06__13-58-39__gemini_3_1_pro_high__dampedmassbetweenwalls__frost_01234-anchor_3,1.0,1.0,1.0,,,,0.3956752,390809,16598,14,13,284.219,284.219,0.0,continuous_elapsed,11,307,11,0,961,87.36363636363636,363,14514,363 lines + 14514 chars,33.0,1319.4545454545455,0,11,Model inspected features directly,0,[],55.10728873036875 +gemini_calibration_none_all_environments_all_seeds.json,gemini_calibration_none_all_environments_all_seeds,CALIBRATION,DONE,gemini_3_1_pro_high,BounceBall,frost_01234-anchor_4,frost_01234-anchor,4,direct,high,none,0,10,2026-05-06__13-58-39__gemini_3_1_pro_high__dampedmassbetweenwalls__frost_01234-anchor_4,1.0,1.0,1.0,,,,0.6150804,953562,20790,29,28,529.596,529.596,0.0,continuous_elapsed,14,292,2,0,1215,86.78571428571429,379,14711,379 lines + 14711 chars,27.071428571428573,1050.7857142857142,0,28,Model inspected features directly,0,[],47.563465743704114 +gemini_calibration_none_all_environments_all_seeds.json,gemini_calibration_none_all_environments_all_seeds,CALIBRATION,DONE,gemini_3_1_pro_high,MassSlide,frost_01234-anchor_0,frost_01234-anchor,0,direct,high,none,0,10,2026-05-06__13-58-39__gemini_3_1_pro_high__inclinedplane__frost_01234-anchor_0,1.0,1.0,1.0,,,,1.1618186000000001,1702048,39728,41,40,693.672,693.672,0.0,continuous_elapsed,38,674,0,0,1015,26.710526315789473,783,33701,783 lines + 33701 chars,20.605263157894736,886.8684210526316,0,39,Model inspected features directly,0,[],30.130415862541994 +gemini_calibration_none_all_environments_all_seeds.json,gemini_calibration_none_all_environments_all_seeds,CALIBRATION,DONE,gemini_3_1_pro_high,MassSlide,frost_01234-anchor_1,frost_01234-anchor,1,direct,high,none,0,10,2026-05-06__13-58-39__gemini_3_1_pro_high__inclinedplane__frost_01234-anchor_1,0.9,0.9,1.0,,,,1.2431292,2132913,39066,56,55,887.618,887.618,0.0,continuous_elapsed,53,793,9,0,703,13.264150943396226,899,30994,899 lines + 30994 chars,16.962264150943398,584.7924528301887,0,53,Model inspected features directly,0,[],24.46671551611213 +gemini_calibration_none_all_environments_all_seeds.json,gemini_calibration_none_all_environments_all_seeds,CALIBRATION,DONE,gemini_3_1_pro_high,MassSlide,frost_01234-anchor_2,frost_01234-anchor,2,direct,high,none,0,10,2026-05-06__13-58-39__gemini_3_1_pro_high__inclinedplane__frost_01234-anchor_2,0.9,0.7,1.4,,,,1.324511,2222122,42150,55,54,781.504,781.504,0.0,continuous_elapsed,53,808,5,0,1310,24.71698113207547,901,31600,901 lines + 31600 chars,17.0,596.2264150943396,0,53,Model inspected features directly,0,[],32.71047099208573 +gemini_calibration_none_all_environments_all_seeds.json,gemini_calibration_none_all_environments_all_seeds,CALIBRATION,DONE,gemini_3_1_pro_high,MassSlide,frost_01234-anchor_3,frost_01234-anchor,3,direct,high,none,0,10,2026-05-06__13-58-39__gemini_3_1_pro_high__inclinedplane__frost_01234-anchor_3,0.7,0.7,1.0,,,,1.0139958,1430424,39918,35,34,639.232,639.232,0.0,continuous_elapsed,31,747,11,0,1581,51.0,875,32806,875 lines + 32806 chars,28.225806451612904,1058.258064516129,0,33,Model inspected features directly,0,[],37.3527341205004 +gemini_calibration_none_all_environments_all_seeds.json,gemini_calibration_none_all_environments_all_seeds,CALIBRATION,DONE,gemini_3_1_pro_high,MassSlide,frost_01234-anchor_4,frost_01234-anchor,4,direct,high,none,0,10,2026-05-06__13-58-39__gemini_3_1_pro_high__inclinedplane__frost_01234-anchor_4,0.8,0.8,1.0,,,,1.1906842,1741673,47001,43,42,891.846,891.846,0.0,continuous_elapsed,40,696,15,0,602,15.05,782,28679,782 lines + 28679 chars,19.55,716.975,0,41,Model inspected features directly,0,[],23.907615973312918 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,BallDrop,frost_01234-anchor_0,frost_01234-anchor,0,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__balldrop__frost_01234-anchor_0,1.0,1.0,1.0,,,,0.603418,400382,6990,12,15,165.374,165.374,0.0,continuous_elapsed,7,180,0,0,4338,619.7142857142857,148,7511,148 lines + 7511 chars,21.142857142857142,1073.0,0,10,Model inspected features directly,0,[],82.55277129082876 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,BallDrop,frost_01234-anchor_1,frost_01234-anchor,1,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__balldrop__frost_01234-anchor_1,1.0,1.0,1.0,,,,0.489485,249859,5777,8,11,118.259,118.259,0.0,continuous_elapsed,6,167,0,0,4383,730.5,146,7479,146 lines + 7479 chars,24.333333333333332,1246.5,0,6,Model inspected features directly,0,[],81.82971315226042 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,BallDrop,frost_01234-anchor_2,frost_01234-anchor,2,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__balldrop__frost_01234-anchor_2,1.0,1.0,1.0,,,,0.474501,204363,5257,8,9,118.74,118.74,0.0,continuous_elapsed,5,105,0,0,2444,488.8,88,4710,88 lines + 4710 chars,17.6,942.0,0,6,Model inspected features directly,0,[],78.50387362913774 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,BallDrop,frost_01234-anchor_3,frost_01234-anchor,3,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__balldrop__frost_01234-anchor_3,1.0,1.0,1.0,,,,0.43176,225366,4671,10,12,124.436,124.436,0.0,continuous_elapsed,7,87,0,0,1678,239.71428571428572,84,4039,84 lines + 4039 chars,12.0,577.0,1,8,Model inspected features directly,0,[],73.34618689658659 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,BallDrop,frost_01234-anchor_4,frost_01234-anchor,4,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__balldrop__frost_01234-anchor_4,1.0,1.0,1.0,,,,0.526745,275629,5796,8,10,124.142,124.142,0.0,continuous_elapsed,6,165,0,0,4906,817.6666666666666,141,6295,141 lines + 6295 chars,23.5,1049.1666666666667,0,6,Model inspected features directly,0,[],85.92336236815022 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,BounceBall,frost_01234-anchor_0,frost_01234-anchor,0,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__dampedmassbetweenwalls__frost_01234-anchor_0,1.0,1.0,1.0,,,,0.469818,159924,9301,7,11,124.391,124.391,0.0,continuous_elapsed,5,106,0,0,1543,308.6,93,5110,93 lines + 5110 chars,18.6,1022.0,0,5,Model inspected features directly,0,[],69.97798728476054 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,BounceBall,frost_01234-anchor_1,frost_01234-anchor,1,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__dampedmassbetweenwalls__frost_01234-anchor_1,1.0,1.0,1.0,,,,0.365122,176306,5980,8,11,97.634,97.634,0.0,continuous_elapsed,5,71,0,0,1785,357.0,64,3488,64 lines + 3488 chars,12.8,697.6,1,6,Model inspected features directly,0,[],75.17674422174971 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,BounceBall,frost_01234-anchor_2,frost_01234-anchor,2,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__dampedmassbetweenwalls__frost_01234-anchor_2,1.0,1.0,1.0,,,,0.431536,174134,8383,9,12,127.217,127.217,0.0,continuous_elapsed,7,142,0,0,1325,189.28571428571428,135,5877,135 lines + 5877 chars,19.285714285714285,839.5714285714286,1,8,Algorithmic method,0,[],58.17592964705326 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,BounceBall,frost_01234-anchor_3,frost_01234-anchor,3,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__dampedmassbetweenwalls__frost_01234-anchor_3,1.0,1.0,1.0,,,,0.660885,258297,7204,10,14,111.114,111.114,0.0,continuous_elapsed,6,77,0,0,1691,281.8333333333333,61,3258,61 lines + 3258 chars,10.166666666666666,543.0,2,6,Model inspected features directly,0,[],77.14552044493512 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,BounceBall,frost_01234-anchor_4,frost_01234-anchor,4,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__dampedmassbetweenwalls__frost_01234-anchor_4,1.0,1.0,1.0,,,,0.658958,340624,8337,9,9,127.757,127.757,0.0,continuous_elapsed,5,151,0,0,3547,709.4,140,6392,140 lines + 6392 chars,28.0,1278.4,0,7,Model inspected features directly,0,[],86.74289007811113 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,MassSlide,frost_01234-anchor_0,frost_01234-anchor,0,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__inclinedplane__frost_01234-anchor_0,1.0,1.0,1.0,,,,0.663351,247095,13454,10,13,181.818,181.818,0.0,continuous_elapsed,9,141,0,0,1834,203.77777777777777,135,7100,135 lines + 7100 chars,15.0,788.8888888888889,1,8,Model inspected features directly,0,[],71.01866456361725 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,MassSlide,frost_01234-anchor_1,frost_01234-anchor,1,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__inclinedplane__frost_01234-anchor_1,0.9,0.9,1.0,,,,1.219131,526917,20095,16,20,304.922,304.922,0.0,continuous_elapsed,12,172,0,0,4665,388.75,151,7690,151 lines + 7690 chars,12.583333333333334,640.8333333333334,1,14,Model inspected features directly,0,[],79.38769429808433 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,MassSlide,frost_01234-anchor_2,frost_01234-anchor,2,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__inclinedplane__frost_01234-anchor_2,1.0,1.0,1.0,,,,0.644189,238945,13348,9,14,196.058,196.058,0.0,continuous_elapsed,6,57,0,0,4082,680.3333333333334,55,3056,55 lines + 3056 chars,9.166666666666666,509.3333333333333,1,7,Model inspected features directly,0,[],81.72357872368548 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,MassSlide,frost_01234-anchor_3,frost_01234-anchor,3,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__inclinedplane__frost_01234-anchor_3,1.0,1.0,1.0,,,,0.848178,270354,16372,9,11,218.565,218.565,0.0,continuous_elapsed,5,93,0,0,4279,855.8,88,5132,88 lines + 5132 chars,17.6,1026.4,0,7,Model inspected features directly,0,[],82.99180247069354 +codex_calibration_none_all_environments_all_seeds_retry.json,codex_calibration_none_all_environments_all_seeds_retry,CALIBRATION,DONE,gpt_5_5_codex_high,MassSlide,frost_01234-anchor_4,frost_01234-anchor,4,direct,high,none,0,10,2026-05-06__18-29-11__gpt_5_5_codex_high__inclinedplane__frost_01234-anchor_4,1.0,1.0,1.0,,,,1.313305,536207,26505,17,21,351.42,351.42,0.0,continuous_elapsed,14,299,0,0,2374,169.57142857142858,235,12043,235 lines + 12043 chars,16.785714285714285,860.2142857142857,1,15,Model inspected features directly,0,[],68.09353208166758 +minimax_calibration_frost_01234_anchor_all_envs_all_seeds.json,minimax_calibration_frost_01234_anchor_all_envs_all_seeds,CALIBRATION,DONE,minimax-m2.7,BallDrop,frost_01234-anchor_0,frost_01234-anchor,0,direct,high,none,0,10,2026-05-07__02-36-05__minimax-m2-7__balldrop__frost_01234-anchor_0,0.4,0.4,1.0,,,,0.089637572,705447,23457,26,25,711.675,711.675,0.0,continuous_elapsed,23,819,0,0,1902,82.69565217391305,1083,49591,1083 lines + 49591 chars,47.08695652173913,2156.1304347826085,0,24,Model inspected features directly,0,[],43.85109958289373 +minimax_calibration_frost_01234_anchor_all_envs_all_seeds.json,minimax_calibration_frost_01234_anchor_all_envs_all_seeds,CALIBRATION,DONE,minimax-m2.7,BallDrop,frost_01234-anchor_1,frost_01234-anchor,1,direct,high,none,0,10,2026-05-07__02-36-05__minimax-m2-7__balldrop__frost_01234-anchor_1,0.4,0.4,1.0,,,,0.04499766,276609,15389,14,14,497.401,497.401,0.0,continuous_elapsed,11,296,0,0,1363,123.9090909090909,408,18395,408 lines + 18395 chars,37.09090909090909,1672.2727272727273,0,12,Algorithmic method,0,[],45.7430707831551 +minimax_calibration_frost_01234_anchor_all_envs_all_seeds.json,minimax_calibration_frost_01234_anchor_all_envs_all_seeds,CALIBRATION,DONE,minimax-m2.7,BallDrop,frost_01234-anchor_2,frost_01234-anchor,2,direct,high,none,0,10,2026-05-07__02-36-05__minimax-m2-7__balldrop__frost_01234-anchor_2,0.4,0.4,1.0,,,,0.05342096799999999,390586,19081,14,14,894.377,894.377,0.0,continuous_elapsed,12,370,0,0,2427,202.25,550,27482,550 lines + 27482 chars,45.833333333333336,2290.1666666666665,0,10,Model inspected features directly,0,[],52.79990250489577 +minimax_calibration_frost_01234_anchor_all_envs_all_seeds.json,minimax_calibration_frost_01234_anchor_all_envs_all_seeds,CALIBRATION,DONE,minimax-m2.7,BallDrop,frost_01234-anchor_3,frost_01234-anchor,3,direct,high,none,0,10,2026-05-07__02-36-05__minimax-m2-7__balldrop__frost_01234-anchor_3,0.4,0.4,1.0,,,,0.110826276,758479,33531,26,26,1632.626,1632.626,0.0,continuous_elapsed,23,832,0,0,2515,109.34782608695652,1096,48493,1096 lines + 48493 chars,47.65217391304348,2108.391304347826,0,24,Model inspected features directly,0,[],40.152468319105026 +minimax_calibration_frost_01234_anchor_all_envs_all_seeds.json,minimax_calibration_frost_01234_anchor_all_envs_all_seeds,CALIBRATION,DONE,minimax-m2.7,BallDrop,frost_01234-anchor_4,frost_01234-anchor,4,direct,high,none,0,10,2026-05-07__02-36-05__minimax-m2-7__balldrop__frost_01234-anchor_4,0.2,0.2,1.0,,,,0.052801096000000006,427646,16086,18,18,690.358,690.358,0.0,continuous_elapsed,16,523,0,0,1624,101.5,619,29918,619 lines + 29918 chars,38.6875,1869.875,0,16,Model inspected features directly,0,[],47.153194102811106 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chars,90.8,3871.733333333333,0,30,Algorithmic method,0,[],39.657284690768996 +minimax_calibration_frost_01234_anchor_all_envs_all_seeds.json,minimax_calibration_frost_01234_anchor_all_envs_all_seeds,CALIBRATION,DONE,minimax-m2.7,BounceBall,frost_01234-anchor_2,frost_01234-anchor,2,direct,high,none,0,10,2026-05-07__02-36-05__minimax-m2-7__dampedmassbetweenwalls__frost_01234-anchor_2,0.2,0.2,1.0,,,,0.13444040399999999,925831,58646,29,29,1564.254,1564.254,0.0,continuous_elapsed,24,568,0,0,2486,103.58333333333333,935,38736,935 lines + 38736 chars,38.958333333333336,1614.0,0,20,Model inspected features directly,0,[],32.68385938511347 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55799 chars,51.608695652173914,2426.0434782608695,0,24,Model inspected features directly,0,[],41.68519459034484 +minimax_calibration_frost_01234_anchor_all_envs_all_seeds.json,minimax_calibration_frost_01234_anchor_all_envs_all_seeds,CALIBRATION,DONE,minimax-m2.7,MassSlide,frost_01234-anchor_0,frost_01234-anchor,0,direct,high,none,0,10,2026-05-07__02-36-05__minimax-m2-7__inclinedplane__frost_01234-anchor_0,0.5,0.5,1.0,,,,0.059670172,353989,24010,18,17,723.653,723.653,0.0,continuous_elapsed,14,374,0,0,926,66.14285714285714,506,22793,506 lines + 22793 chars,36.142857142857146,1628.0714285714287,0,16,Model inspected features directly,0,[],33.84070548890203 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chars,28.045454545454547,1162.8636363636363,0,24,Model inspected features directly,0,[],32.36266134563821 +minimax_calibration_frost_01234_anchor_all_envs_all_seeds.json,minimax_calibration_frost_01234_anchor_all_envs_all_seeds,CALIBRATION,DONE,minimax-m2.7,MassSlide,frost_01234-anchor_3,frost_01234-anchor,3,direct,high,none,0,10,2026-05-07__02-36-05__minimax-m2-7__inclinedplane__frost_01234-anchor_3,0.6,0.6,1.0,,,,0.032511488,286752,9556,14,14,291.689,291.689,0.0,continuous_elapsed,11,348,0,0,998,90.72727272727273,440,18452,440 lines + 18452 chars,40.0,1677.4545454545455,0,12,Algorithmic method,0,[],52.79385370742014 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48693 chars,45.03333333333333,1623.1,0,31,Algorithmic method,0,[],75.52826979929128 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,frost_01234-orbit_1,frost_01234-orbit,1,code,high,high,0,10,2026-05-06__19-33-52__gemini_3_1_pro_high__balldrop__frost_01234-orbit_1,0.8,0.8,1.0,0.6197183098591549,0.6197183098591549,1.0,0.9366272,1546651,33896,22,21,370.04,370.04,0.0,continuous_elapsed,19,836,0,0,5139,270.4736842105263,823,32578,823 lines + 32578 chars,43.31578947368421,1714.6315789473683,0,19,Algorithmic method,0,[],73.90577731510409 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lines + 21268 chars,32.26315789473684,1119.3684210526317,0,23,Model inspected features directly,0,[],51.90340847921107 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,frost_01234-pine_2,frost_01234-pine,2,direct,high,high,0,10,2026-05-06__19-33-52__gemini_3_1_pro_high__inclinedplane__frost_01234-pine_2,0.6,0.6,1.0,,,,1.4496612,2496273,41233,62,61,637.132,637.132,0.0,continuous_elapsed,60,910,19,0,936,15.6,1008,37302,1008 lines + 37302 chars,16.8,621.7,0,60,Model inspected features directly,0,[],31.807565974347124 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,frost_01234-pine_3,frost_01234-pine,3,direct,high,high,0,10,2026-05-06__19-33-52__gemini_3_1_pro_high__inclinedplane__frost_01234-pine_3,0.7,0.7,1.0,,,,0.833291,1030483,31547,32,31,410.267,410.267,0.0,continuous_elapsed,31,693,16,0,800,25.806451612903224,786,28859,786 lines + 28859 chars,25.35483870967742,930.9354838709677,0,31,Model inspected features directly,0,[],34.63042533674504 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,frost_01234-pine_4,frost_01234-pine,4,direct,high,high,0,10,2026-05-06__19-33-52__gemini_3_1_pro_high__inclinedplane__frost_01234-pine_4,0.9,0.9,1.0,,,,1.3314062,2273416,43700,58,57,663.243,663.243,0.0,continuous_elapsed,56,1000,12,0,997,17.803571428571427,1050,40914,1050 lines + 40914 chars,18.75,730.6071428571429,0,56,Model inspected features directly,0,[],25.988487132977596 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,frost_01234-pine_0,frost_01234-pine,0,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__balldrop__frost_01234-pine_0,1.0,1.0,1.0,,,,0.973311,682197,13093,18,21,266.38,266.38,0.0,continuous_elapsed,12,438,0,0,4755,396.25,338,16195,338 lines + 16195 chars,28.166666666666668,1349.5833333333333,0,16,Model inspected features directly,0,[],76.48702873205588 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,frost_01234-pine_1,frost_01234-pine,1,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__balldrop__frost_01234-pine_1,0.7,0.7,1.0,,,,1.033944,556392,11256,15,17,260.362,260.362,0.0,continuous_elapsed,10,268,0,0,3831,383.1,216,10249,216 lines + 10249 chars,21.6,1024.9,0,12,Model inspected features directly,0,[],78.45448009933992 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,frost_01234-pine_2,frost_01234-pine,2,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__balldrop__frost_01234-pine_2,1.0,1.0,1.0,,,,1.083383,729805,13301,21,25,276.812,276.812,0.0,continuous_elapsed,15,315,0,0,4529,301.93333333333334,266,12298,266 lines + 12298 chars,17.733333333333334,819.8666666666667,1,19,Model inspected features directly,0,[],77.0804022159735 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,frost_01234-pine_3,frost_01234-pine,3,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__balldrop__frost_01234-pine_3,0.9,0.9,1.0,,,,0.56832,327012,8506,13,16,173.297,173.297,0.0,continuous_elapsed,9,229,0,0,2390,265.55555555555554,189,8677,189 lines + 8677 chars,21.0,964.1111111111111,1,11,Model inspected features directly,0,[],72.75645165147928 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,frost_01234-pine_4,frost_01234-pine,4,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__balldrop__frost_01234-pine_4,0.9,0.9,1.0,,,,0.970641,770625,13050,24,29,294.511,294.511,0.0,continuous_elapsed,10,306,0,0,2908,290.8,266,13593,266 lines + 13593 chars,26.6,1359.3,2,22,Model inspected features directly,0,[],70.11063902137509 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,frost_01234-pine_0,frost_01234-pine,0,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__dampedmassbetweenwalls__frost_01234-pine_0,1.0,1.0,1.0,,,,1.504569,909861,20448,21,25,281.178,281.178,0.0,continuous_elapsed,13,271,1,1,4448,342.15384615384613,238,11095,238 lines + 11095 chars,18.307692307692307,853.4615384615385,1,19,Model inspected features directly,0,[],79.43544024222898 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,frost_01234-pine_1,frost_01234-pine,1,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__dampedmassbetweenwalls__frost_01234-pine_1,0.9,0.9,1.0,,,,1.84005,1041048,25683,24,26,345.027,345.027,0.0,continuous_elapsed,18,400,0,0,3896,216.44444444444446,289,13431,289 lines + 13431 chars,16.055555555555557,746.1666666666666,1,20,Model inspected features directly,0,[],77.33749215015315 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,frost_01234-pine_2,frost_01234-pine,2,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__dampedmassbetweenwalls__frost_01234-pine_2,0.6,0.5,1.4,,,,1.961777,1066525,28528,26,31,551.813,551.813,0.0,continuous_elapsed,12,417,1,1,2473,206.08333333333334,340,14956,340 lines + 14956 chars,28.333333333333332,1246.3333333333333,1,24,Model inspected features directly,0,[],68.21981539017457 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,frost_01234-pine_3,frost_01234-pine,3,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__dampedmassbetweenwalls__frost_01234-pine_3,0.8,0.8,1.0,,,,1.351897,549125,25504,16,21,325.544,325.544,0.0,continuous_elapsed,10,305,0,0,3329,332.9,259,12850,259 lines + 12850 chars,25.9,1285.0,1,14,Model inspected features directly,0,[],71.55413474702165 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,frost_01234-pine_4,frost_01234-pine,4,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__dampedmassbetweenwalls__frost_01234-pine_4,0.8,0.8,1.0,,,,2.025291,963153,33685,21,25,424.856,424.856,0.0,continuous_elapsed,16,364,11,1,5902,368.875,283,14403,283 lines + 14403 chars,17.6875,900.1875,1,19,Model inspected features directly,0,[],81.97965825874695 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,frost_01234-pine_0,frost_01234-pine,0,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__inclinedplane__frost_01234-pine_0,1.0,1.0,1.0,,,,1.285087,720173,21553,17,20,332.14,332.14,0.0,continuous_elapsed,11,232,1,1,4341,394.6363636363636,182,10293,182 lines + 10293 chars,16.545454545454547,935.7272727272727,0,15,Model inspected features directly,0,[],82.35774236886584 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,frost_01234-pine_1,frost_01234-pine,1,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__inclinedplane__frost_01234-pine_1,1.0,1.0,1.0,,,,1.889603,1069459,28686,27,33,381.144,381.144,0.0,continuous_elapsed,14,321,10,7,3322,237.28571428571428,252,14387,252 lines + 14387 chars,18.0,1027.642857142857,1,25,Model inspected features directly,0,[],68.73053620972765 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,frost_01234-pine_2,frost_01234-pine,2,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__inclinedplane__frost_01234-pine_2,1.0,1.0,1.0,,,,0.919899,443745,15285,13,16,193.327,193.327,0.0,continuous_elapsed,10,186,0,0,4946,494.6,150,7894,150 lines + 7894 chars,15.0,789.4,1,11,Model inspected features directly,0,[],82.13308525774325 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,frost_01234-pine_3,frost_01234-pine,3,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__inclinedplane__frost_01234-pine_3,0.7,0.7,1.0,,,,1.833191,923269,25385,18,20,357.216,357.216,0.0,continuous_elapsed,13,262,0,0,5938,456.7692307692308,220,10783,220 lines + 10783 chars,16.923076923076923,829.4615384615385,0,16,Algorithmic method,0,[],84.9223888233304 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,frost_01234-pine_4,frost_01234-pine,4,direct,high,high,0,10,2026-05-06__02-08-12__gpt_5_5_codex_high__inclinedplane__frost_01234-pine_4,1.0,1.0,1.0,,,,1.743074,885376,23743,20,24,316.066,316.066,0.0,continuous_elapsed,13,228,1,1,2707,208.23076923076923,181,10499,181 lines + 10499 chars,13.923076923076923,807.6153846153846,1,18,Model inspected features directly,0,[],79.120854579968 +claude_balldrop_main_missing_retry.json,claude_balldrop_main_missing_retry,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-cloud_0,gentle_01234-cloud,0,direct,high,low,3,10,2026-05-07__02-03-20__claude_4_opus_high__balldrop__gentle_01234-cloud_0,1.0,1.0,1.0,,,,2.1226325,939110,66025,17,17,1283.715,1283.715,0.0,continuous_elapsed,14,896,0,0,3811,272.2142857142857,1174,49881,1174 lines + 49881 chars,83.85714285714286,3562.9285714285716,0,16,Model inspected features directly,0,[],45.700221539671794 +claude_paper_low_noise_3shot_all_environments_all_seeds.json,claude_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-cloud_1,gentle_01234-cloud,1,direct,high,low,3,10,2026-05-06__12-31-39__claude_4_opus_high__balldrop__gentle_01234-cloud_1,0.7,0.65,1.3,,,,4.8138025,2010728,152238,29,32,2882.395,2882.395,0.0,continuous_elapsed,21,3460,0,0,8145,387.85714285714283,4910,214973,4910 lines + 214973 chars,233.8095238095238,10236.809523809523,0,27,Algorithmic method,1,[18],34.6649168143031 +claude_paper_low_noise_3shot_all_environments_all_seeds.json,claude_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-cloud_2,gentle_01234-cloud,2,direct,high,low,3,10,2026-05-06__12-31-39__claude_4_opus_high__balldrop__gentle_01234-cloud_2,0.8,0.8,1.0,,,,3.4522185,2119329,95601,30,31,2021.832,2021.832,0.0,continuous_elapsed,21,2376,0,0,4902,233.42857142857142,2954,123612,2954 lines + 123612 chars,140.66666666666666,5886.285714285715,1,20,Algorithmic method,1,[27],44.47246474658974 +claude_paper_low_noise_3shot_all_environments_all_seeds.json,claude_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-cloud_3,gentle_01234-cloud,3,direct,high,low,3,10,2026-05-06__12-31-39__claude_4_opus_high__balldrop__gentle_01234-cloud_3,0.9,0.9,1.0,,,,3.456162,1942495,99229,23,25,1764.228,1764.228,0.0,continuous_elapsed,19,2557,0,0,5781,304.2631578947368,2961,133317,2961 lines + 133317 chars,155.8421052631579,7016.684210526316,0,21,Algorithmic method,1,[23],48.06603889382952 +claude_paper_low_noise_3shot_all_environments_all_seeds.json,claude_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-cloud_4,gentle_01234-cloud,4,direct,high,low,3,10,2026-05-06__12-31-39__claude_4_opus_high__balldrop__gentle_01234-cloud_4,0.7,0.2,5.0,,,,4.1655075,2835540,109743,39,42,1921.333,1921.333,0.0,continuous_elapsed,32,2738,0,0,7178,224.3125,3878,176171,3878 lines + 176171 chars,121.1875,5505.34375,0,38,Model inspected features directly,1,[30],45.05450195637264 +claude_paper_low_noise_3shot_all_environments_all_seeds.json,claude_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-cloud_0,gentle_01234-cloud,0,direct,high,low,3,10,2026-05-06__12-31-39__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-cloud_0,0.9,0.16666666666666666,6.0,,,,3.730324,1194808,125189,26,30,2292.046,2292.046,0.0,continuous_elapsed,21,1561,0,0,6014,286.3809523809524,2122,94688,2122 lines + 94688 chars,101.04761904761905,4508.952380952381,0,24,Algorithmic method,1,[13],41.496429357857295 +claude_paper_low_noise_3shot_all_environments_all_seeds.json,claude_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-cloud_1,gentle_01234-cloud,1,direct,high,low,3,10,2026-05-06__12-31-39__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-cloud_1,1.0,0.16666666666666666,6.0,,,,6.659861,3471087,196763,46,49,3488.021,3488.021,0.0,continuous_elapsed,42,3706,0,0,9448,224.95238095238096,4902,212184,4902 lines + 212184 chars,116.71428571428571,5052.0,0,42,Algorithmic method,1,[26],42.28104625125435 +claude_paper_low_noise_3shot_all_environments_all_seeds.json,claude_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-cloud_2,gentle_01234-cloud,2,direct,high,low,3,10,2026-05-06__12-31-39__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-cloud_2,1.0,0.16666666666666666,6.0,,,,4.7927435,2428682,142930,34,37,2525.03,2525.03,0.0,continuous_elapsed,22,2629,0,0,10328,469.45454545454544,3708,168749,3708 lines + 168749 chars,168.54545454545453,7670.409090909091,0,32,Algorithmic method,1,[18],38.329941173625244 +claude_paper_low_noise_3shot_all_environments_all_seeds.json,claude_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-cloud_3,gentle_01234-cloud,3,direct,high,low,3,10,2026-05-06__12-31-39__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-cloud_3,0.7,0.7,1.2,,,,2.2329495,914643,70776,20,23,1735.859,1735.859,0.0,continuous_elapsed,18,1572,0,0,5044,280.22222222222223,2833,127476,2833 lines + 127476 chars,157.38888888888889,7082.0,0,19,Algorithmic method,1,[17],54.77852627975499 +claude_paper_low_noise_3shot_all_environments_all_seeds.json,claude_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-cloud_4,gentle_01234-cloud,4,direct,high,low,3,10,2026-05-06__12-31-39__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-cloud_4,1.0,0.95,1.1,,,,3.401959,1182340,112262,20,23,2412.777,2412.777,0.0,continuous_elapsed,15,1673,0,0,5015,334.3333333333333,2321,105224,2321 lines + 105224 chars,154.73333333333332,7014.933333333333,0,16,Algorithmic method,1,[17],43.77299109870035 +claude_paper_low_noise_3shot_all_environments_all_seeds.json,claude_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,claude_4_opus_high,MassSlide,gentle_01234-cloud_0,gentle_01234-cloud,0,direct,high,low,3,10,2026-05-06__12-31-39__claude_4_opus_high__inclinedplane__gentle_01234-cloud_0,0.9,0.9,1.0,,,,4.9351385,2608867,145059,37,40,2722.859,2722.859,0.0,continuous_elapsed,33,2018,0,0,5273,159.78787878787878,2550,118861,2550 lines + 118861 chars,77.27272727272727,3601.848484848485,0,22,Algorithmic method,1,[24],35.2606729844968 +claude_paper_low_noise_3shot_all_environments_all_seeds.json,claude_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,claude_4_opus_high,MassSlide,gentle_01234-cloud_1,gentle_01234-cloud,1,direct,high,low,3,10,2026-05-06__12-31-39__claude_4_opus_high__inclinedplane__gentle_01234-cloud_1,0.9,0.2,5.0,,,,4.4987695,1889977,142049,31,34,2564.812,2564.812,0.0,continuous_elapsed,27,2084,0,0,2480,91.85185185185185,2619,116828,2619 lines + 116828 chars,97.0,4326.962962962963,0,29,Algorithmic method refined,1,[25],28.465244925948248 +claude_paper_low_noise_3shot_all_environments_all_seeds.json,claude_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,claude_4_opus_high,MassSlide,gentle_01234-cloud_2,gentle_01234-cloud,2,direct,high,low,3,10,2026-05-06__12-31-39__claude_4_opus_high__inclinedplane__gentle_01234-cloud_2,0.7,0.7,1.0,,,,3.9607085,1825288,121917,27,29,2222.162,2222.162,0.0,continuous_elapsed,24,1673,0,0,3461,144.20833333333334,2031,87793,2031 lines + 87793 chars,84.625,3658.0416666666665,0,25,Algorithmic method,1,[26],33.342869772749516 +claude_paper_low_noise_3shot_all_environments_all_seeds.json,claude_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,claude_4_opus_high,MassSlide,gentle_01234-cloud_3,gentle_01234-cloud,3,direct,high,low,3,10,2026-05-06__12-31-39__claude_4_opus_high__inclinedplane__gentle_01234-cloud_3,0.9,0.9,1.2,,,,4.0351615,1307581,135086,21,23,2399.968,2399.968,0.0,continuous_elapsed,17,1391,0,0,2055,120.88235294117646,1777,80428,1777 lines + 80428 chars,104.52941176470588,4731.058823529412,0,16,Algorithmic method,1,[18],30.814319336058468 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chars,48.170731707317074,1754.8292682926829,0,43,Algorithmic method,0,[],61.290703099384146 +gemini_paper_low_noise_3shot_all_environments_all_seeds.json,gemini_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,gentle_01234-cloud_1,gentle_01234-cloud,1,direct,high,low,3,10,2026-05-06__10-52-01__gemini_3_1_pro_high__balldrop__gentle_01234-cloud_1,0.5,0.5,1.0,,,,1.1105198,1717693,34807,31,30,417.214,417.214,0.0,continuous_elapsed,27,762,0,0,9291,344.1111111111111,861,34772,861 lines + 34772 chars,31.88888888888889,1287.851851851852,0,29,Model inspected features directly,0,[],69.74857017782084 +gemini_paper_low_noise_3shot_all_environments_all_seeds.json,gemini_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,gentle_01234-cloud_2,gentle_01234-cloud,2,direct,high,low,3,10,2026-05-06__10-52-01__gemini_3_1_pro_high__balldrop__gentle_01234-cloud_2,0.5,0.5,1.6,,,,2.6418694,4952051,59819,58,57,1397.966,1397.966,0.0,continuous_elapsed,54,2073,0,0,4725,87.5,2252,82554,2252 lines + 82554 chars,41.7037037037037,1528.7777777777778,0,56,Algorithmic method,0,[],63.831519856988784 +gemini_paper_low_noise_3shot_all_environments_all_seeds.json,gemini_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,gentle_01234-cloud_3,gentle_01234-cloud,3,direct,high,low,3,10,2026-05-06__10-52-01__gemini_3_1_pro_high__balldrop__gentle_01234-cloud_3,0.9,0.8,1.2,,,,0.9899595999999999,977174,35482,26,25,391.718,391.718,0.0,continuous_elapsed,22,1426,0,0,1725,78.4090909090909,1481,56031,1481 lines + 56031 chars,67.31818181818181,2546.8636363636365,0,23,Algorithmic method,0,[],46.50315046676887 +gemini_paper_low_noise_3shot_all_environments_all_seeds.json,gemini_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,gentle_01234-cloud_4,gentle_01234-cloud,4,direct,high,low,3,10,2026-05-06__10-52-01__gemini_3_1_pro_high__balldrop__gentle_01234-cloud_4,0.8,0.6666666666666667,2.0,,,,0.9761124,1069548,35652,23,22,415.961,415.961,0.0,continuous_elapsed,21,838,0,0,3586,170.76190476190476,896,35669,896 lines + 35669 chars,42.666666666666664,1698.5238095238096,0,21,Algorithmic method,0,[],59.20219624950272 +gemini_paper_low_noise_3shot_all_environments_all_seeds.json,gemini_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-cloud_0,gentle_01234-cloud,0,direct,high,low,3,10,2026-05-06__10-52-01__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-cloud_0,0.9,0.9,1.0,,,,1.1232204,1237002,43027,29,28,520.353,520.353,0.0,continuous_elapsed,27,1203,2,0,1866,69.11111111111111,1339,49146,1339 lines + 49146 chars,49.592592592592595,1820.2222222222222,0,27,Algorithmic method,0,[],41.84673526733701 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+ 64113 chars,50.53125,2003.53125,0,32,Algorithmic method,0,[],60.79647380758435 +gemini_paper_low_noise_3shot_all_environments_all_seeds.json,gemini_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-cloud_3,gentle_01234-cloud,3,direct,high,low,3,10,2026-05-06__10-52-01__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-cloud_3,0.8,0.8,1.0,,,,1.0289346000000001,1465086,41118,32,31,520.657,520.657,0.0,continuous_elapsed,27,1183,4,0,2442,90.44444444444444,1480,58658,1480 lines + 58658 chars,54.81481481481482,2172.5185185185187,0,28,Algorithmic method,0,[],51.688860141152496 +gemini_paper_low_noise_3shot_all_environments_all_seeds.json,gemini_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-cloud_4,gentle_01234-cloud,4,direct,high,low,3,10,2026-05-06__10-52-01__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-cloud_4,1.0,0.95,1.1,,,,1.5939188,2535439,49608,52,51,779.965,779.965,0.0,continuous_elapsed,44,1793,0,0,1904,43.27272727272727,1841,68189,1841 lines + 68189 chars,41.84090909090909,1549.75,0,46,Algorithmic method,0,[],39.56597841236913 +gemini_paper_low_noise_3shot_all_environments_all_seeds.json,gemini_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,gentle_01234-cloud_0,gentle_01234-cloud,0,direct,high,low,3,10,2026-05-06__10-52-01__gemini_3_1_pro_high__inclinedplane__gentle_01234-cloud_0,0.7,0.65,1.1,,,,1.9502004,3536550,58879,67,66,833.044,833.044,0.0,continuous_elapsed,60,1182,0,0,2196,36.6,1198,49950,1198 lines + 49950 chars,19.966666666666665,832.5,0,65,Model inspected features directly,0,[],40.241604528384045 +gemini_paper_low_noise_3shot_all_environments_all_seeds.json,gemini_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,gentle_01234-cloud_1,gentle_01234-cloud,1,direct,high,low,3,10,2026-05-06__10-52-01__gemini_3_1_pro_high__inclinedplane__gentle_01234-cloud_1,0.6,0.4,1.9,,,,1.4966332,2522327,51275,55,54,705.928,705.928,0.0,continuous_elapsed,53,1328,0,0,1239,23.37735849056604,1534,56259,1534 lines + 56259 chars,28.943396226415093,1061.4905660377358,0,53,Algorithmic method,0,[],31.96401297543039 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39201 chars,28.666666666666668,1005.1538461538462,0,39,Algorithmic method,0,[],27.46009767848039 +gemini_paper_low_noise_3shot_all_environments_all_seeds.json,gemini_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,gentle_01234-cloud_4,gentle_01234-cloud,4,direct,high,low,3,10,2026-05-06__10-52-01__gemini_3_1_pro_high__inclinedplane__gentle_01234-cloud_4,0.7,0.7,1.0,,,,1.0739982,1502220,32987,44,43,506.495,506.495,0.0,continuous_elapsed,42,752,0,0,984,23.428571428571427,862,32029,862 lines + 32029 chars,20.523809523809526,762.5952380952381,0,42,Model inspected features directly,0,[],32.53582602971627 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features directly,0,[],83.81361028491557 +codex_paper_low_noise_3shot_all_environments_all_seeds.json,codex_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,gentle_01234-cloud_2,gentle_01234-cloud,2,direct,high,low,3,10,2026-05-06__10-31-38__gpt_5_5_codex_high__balldrop__gentle_01234-cloud_2,1.0,1.0,1.0,,,,0.859383,478389,10393,14,15,222.997,222.997,0.0,continuous_elapsed,11,263,0,0,4970,451.8181818181818,208,9483,208 lines + 9483 chars,18.90909090909091,862.0909090909091,0,12,Model inspected features directly,0,[],82.53626378424003 +codex_paper_low_noise_3shot_all_environments_all_seeds.json,codex_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,gentle_01234-cloud_3,gentle_01234-cloud,3,direct,high,low,3,10,2026-05-06__10-31-38__gpt_5_5_codex_high__balldrop__gentle_01234-cloud_3,1.0,1.0,1.0,,,,0.815139,400263,7808,14,17,192.535,192.535,0.0,continuous_elapsed,11,216,0,0,2458,223.45454545454547,179,8772,179 lines + 8772 chars,16.272727272727273,797.4545454545455,1,12,Model inspected features directly,0,[],77.47368825382192 +codex_paper_low_noise_3shot_all_environments_all_seeds.json,codex_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,gentle_01234-cloud_4,gentle_01234-cloud,4,direct,high,low,3,10,2026-05-06__10-31-38__gpt_5_5_codex_high__balldrop__gentle_01234-cloud_4,1.0,1.0,1.0,,,,1.387088,1140220,17710,28,30,402.57,402.57,0.0,continuous_elapsed,22,498,0,0,5099,231.77272727272728,407,19420,407 lines + 19420 chars,18.5,882.7272727272727,1,26,Model inspected features directly,0,[],75.44917705044016 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chars,28.833333333333332,1325.75,0,16,Model inspected features directly,0,[],85.63460810362282 +codex_paper_low_noise_3shot_all_environments_all_seeds.json,codex_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,gentle_01234-cloud_2,gentle_01234-cloud,2,direct,high,low,3,10,2026-05-06__10-31-38__gpt_5_5_codex_high__dampedmassbetweenwalls__gentle_01234-cloud_2,0.7,0.7,1.0,,,,2.550208,1706648,36956,32,35,578.216,578.216,0.0,continuous_elapsed,17,501,0,0,4129,242.88235294117646,407,19712,407 lines + 19712 chars,23.941176470588236,1159.5294117647059,1,30,Model inspected features directly,0,[],77.40563047130401 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chars,21.833333333333332,947.1666666666666,0,14,Model inspected features directly,0,[],81.21589163159622 +codex_paper_low_noise_3shot_all_environments_all_seeds.json,codex_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,gentle_01234-cloud_0,gentle_01234-cloud,0,direct,high,low,3,10,2026-05-06__10-31-38__gpt_5_5_codex_high__inclinedplane__gentle_01234-cloud_0,1.0,1.0,1.0,,,,1.173629,758269,17210,23,25,333.653,333.653,0.0,continuous_elapsed,10,299,0,0,2840,284.0,225,12764,225 lines + 12764 chars,22.5,1276.4,1,21,Model inspected features directly,0,[],74.64371900355331 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chars,22.285714285714285,1282.7857142857142,1,26,Model inspected features directly,0,[],79.31285568379892 +codex_paper_low_noise_3shot_all_environments_all_seeds.json,codex_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,gentle_01234-cloud_3,gentle_01234-cloud,3,direct,high,low,3,10,2026-05-06__10-31-38__gpt_5_5_codex_high__inclinedplane__gentle_01234-cloud_3,0.9,0.9,1.0,,,,1.482496,676442,22289,21,24,361.679,361.679,0.0,continuous_elapsed,14,366,27,2,1877,134.07142857142858,288,14886,288 lines + 14886 chars,20.571428571428573,1063.2857142857142,1,19,Model inspected features directly,0,[],64.78089692422397 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chars,36.27272727272727,1424.090909090909,0,13,Algorithmic method,0,[],31.875876386283757 +minimax_paper_low_noise_3shot_all_environments_all_seeds.json,minimax_paper_low_noise_3shot_all_environments_all_seeds,PAPER,DONE,minimax-m2.7,MassSlide,gentle_01234-cloud_1,gentle_01234-cloud,1,direct,high,low,3,10,2026-05-06__11-26-15__minimax-m2-7__inclinedplane__gentle_01234-cloud_1,0.5,0.5,1.0,,,,0.056608284,379661,17926,18,21,399.011,399.011,0.0,continuous_elapsed,14,328,0,0,756,54.0,524,26591,524 lines + 26591 chars,37.42857142857143,1899.357142857143,0,15,Algorithmic method,0,[],37.973748487345674 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+gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,gentle_01234-harbor_2,gentle_01234-harbor,2,direct,none,high,3,10,2026-05-06__19-33-52__gemini_3_1_pro_high__balldrop__gentle_01234-harbor_2,0.2,0.2,1.0,,,,0.8966798,1444312,26885,40,39,536.494,536.494,0.0,continuous_elapsed,38,877,15,0,1101,28.973684210526315,963,36377,963 lines + 36377 chars,25.342105263157894,957.2894736842105,0,37,Algorithmic method,0,[],48.20893593372365 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,gentle_01234-harbor_3,gentle_01234-harbor,3,direct,none,high,3,10,2026-05-06__19-33-52__gemini_3_1_pro_high__balldrop__gentle_01234-harbor_3,0.3,0.2,5.0,,,,1.3829416,2681189,34086,64,63,973.099,973.099,0.0,continuous_elapsed,58,814,10,0,1299,22.396551724137932,885,34303,885 lines + 34303 chars,15.258620689655173,591.4310344827586,0,61,Algorithmic method,0,[],44.511971954425945 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,gentle_01234-harbor_4,gentle_01234-harbor,4,direct,none,high,3,10,2026-05-06__19-33-52__gemini_3_1_pro_high__balldrop__gentle_01234-harbor_4,0.2,0.2,5.0,,,,1.6366738,2984810,43489,63,62,1082.327,1082.327,0.0,continuous_elapsed,57,911,16,5,2877,50.473684210526315,952,36644,952 lines + 36644 chars,16.70175438596491,642.8771929824561,0,61,Algorithmic method,0,[],45.71894545860204 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-harbor_0,gentle_01234-harbor,0,direct,none,high,3,10,2026-05-06__19-33-52__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-harbor_0,0.1,0.16666666666666666,6.0,,,,1.606103,2831344,31201,67,66,1151.647,1151.647,0.0,continuous_elapsed,59,810,36,0,2191,37.13559322033898,839,33369,839 lines + 33369 chars,14.220338983050848,565.5762711864406,0,65,Algorithmic method,0,[],47.012748144859515 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-harbor_1,gentle_01234-harbor,1,direct,none,high,3,10,2026-05-06__19-33-52__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-harbor_1,0.3,0.25,2.9,,,,1.8858012000000002,4439886,34624,80,79,901.858,901.858,0.0,continuous_elapsed,41,791,2,2,2542,62.0,867,32694,867 lines + 32694 chars,21.146341463414632,797.4146341463414,0,77,Algorithmic method,0,[],43.8664501585076 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-harbor_2,gentle_01234-harbor,2,direct,none,high,3,10,2026-05-06__19-33-52__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-harbor_2,0.2,0.16666666666666666,6.0,,,,2.387605,5147258,51007,73,72,1051.357,1051.357,0.0,continuous_elapsed,70,1357,18,0,2516,35.94285714285714,1405,53822,1405 lines + 53822 chars,20.071428571428573,768.8857142857142,0,71,Algorithmic method,0,[],60.06590839584967 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-harbor_3,gentle_01234-harbor,3,direct,none,high,3,10,2026-05-06__19-33-52__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-harbor_3,0.2,0.2,3.0,,,,1.4677792,2722691,38365,60,59,955.471,955.471,0.0,continuous_elapsed,57,1208,1,0,2272,39.85964912280702,1262,45059,1262 lines + 45059 chars,22.140350877192983,790.5087719298245,0,57,Algorithmic method,0,[],44.60795642468769 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-harbor_4,gentle_01234-harbor,4,direct,none,high,3,10,2026-05-06__19-33-52__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-harbor_4,0.2,0.35,1.8,,,,0.8618978,1461115,26193,46,45,842.276,842.276,0.0,continuous_elapsed,42,1014,3,0,708,16.857142857142858,1110,40305,1110 lines + 40305 chars,26.428571428571427,959.6428571428571,1,43,Algorithmic method,0,[],40.575763416958196 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,gentle_01234-harbor_0,gentle_01234-harbor,0,direct,none,high,3,10,2026-05-06__19-33-52__gemini_3_1_pro_high__inclinedplane__gentle_01234-harbor_0,0.9,0.65,1.6,,,,1.1160098,2053708,27115,49,48,757.999,757.999,0.0,continuous_elapsed,23,501,25,21,551,23.956521739130434,520,19755,520 lines + 19755 chars,22.608695652173914,858.9130434782609,0,47,Model inspected features directly,0,[],39.20047038285285 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,gentle_01234-harbor_1,gentle_01234-harbor,1,direct,none,high,3,10,2026-05-06__19-33-52__gemini_3_1_pro_high__inclinedplane__gentle_01234-harbor_1,0.6,0.55,1.3,,,,0.5127642,465216,24233,18,17,306.077,306.077,0.0,continuous_elapsed,12,363,3,3,319,26.583333333333332,386,14493,386 lines + 14493 chars,32.166666666666664,1207.75,0,17,Algorithmic method refined,0,[],35.98159376754113 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,gentle_01234-harbor_2,gentle_01234-harbor,2,direct,none,high,3,10,2026-05-06__19-33-52__gemini_3_1_pro_high__inclinedplane__gentle_01234-harbor_2,0.4,0.4,1.0,,,,0.9985118000000001,1531534,35765,37,36,480.082,480.082,0.0,continuous_elapsed,32,888,0,0,1635,51.09375,910,35476,910 lines + 35476 chars,28.4375,1108.625,0,34,Algorithmic method,0,[],53.170256165311386 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,gentle_01234-harbor_3,gentle_01234-harbor,3,direct,none,high,3,10,2026-05-06__19-33-52__gemini_3_1_pro_high__inclinedplane__gentle_01234-harbor_3,0.3,0.3833333333333333,2.3,,,,1.743037,3306506,48830,65,64,994.757,994.757,0.0,continuous_elapsed,62,1428,1,0,1774,28.612903225806452,1546,57745,1546 lines + 57745 chars,24.93548387096774,931.3709677419355,1,62,Algorithmic method,0,[],43.19782869886162 +gemini_paper_ablation_high_noise_all_environments_all_seeds.json,gemini_paper_ablation_high_noise_all_environments_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,gentle_01234-harbor_4,gentle_01234-harbor,4,direct,none,high,3,10,2026-05-06__19-33-52__gemini_3_1_pro_high__inclinedplane__gentle_01234-harbor_4,0.4,0.26666666666666666,2.0,,,,0.8209204,1416869,24702,45,44,445.627,445.627,0.0,continuous_elapsed,39,903,15,0,1082,27.743589743589745,1036,37251,1036 lines + 37251 chars,26.564102564102566,955.1538461538462,0,42,Algorithmic method,0,[],45.09618351737971 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,gentle_01234-harbor_0,gentle_01234-harbor,0,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__balldrop__gentle_01234-harbor_0,0.3,0.35,1.5,,,,1.741002,1069386,23188,24,26,526.619,526.619,0.0,continuous_elapsed,17,380,1,1,2970,174.7058823529412,322,17109,322 lines + 17109 chars,18.941176470588236,1006.4117647058823,0,22,Model inspected features directly,0,[],72.86979993940339 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,gentle_01234-harbor_1,gentle_01234-harbor,1,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__balldrop__gentle_01234-harbor_1,0.4,0.5,1.3,,,,1.750711,1097057,20935,26,29,481.493,481.493,0.0,continuous_elapsed,15,519,1,1,3557,237.13333333333333,511,25368,511 lines + 25368 chars,34.06666666666667,1691.2,1,24,Algorithmic method,0,[],67.37517312564252 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,gentle_01234-harbor_2,gentle_01234-harbor,2,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__balldrop__gentle_01234-harbor_2,0.8,0.8,1.0,,,,1.760402,1123594,23728,27,29,518.9,518.9,0.0,continuous_elapsed,19,499,1,1,3045,160.26315789473685,357,17785,357 lines + 17785 chars,18.789473684210527,936.0526315789474,0,25,Model inspected features directly,0,[],65.97969018996122 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,gentle_01234-harbor_3,gentle_01234-harbor,3,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__balldrop__gentle_01234-harbor_3,0.8,0.7,1.2,,,,2.022643,1299149,20183,23,26,425.831,425.831,0.0,continuous_elapsed,17,360,2,2,5587,328.6470588235294,331,16145,331 lines + 16145 chars,19.470588235294116,949.7058823529412,0,21,Model inspected features directly,0,[],84.6862574229999 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,gentle_01234-harbor_4,gentle_01234-harbor,4,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__balldrop__gentle_01234-harbor_4,0.7,0.65,1.4,,,,2.648977,2070767,25373,34,39,565.574,565.574,0.0,continuous_elapsed,24,518,0,0,7257,302.375,472,22570,472 lines + 22570 chars,19.666666666666668,940.4166666666666,1,31,Model inspected features directly,0,[],79.06070735231867 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,gentle_01234-harbor_0,gentle_01234-harbor,0,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__dampedmassbetweenwalls__gentle_01234-harbor_0,0.2,0.2,1.0,,,,4.331686,3357122,46802,53,60,771.187,771.187,0.0,continuous_elapsed,31,847,1,1,6309,203.51612903225808,734,38923,734 lines + 38923 chars,23.677419354838708,1255.5806451612902,0,51,Model inspected features directly,0,[],73.65270370201678 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,gentle_01234-harbor_1,gentle_01234-harbor,1,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__dampedmassbetweenwalls__gentle_01234-harbor_1,0.5,0.5,1.0,,,,3.595464,2483844,39494,40,48,895.694,895.694,0.0,continuous_elapsed,26,1139,2,2,4944,190.15384615384616,793,47539,793 lines + 47539 chars,30.5,1828.423076923077,1,38,Model inspected features directly,0,[],69.73269645234664 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,gentle_01234-harbor_2,gentle_01234-harbor,2,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__dampedmassbetweenwalls__gentle_01234-harbor_2,0.5,0.5,1.0,,,,3.387969,2466399,32125,47,50,659.793,659.793,0.0,continuous_elapsed,27,522,2,2,5782,214.14814814814815,641,32716,641 lines + 32716 chars,23.74074074074074,1211.7037037037037,1,45,Model inspected features directly,0,[],72.82904673771992 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,gentle_01234-harbor_3,gentle_01234-harbor,3,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__dampedmassbetweenwalls__gentle_01234-harbor_3,0.2,0.2,1.0,,,,3.936458,3647164,34593,61,66,837.258,837.258,0.0,continuous_elapsed,29,925,2,2,2956,101.93103448275862,954,51796,954 lines + 51796 chars,32.89655172413793,1786.0689655172414,2,59,Model inspected features directly,0,[],60.12065517396537 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,gentle_01234-harbor_4,gentle_01234-harbor,4,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__dampedmassbetweenwalls__gentle_01234-harbor_4,0.6,0.55,1.2,,,,2.518795,1716239,33984,41,42,549.924,549.924,0.0,continuous_elapsed,27,642,1,1,4421,163.74074074074073,510,29683,510 lines + 29683 chars,18.88888888888889,1099.3703703703704,0,39,Model inspected features directly,0,[],64.4313538138666 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,gentle_01234-harbor_0,gentle_01234-harbor,0,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__inclinedplane__gentle_01234-harbor_0,0.9,0.9,1.0,,,,2.23952,944050,38269,20,23,500.397,500.397,0.0,continuous_elapsed,14,330,2,2,1694,121.0,273,13900,273 lines + 13900 chars,19.5,992.8571428571429,1,18,Model inspected features directly,0,[],71.78171128726868 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,gentle_01234-harbor_1,gentle_01234-harbor,1,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__inclinedplane__gentle_01234-harbor_1,0.3,0.3,1.0,,,,1.839095,916387,24732,22,24,333.679,333.679,0.0,continuous_elapsed,13,255,2,2,2119,163.0,244,12596,244 lines + 12596 chars,18.76923076923077,968.9230769230769,0,20,Model inspected features directly,0,[],77.61298990975368 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,gentle_01234-harbor_2,gentle_01234-harbor,2,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__inclinedplane__gentle_01234-harbor_2,0.4,0.4,1.0,,,,2.099874,1416390,36126,37,39,563.373,563.373,0.0,continuous_elapsed,15,337,0,0,3197,213.13333333333333,246,12323,246 lines + 12323 chars,16.4,821.5333333333333,1,33,Model inspected features directly,0,[],68.86782330721029 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,gentle_01234-harbor_3,gentle_01234-harbor,3,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__inclinedplane__gentle_01234-harbor_3,0.6,0.6,1.0,,,,2.236179,1110669,36039,24,26,493.548,493.548,0.0,continuous_elapsed,15,420,2,2,2314,154.26666666666668,360,18851,360 lines + 18851 chars,24.0,1256.7333333333333,0,22,Model inspected features directly,0,[],67.0753154616264 +codex_paper_ablation_all_environments_all_seeds.json,codex_paper_ablation_all_environments_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,gentle_01234-harbor_4,gentle_01234-harbor,4,direct,none,high,3,10,2026-05-06__02-08-12__gpt_5_5_codex_high__inclinedplane__gentle_01234-harbor_4,0.5,0.5,1.0,,,,2.897294,1752340,41179,30,36,524.633,524.633,0.0,continuous_elapsed,23,409,1,1,5489,238.65217391304347,377,19207,377 lines + 19207 chars,16.391304347826086,835.0869565217391,0,28,Model inspected features directly,0,[],81.5180083624431 +claude_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,claude_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-orbit_0,gentle_01234-orbit,0,code,high,high,3,10,2026-05-06__01-29-36__claude_4_opus_high__balldrop__gentle_01234-orbit_0,0.6,0.2,5.0,0.6014084507042253,0.2,5.0,5.193667,3138206,144975,38,40,2907.694,2907.694,0.0,continuous_elapsed,33,2833,0,0,13238,401.1515151515151,3446,145154,3446 lines + 145154 chars,104.42424242424242,4398.606060606061,0,35,Algorithmic method,1,[26],50.60059318577105 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+claude_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,claude_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-orbit_2,gentle_01234-orbit,2,code,high,high,3,10,2026-05-06__01-29-36__claude_4_opus_high__balldrop__gentle_01234-orbit_2,0.4,0.4,1.0,0.4056338028169014,0.4056338028169014,1.0,5.1844415,3408711,139193,54,57,2724.796,2724.796,0.0,continuous_elapsed,44,3226,0,0,5502,125.04545454545455,3305,136989,3305 lines + 136989 chars,75.11363636363636,3113.3863636363635,0,35,Algorithmic method,1,[28],38.676228166034676 +claude_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,claude_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-orbit_3,gentle_01234-orbit,3,code,high,high,3,10,2026-05-06__01-29-36__claude_4_opus_high__balldrop__gentle_01234-orbit_3,0.4,0.2,5.0,0.3408450704225352,0.2,5.0,4.707449,2727435,133694,38,40,2819.918,2819.918,0.0,continuous_elapsed,29,2280,0,0,9857,339.8965517241379,2487,104458,2487 lines + 104458 chars,85.75862068965517,3602.0,0,28,Algorithmic method,1,[21],49.10391124501586 +claude_balldrop_main_missing_retry.json,claude_balldrop_main_missing_retry,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-orbit_4,gentle_01234-orbit,4,code,high,high,3,10,2026-05-07__02-03-20__claude_4_opus_high__balldrop__gentle_01234-orbit_4,0.7,0.7,1.0,0.5394366197183098,0.5225352112676056,1.1070422535211268,4.969248,4296161,112835,61,63,2360.292,2360.292,0.0,continuous_elapsed,48,2783,0,0,8722,181.70833333333334,2460,107844,2460 lines + 107844 chars,51.25,2246.75,0,28,Algorithmic method,1,[37],47.38106844768957 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-orbit_0,gentle_01234-orbit,0,code,high,high,3,10,2026-05-07__01-55-25__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-orbit_0,0.9,0.75,1.5,0.7089201877934272,0.5854851330203442,1.4553990610328638,5.4358115,3662544,144122,53,55,2804.11,2804.11,0.0,continuous_elapsed,40,2894,0,0,6436,160.9,2001,89825,2001 lines + 89825 chars,50.025,2245.625,0,21,Algorithmic method,1,[25],43.10059975311542 +claude_damped_orbit_1_4_retry_20260702_070517.json,claude_damped_orbit_1_4_retry_20260702_070517,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-orbit_1,gentle_01234-orbit,1,code,high,high,3,10,2026-07-02__09-05-17__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-orbit_1,0.3,0.16666666666666666,6.0,0.25938967136150237,0.16666666666666666,6.0,7.7141485,4447747,219602,46,44,3921.825,36523.412,32601.587,summed_agent_attempt_durations,29,2805,3,3,5448,187.86206896551724,2185,92382,2185 lines + 92382 chars,75.34482758620689,3185.5862068965516,0,35,Algorithmic method,1,[24],33.1726710158949 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-orbit_2,gentle_01234-orbit,2,code,high,high,3,10,2026-07-01__claude_cli_retry6__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-orbit_2,0.2,0.16666666666666666,6.0,0.24882629107981222,0.16666666666666666,6.0,7.296433,5968777,172469,71,66,3284.289,44235.17,40950.881,summed_agent_attempt_durations,52,4016,0,0,13933,267.9423076923077,3901,161761,3901 lines + 161761 chars,75.01923076923077,3110.7884615384614,0,66,Algorithmic method,2,"[43,62]",59.8102469561665 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-orbit_3,gentle_01234-orbit,3,code,high,high,3,10,2026-05-07__01-55-25__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-orbit_3,0.2,0.16666666666666666,6.0,0.32511737089201875,0.16666666666666666,6.0,5.5554585,2177107,178480,31,35,3169.892,3169.892,0.0,continuous_elapsed,26,2345,0,0,8571,329.65384615384613,2807,122792,2807 lines + 122792 chars,107.96153846153847,4722.7692307692305,0,28,Algorithmic method,1,[21],36.96573394670737 +claude_damped_orbit_1_4_retry_20260702_070517.json,claude_damped_orbit_1_4_retry_20260702_070517,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-orbit_4,gentle_01234-orbit,4,code,high,high,3,10,2026-07-02__09-05-17__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-orbit_4,0.6,0.55,1.1,0.4307511737089202,0.4133411580594679,1.17018779342723,5.832787,4273629,147829,53,49,2870.048,36480.357,33610.309,summed_agent_attempt_durations,35,3547,0,0,7476,213.6,1636,73057,1636 lines + 73057 chars,46.74285714285714,2087.342857142857,0,25,Algorithmic method,1,[28],43.86940994586084 +claude_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,claude_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,claude_4_opus_high,MassSlide,gentle_01234-orbit_0,gentle_01234-orbit,0,code,high,high,3,10,2026-05-06__01-29-36__claude_4_opus_high__inclinedplane__gentle_01234-orbit_0,0.7,0.5,1.9,0.5492957746478874,0.4732394366197183,1.9028169014084506,3.668561,1596779,114802,23,25,2595.018,2595.018,0.0,continuous_elapsed,18,1099,0,0,1869,103.83333333333333,1165,51622,1165 lines + 51622 chars,64.72222222222223,2867.8888888888887,0,16,Algorithmic method,1,[20],32.37746697662878 +claude_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,claude_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,claude_4_opus_high,MassSlide,gentle_01234-orbit_1,gentle_01234-orbit,1,code,high,high,3,10,2026-05-06__01-29-36__claude_4_opus_high__inclinedplane__gentle_01234-orbit_1,1.0,0.6,1.8,0.7859154929577464,0.5549295774647888,1.8338028169014085,3.441696,2807211,81405,35,37,1580.464,1580.464,0.0,continuous_elapsed,25,1564,0,0,1834,73.36,1193,53896,1193 lines + 53896 chars,47.72,2155.84,0,26,Algorithmic method,0,[],31.173072696748587 +claude_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,claude_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,claude_4_opus_high,MassSlide,gentle_01234-orbit_2,gentle_01234-orbit,2,code,high,high,3,10,2026-05-06__01-29-36__claude_4_opus_high__inclinedplane__gentle_01234-orbit_2,0.8,0.65,1.7,0.6225352112676056,0.5373239436619718,1.7394366197183098,4.584629,3651996,110225,37,39,2171.606,2171.606,0.0,continuous_elapsed,29,1568,0,0,3785,130.51724137931035,1425,59958,1425 lines + 59958 chars,49.13793103448276,2067.5172413793102,0,22,Algorithmic method,1,[37],37.603322689241445 +claude_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,claude_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,claude_4_opus_high,MassSlide,gentle_01234-orbit_3,gentle_01234-orbit,3,code,high,high,3,10,2026-05-06__01-29-36__claude_4_opus_high__inclinedplane__gentle_01234-orbit_3,0.7,0.5,1.9,0.5380281690140845,0.4732394366197183,1.7746478873239437,4.619531,3064309,123375,32,34,2358.03,2358.03,0.0,continuous_elapsed,25,1446,0,0,2455,98.2,1133,52449,1133 lines + 52449 chars,45.32,2097.96,0,31,Algorithmic method,1,[32],29.257573484052752 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+opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds.json,opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,gentle_01234-orbit_0,gentle_01234-orbit,0,code,high,high,3,10,2026-05-06__01-14-30__gemini_3_1_pro_high__balldrop__gentle_01234-orbit_0,0.5,0.3433333333333334,3.0,0.47464788732394364,0.33704225352112677,2.6732394366197183,2.124929,3244981,54799,38,37,558.933,558.933,0.0,continuous_elapsed,33,1312,0,0,7554,228.9090909090909,1235,52768,1235 lines + 52768 chars,37.42424242424242,1599.030303030303,0,36,Algorithmic method,0,[],71.20117846738992 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lines + 66052 chars,42.8421052631579,1738.2105263157894,0,40,Algorithmic method,0,[],69.16712046836957 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-orbit_0,gentle_01234-orbit,0,code,high,high,3,10,2026-05-07__01-55-25__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-orbit_0,0.7,0.7,1.0,0.5011737089201878,0.5011737089201878,1.0,1.3965392,2813512,40750,45,44,509.064,509.064,0.0,continuous_elapsed,41,1075,0,0,4082,99.5609756097561,1069,42971,1069 lines + 42971 chars,26.073170731707318,1048.0731707317073,0,43,Algorithmic method,0,[],67.49967759309973 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-orbit_1,gentle_01234-orbit,1,code,high,high,3,10,2026-05-07__01-55-25__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-orbit_1,0.4,0.16666666666666666,6.0,0.19131455399061034,0.16666666666666666,6.0,2.4569446,5139077,58594,58,57,728.066,728.066,0.0,continuous_elapsed,53,1536,0,0,5698,107.50943396226415,1751,69381,1751 lines + 69381 chars,33.0377358490566,1309.0754716981132,0,55,Algorithmic method,0,[],66.58234618065558 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16476 chars,15.588235294117647,969.1764705882352,1,20,Algorithmic method,0,[],80.69836899426254 +codex_high_noise_3shot_balldrop_massslide_all_seeds.json,codex_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,gentle_01234-orbit_3,gentle_01234-orbit,3,code,high,high,3,10,2026-05-05__23-59-58__gpt_5_5_codex_high__inclinedplane__gentle_01234-orbit_3,0.7,0.7,1.0,0.47183098591549294,0.47183098591549294,1.0,1.688383,936383,24478,20,30,355.902,355.902,0.0,continuous_elapsed,21,600,0,0,4394,209.23809523809524,289,15659,289 lines + 15659 chars,13.761904761904763,745.6666666666666,2,18,Algorithmic method,0,[],79.63058960123628 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+retry_missing_artifact_minimax_damped_gentle_orbit_0.json,retry_missing_artifact_minimax_damped_gentle_orbit_0,PAPER,DONE,minimax-m2.7,BounceBall,gentle_01234-orbit_0,gentle_01234-orbit,0,code,high,high,3,10,2026-05-19__09-11-18__minimax-m2-7__dampedmassbetweenwalls__gentle_01234-orbit_0,0.2,0.2,1.0,0.2476525821596244,0.2476525821596244,1.0,0.267641086,3091255,44698,66,68,887.931,887.931,0.0,continuous_elapsed,55,1617,0,0,2887,52.49090909090909,2041,85808,2041 lines + 85808 chars,37.10909090909091,1560.1454545454546,0,49,Algorithmic method,0,[],38.73368551559786 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,minimax-m2.7,BounceBall,gentle_01234-orbit_1,gentle_01234-orbit,1,code,high,high,3,10,2026-05-07__01-55-25__minimax-m2-7__dampedmassbetweenwalls__gentle_01234-orbit_1,0.3,0.3,1.0,0.22417840375586853,0.22417840375586853,1.0,0.285585728,3353460,59841,77,79,1124.626,1124.626,0.0,continuous_elapsed,71,1666,0,0,2996,42.19718309859155,2064,84253,2064 lines + 84253 chars,29.070422535211268,1186.661971830986,0,73,Algorithmic method,0,[],35.90159214923563 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,minimax-m2.7,BounceBall,gentle_01234-orbit_2,gentle_01234-orbit,2,code,high,high,3,10,2026-05-07__01-55-25__minimax-m2-7__dampedmassbetweenwalls__gentle_01234-orbit_2,0.2,0.2,1.0,0.18896713615023475,0.18896713615023475,1.0,0.18364502000000002,1819401,50465,41,43,799.6,799.6,0.0,continuous_elapsed,35,1510,0,0,1886,53.885714285714286,2271,118942,2271 lines + 118942 chars,64.88571428571429,3398.342857142857,0,34,Algorithmic method,0,[],28.695447871466556 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,minimax-m2.7,BounceBall,gentle_01234-orbit_3,gentle_01234-orbit,3,code,high,high,3,10,2026-05-07__01-55-25__minimax-m2-7__dampedmassbetweenwalls__gentle_01234-orbit_3,0.1,0.1,1.1,0.12793427230046947,0.12519561815336464,1.1948356807511737,0.333413312,3830296,54832,79,80,1029.655,1029.655,0.0,continuous_elapsed,70,1909,0,0,3155,45.07142857142857,2445,101623,2445 lines + 101623 chars,34.92857142857143,1451.7571428571428,0,72,Algorithmic method,0,[],39.080291204393944 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,minimax-m2.7,BounceBall,gentle_01234-orbit_4,gentle_01234-orbit,4,code,high,high,3,10,2026-05-07__01-55-25__minimax-m2-7__dampedmassbetweenwalls__gentle_01234-orbit_4,0.2,0.2,1.0,0.18896713615023475,0.18896713615023475,1.0,0.302323564,3426581,67861,68,71,1310.343,1310.343,0.0,continuous_elapsed,59,2100,0,0,2189,37.101694915254235,3149,154719,3149 lines + 154719 chars,53.3728813559322,2622.35593220339,0,66,Algorithmic method,0,[],23.34722213973593 +minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,minimax-m2.7,MassSlide,gentle_01234-orbit_0,gentle_01234-orbit,0,code,high,high,3,10,2026-05-06__01-45-36__minimax-m2-7__inclinedplane__gentle_01234-orbit_0,0.6,0.6,1.0,0.37746478873239436,0.37746478873239436,1.0,0.15805144000000002,1487528,43115,49,49,1586.119,1586.119,0.0,continuous_elapsed,43,1006,0,0,1298,30.186046511627907,1246,52297,1246 lines + 52297 chars,28.976744186046513,1216.2093023255813,0,39,Algorithmic method,0,[],27.046271843732782 +minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,minimax-m2.7,MassSlide,gentle_01234-orbit_1,gentle_01234-orbit,1,code,high,high,3,10,2026-05-06__01-45-36__minimax-m2-7__inclinedplane__gentle_01234-orbit_1,0.3,0.3,1.0,0.5112676056338028,0.5112676056338028,1.0,0.23404022200000002,2298395,54628,56,59,2112.783,2112.783,0.0,continuous_elapsed,50,917,0,0,1146,22.92,1347,70817,1347 lines + 70817 chars,26.94,1416.34,0,44,Algorithmic method,0,[],22.79449516602978 +minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,minimax-m2.7,MassSlide,gentle_01234-orbit_2,gentle_01234-orbit,2,code,high,high,3,10,2026-05-06__01-45-36__minimax-m2-7__inclinedplane__gentle_01234-orbit_2,0.3,0.3,1.0,0.5098591549295775,0.5098591549295775,1.0,0.43555221899999996,5028364,59414,69,71,2163.154,2163.154,0.0,continuous_elapsed,54,1858,0,0,7039,130.35185185185185,1625,67872,1625 lines + 67872 chars,30.09259259259259,1256.888888888889,0,62,Algorithmic method,0,[],50.408843404153735 +minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,minimax-m2.7,MassSlide,gentle_01234-orbit_3,gentle_01234-orbit,3,code,high,high,3,10,2026-05-06__01-45-36__minimax-m2-7__inclinedplane__gentle_01234-orbit_3,0.5,0.5,1.0,0.5422535211267606,0.5422535211267606,1.0,0.24186997699999999,2406904,51487,49,51,1858.055,1858.055,0.0,continuous_elapsed,43,1338,0,0,1173,27.27906976744186,1996,96512,1996 lines + 96512 chars,46.41860465116279,2244.4651162790697,0,44,Algorithmic method,0,[],23.477262568153147 +minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,minimax-m2.7,MassSlide,gentle_01234-orbit_4,gentle_01234-orbit,4,code,high,high,3,10,2026-05-06__01-45-36__minimax-m2-7__inclinedplane__gentle_01234-orbit_4,0.9,0.9,1.0,0.4887323943661972,0.4887323943661972,1.0,0.169526875,1781778,42893,47,49,983.559,983.559,0.0,continuous_elapsed,42,1036,0,0,924,22.0,1452,71682,1452 lines + 71682 chars,34.57142857142857,1706.7142857142858,0,41,Algorithmic method,0,[],21.457877048494954 +claude_balldrop_main_missing_retry.json,claude_balldrop_main_missing_retry,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-pine_0,gentle_01234-pine,0,direct,high,high,3,10,2026-05-07__02-03-20__claude_4_opus_high__balldrop__gentle_01234-pine_0,0.7,0.2,5.0,,,,4.522737,2302198,134764,34,36,2532.014,2532.014,0.0,continuous_elapsed,30,3031,0,0,11524,384.1333333333333,3680,149703,3680 lines + 149703 chars,122.66666666666667,4990.1,0,32,Model inspected features directly,1,[22],52.02025368965551 +retry_missing_seed_claude_balldrop_gentle_pine_1_v2.json,retry_missing_seed_claude_balldrop_gentle_pine_1_v2,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-pine_1,gentle_01234-pine,1,direct,high,high,3,10,2026-05-18__19-51-37__claude_4_opus_high__balldrop__gentle_01234-pine_1,0.2,0.25,1.5,,,,2.7329125,1340444,82344,21,23,1632.152,1632.152,0.0,continuous_elapsed,18,1556,0,0,8890,493.8888888888889,1983,89594,1983 lines + 89594 chars,110.16666666666667,4977.444444444444,0,20,Model inspected features directly,1,[20],62.320437603453385 +claude_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,claude_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-pine_2,gentle_01234-pine,2,direct,high,high,3,10,2026-05-06__01-29-36__claude_4_opus_high__balldrop__gentle_01234-pine_2,0.6,0.2,5.0,,,,3.0415705,1571278,90137,27,29,1763.998,1763.998,0.0,continuous_elapsed,23,1419,0,0,7707,335.0869565217391,1924,85771,1924 lines + 85771 chars,83.65217391304348,3729.1739130434785,0,26,Model inspected features directly,1,[25],64.15265847602672 +claude_balldrop_main_missing_retry.json,claude_balldrop_main_missing_retry,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-pine_3,gentle_01234-pine,3,direct,high,high,3,10,2026-05-07__02-03-20__claude_4_opus_high__balldrop__gentle_01234-pine_3,0.5,0.2,5.0,,,,4.8136295,2095837,150330,37,40,3069.952,3069.952,0.0,continuous_elapsed,33,2445,0,0,6467,195.96969696969697,2915,119692,2915 lines + 119692 chars,88.33333333333333,3627.030303030303,0,36,Algorithmic method refined,1,[25],41.95535905534342 +claude_balldrop_main_missing_retry.json,claude_balldrop_main_missing_retry,PAPER,DONE,claude_4_opus_high,BallDrop,gentle_01234-pine_4,gentle_01234-pine,4,direct,high,high,3,10,2026-05-07__02-03-20__claude_4_opus_high__balldrop__gentle_01234-pine_4,0.5,0.2,5.0,,,,5.03757,2578532,149831,40,42,2869.581,2869.581,0.0,continuous_elapsed,37,3365,0,0,7621,205.97297297297297,4091,177371,4091 lines + 177371 chars,110.56756756756756,4793.810810810811,0,38,Algorithmic method,1,[26],44.02587430947722 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-pine_0,gentle_01234-pine,0,direct,high,high,3,10,2026-05-07__01-55-25__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-pine_0,0.6,0.16666666666666666,6.0,,,,3.2944545,1696757,97838,24,24,2183.954,2183.954,0.0,continuous_elapsed,18,1557,0,0,8998,499.8888888888889,1997,88384,1997 lines + 88384 chars,110.94444444444444,4910.222222222223,0,23,Model inspected features directly,1,[23],33.726663357149725 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-pine_1,gentle_01234-pine,1,direct,high,high,3,10,2026-05-07__01-55-25__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-pine_1,0.6,0.16666666666666666,6.0,,,,4.2336635,1681130,135718,29,32,3146.485,3146.485,0.0,continuous_elapsed,21,2435,0,0,6218,296.0952380952381,3498,154469,3498 lines + 154469 chars,166.57142857142858,7355.666666666667,0,27,Algorithmic method,1,[20],41.000687858871494 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-pine_2,gentle_01234-pine,2,direct,high,high,3,10,2026-05-07__01-55-25__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-pine_2,0.2,0.16666666666666666,6.0,,,,4.708289,2285754,142484,34,34,2719.414,2719.414,0.0,continuous_elapsed,32,2141,0,0,9823,306.96875,2928,128008,2928 lines + 128008 chars,91.5,4000.25,0,33,Model inspected features directly,1,[26],51.89997098159351 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-pine_3,gentle_01234-pine,3,direct,high,high,3,10,2026-05-07__01-55-25__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-pine_3,0.2,0.16666666666666666,6.0,,,,2.92556,1249952,91897,24,24,3357.094,3357.094,0.0,continuous_elapsed,19,1656,0,0,9804,516.0,2090,91630,2090 lines + 91630 chars,110.0,4822.631578947368,0,22,Model inspected features directly,1,[18],66.72654886250709 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,claude_4_opus_high,BounceBall,gentle_01234-pine_4,gentle_01234-pine,4,direct,high,high,3,10,2026-05-07__01-55-25__claude_4_opus_high__dampedmassbetweenwalls__gentle_01234-pine_4,0.6,0.16666666666666666,6.0,,,,5.9370725,2587515,185603,37,40,3547.066,3547.066,0.0,continuous_elapsed,33,2419,0,0,9286,281.3939393939394,3317,144261,3317 lines + 144261 chars,100.51515151515152,4371.545454545455,0,35,Algorithmic method refined,1,[28],50.74825766601388 +claude_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,claude_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,claude_4_opus_high,MassSlide,gentle_01234-pine_0,gentle_01234-pine,0,direct,high,high,3,10,2026-05-06__01-29-36__claude_4_opus_high__inclinedplane__gentle_01234-pine_0,0.7,0.7,1.2,,,,3.671558,1937391,107948,24,26,2116.287,2116.287,0.0,continuous_elapsed,21,1449,0,0,1891,90.04761904761905,1937,90757,1937 lines + 90757 chars,92.23809523809524,4321.761904761905,0,22,Algorithmic method refined,0,[],27.526254648651598 +claude_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,claude_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,claude_4_opus_high,MassSlide,gentle_01234-pine_1,gentle_01234-pine,1,direct,high,high,3,10,2026-05-06__01-29-36__claude_4_opus_high__inclinedplane__gentle_01234-pine_1,0.9,0.9,1.0,,,,3.0116885,1724605,85875,23,25,1676.643,1676.643,0.0,continuous_elapsed,21,1343,0,0,3487,166.04761904761904,1608,72335,1608 lines + 72335 chars,76.57142857142857,3444.5238095238096,0,22,Algorithmic method refined,0,[],43.379522035495036 +claude_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,claude_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,claude_4_opus_high,MassSlide,gentle_01234-pine_2,gentle_01234-pine,2,direct,high,high,3,10,2026-05-06__01-29-36__claude_4_opus_high__inclinedplane__gentle_01234-pine_2,0.9,0.95,1.1,,,,4.4171545,3026638,116051,34,36,2269.91,2269.91,0.0,continuous_elapsed,31,1849,0,0,2863,92.35483870967742,2278,97900,2278 lines + 97900 chars,73.48387096774194,3158.064516129032,0,31,Algorithmic method,1,[34],33.799199879342225 +claude_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,claude_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,claude_4_opus_high,MassSlide,gentle_01234-pine_3,gentle_01234-pine,3,direct,high,high,3,10,2026-05-06__01-29-36__claude_4_opus_high__inclinedplane__gentle_01234-pine_3,0.7,0.7,1.0,,,,4.8472975,2423278,145253,46,48,2656.022,2656.022,0.0,continuous_elapsed,37,2875,0,0,2225,60.13513513513514,3373,153978,3373 lines + 153978 chars,91.16216216216216,4161.5675675675675,0,39,Algorithmic method,1,[23],28.316383087645466 +claude_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,claude_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,claude_4_opus_high,MassSlide,gentle_01234-pine_4,gentle_01234-pine,4,direct,high,high,3,10,2026-05-06__01-29-36__claude_4_opus_high__inclinedplane__gentle_01234-pine_4,1.0,0.95,1.1,,,,2.964161,1753647,83390,25,27,1778.032,1778.032,0.0,continuous_elapsed,23,1440,0,0,2653,115.34782608695652,1814,77225,1814 lines + 77225 chars,78.8695652173913,3357.608695652174,0,23,Algorithmic method refined,0,[],37.24475990258727 +opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds.json,opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,gentle_01234-pine_0,gentle_01234-pine,0,direct,high,high,3,10,2026-05-06__01-14-30__gemini_3_1_pro_high__balldrop__gentle_01234-pine_0,0.6,0.6,1.4,,,,2.1483966,3644850,60716,46,45,695.345,695.345,0.0,continuous_elapsed,42,1689,0,0,9717,231.35714285714286,1846,70519,1846 lines + 70519 chars,43.95238095238095,1679.0238095238096,0,44,Algorithmic method refined,0,[],64.7417460655527 +opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds.json,opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,gentle_01234-pine_1,gentle_01234-pine,1,direct,high,high,3,10,2026-05-06__01-14-30__gemini_3_1_pro_high__balldrop__gentle_01234-pine_1,0.6,0.6,1.0,,,,0.8188248,1152537,29949,27,26,322.147,322.147,0.0,continuous_elapsed,22,456,0,0,3314,150.63636363636363,470,18493,470 lines + 18493 chars,21.363636363636363,840.5909090909091,0,26,Model inspected features directly,0,[],64.4379101679025 +opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds.json,opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,gentle_01234-pine_2,gentle_01234-pine,2,direct,high,high,3,10,2026-05-06__01-14-30__gemini_3_1_pro_high__balldrop__gentle_01234-pine_2,0.6,0.65,1.3,,,,1.7019098000000001,3318919,51589,50,49,573.634,573.634,0.0,continuous_elapsed,45,1032,4,0,4604,102.31111111111112,1173,57662,1173 lines + 57662 chars,26.066666666666666,1281.3777777777777,0,46,Algorithmic method,0,[],63.215248485948 +opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds.json,opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,gentle_01234-pine_3,gentle_01234-pine,3,direct,high,high,3,10,2026-05-06__01-14-30__gemini_3_1_pro_high__balldrop__gentle_01234-pine_3,0.1,0.13333333333333333,3.0,,,,1.1120896,2086136,35497,31,30,393.942,393.942,0.0,continuous_elapsed,26,1188,0,0,4737,182.19230769230768,1339,49694,1339 lines + 49694 chars,51.5,1911.3076923076924,0,27,Algorithmic method,0,[],67.2377781603961 +opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds.json,opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gemini_3_1_pro_high,BallDrop,gentle_01234-pine_4,gentle_01234-pine,4,direct,high,high,3,10,2026-05-06__01-14-30__gemini_3_1_pro_high__balldrop__gentle_01234-pine_4,0.3,0.3,1.4,,,,1.6504972,2325845,52087,32,31,529.285,529.285,0.0,continuous_elapsed,29,1092,5,0,5600,193.10344827586206,1254,47926,1254 lines + 47926 chars,43.241379310344826,1652.6206896551723,0,30,Model inspected features directly,0,[],69.44673427306378 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-pine_0,gentle_01234-pine,0,direct,high,high,3,10,2026-05-07__01-55-25__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-pine_0,0.9,0.9,1.0,,,,1.2991216,2225081,43911,54,53,500.086,500.086,0.0,continuous_elapsed,50,1207,1,0,1954,39.08,1361,50589,1361 lines + 50589 chars,27.22,1011.78,0,52,Algorithmic method refined,0,[],38.099105238181224 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-pine_1,gentle_01234-pine,1,direct,high,high,3,10,2026-05-07__01-55-25__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-pine_1,0.8,0.8,1.0,,,,2.5089154,4354925,79031,67,66,815.863,815.863,0.0,continuous_elapsed,59,2297,2,0,3552,60.20338983050848,2872,122361,2872 lines + 122361 chars,48.67796610169491,2073.915254237288,0,64,Algorithmic method,0,[],40.37169224613495 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-pine_2,gentle_01234-pine,2,direct,high,high,3,10,2026-05-07__01-55-25__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-pine_2,0.3,0.3,1.0,,,,1.9881704,3998638,62907,62,61,666.503,666.503,0.0,continuous_elapsed,60,1757,2,0,3501,58.35,1547,62011,1547 lines + 62011 chars,25.783333333333335,1033.5166666666667,0,59,Algorithmic method,0,[],48.39982129717229 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-pine_3,gentle_01234-pine,3,direct,high,high,3,10,2026-05-07__01-55-25__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-pine_3,0.3,0.3,1.5,,,,1.0615896,1466040,37627,40,39,416.776,416.776,0.0,continuous_elapsed,37,660,1,0,4741,128.13513513513513,980,41559,980 lines + 41559 chars,26.486486486486488,1123.2162162162163,0,38,Model inspected features directly,0,[],54.41408364517875 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,gemini_3_1_pro_high,BounceBall,gentle_01234-pine_4,gentle_01234-pine,4,direct,high,high,3,10,2026-05-07__01-55-25__gemini_3_1_pro_high__dampedmassbetweenwalls__gentle_01234-pine_4,0.2,0.2,1.0,,,,1.0629258000000001,1718130,31683,51,50,359.604,359.604,0.0,continuous_elapsed,49,843,8,0,2017,41.16326530612245,1047,44415,1047 lines + 44415 chars,21.367346938775512,906.4285714285714,0,48,Algorithmic method,0,[],43.18270427991096 +opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds.json,opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,gentle_01234-pine_0,gentle_01234-pine,0,direct,high,high,3,10,2026-05-06__01-14-30__gemini_3_1_pro_high__inclinedplane__gentle_01234-pine_0,0.7,0.4,2.0,,,,1.3543226000000002,2632438,41306,55,54,522.417,522.417,0.0,continuous_elapsed,51,1107,0,0,1445,28.333333333333332,1234,46354,1234 lines + 46354 chars,24.19607843137255,908.9019607843137,0,51,Algorithmic method,0,[],44.472198419895236 +opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds.json,opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,gentle_01234-pine_1,gentle_01234-pine,1,direct,high,high,3,10,2026-05-06__01-14-30__gemini_3_1_pro_high__inclinedplane__gentle_01234-pine_1,0.6,0.65,1.1,,,,1.4695818,2688777,48794,56,55,607.31,607.31,0.0,continuous_elapsed,51,1364,10,0,1859,36.450980392156865,1483,56319,1483 lines + 56319 chars,29.07843137254902,1104.2941176470588,0,53,Algorithmic method,0,[],39.547182551740434 +opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds.json,opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,gentle_01234-pine_2,gentle_01234-pine,2,direct,high,high,3,10,2026-05-06__01-14-30__gemini_3_1_pro_high__inclinedplane__gentle_01234-pine_2,0.9,0.8,1.4,,,,1.3824866,2526007,45298,50,49,540.143,540.143,0.0,continuous_elapsed,44,958,18,0,1733,39.38636363636363,1070,40783,1070 lines + 40783 chars,24.318181818181817,926.8863636363636,0,44,Algorithmic method,0,[],44.05838210105095 +opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds.json,opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,gentle_01234-pine_3,gentle_01234-pine,3,direct,high,high,3,10,2026-05-06__01-14-30__gemini_3_1_pro_high__inclinedplane__gentle_01234-pine_3,0.8,0.8,1.0,,,,1.30895,2136493,48698,44,43,517.598,517.598,0.0,continuous_elapsed,41,1163,24,0,1359,33.146341463414636,1315,51350,1315 lines + 51350 chars,32.073170731707314,1252.439024390244,0,41,Algorithmic method,0,[],41.32819985408261 +opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds.json,opus_gemini_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gemini_3_1_pro_high,MassSlide,gentle_01234-pine_4,gentle_01234-pine,4,direct,high,high,3,10,2026-05-06__01-14-30__gemini_3_1_pro_high__inclinedplane__gentle_01234-pine_4,0.7,0.6,1.4,,,,2.2247956,4933067,66672,64,63,745.257,745.257,0.0,continuous_elapsed,61,1499,2,0,4329,70.9672131147541,1714,68093,1714 lines + 68093 chars,28.098360655737704,1116.27868852459,1,57,Algorithmic method,0,[],54.07684528556123 +codex_high_noise_3shot_balldrop_massslide_all_seeds.json,codex_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,gentle_01234-pine_0,gentle_01234-pine,0,direct,high,high,3,10,2026-05-05__23-59-58__gpt_5_5_codex_high__balldrop__gentle_01234-pine_0,0.8,0.8,1.0,,,,1.415602,896546,13500,20,22,286.231,286.231,0.0,continuous_elapsed,15,380,0,0,6365,424.3333333333333,303,14574,303 lines + 14574 chars,20.2,971.6,0,18,Model inspected features directly,0,[],84.29719022693423 +codex_high_noise_3shot_balldrop_massslide_all_seeds.json,codex_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,gentle_01234-pine_1,gentle_01234-pine,1,direct,high,high,3,10,2026-05-05__23-59-58__gpt_5_5_codex_high__balldrop__gentle_01234-pine_1,0.4,0.45,1.1,,,,1.410589,1080755,18977,27,30,417.537,417.537,0.0,continuous_elapsed,16,583,0,0,3431,214.4375,428,21798,428 lines + 21798 chars,26.75,1362.375,1,25,Model inspected features directly,0,[],74.07239923048034 +codex_high_noise_3shot_balldrop_massslide_all_seeds.json,codex_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,gentle_01234-pine_2,gentle_01234-pine,2,direct,high,high,3,10,2026-05-05__23-59-58__gpt_5_5_codex_high__balldrop__gentle_01234-pine_2,1.0,1.0,1.0,,,,1.510459,1372001,15525,28,29,352.524,352.524,0.0,continuous_elapsed,16,387,0,0,7277,454.8125,344,15555,344 lines + 15555 chars,21.5,972.1875,0,25,Model inspected features directly,0,[],83.89895162469563 +codex_high_noise_3shot_balldrop_massslide_all_seeds.json,codex_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,gentle_01234-pine_3,gentle_01234-pine,3,direct,high,high,3,10,2026-05-05__23-59-58__gpt_5_5_codex_high__balldrop__gentle_01234-pine_3,1.0,0.95,1.1,,,,1.105513,661259,14615,18,19,294.988,294.988,0.0,continuous_elapsed,12,363,0,0,3509,292.4166666666667,321,15561,321 lines + 15561 chars,26.75,1296.75,0,16,Model inspected features directly,0,[],76.04152117827807 +codex_high_noise_3shot_balldrop_massslide_all_seeds.json,codex_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BallDrop,gentle_01234-pine_4,gentle_01234-pine,4,direct,high,high,3,10,2026-05-05__23-59-58__gpt_5_5_codex_high__balldrop__gentle_01234-pine_4,1.0,1.0,1.0,,,,0.902119,564665,13163,15,17,264.517,264.517,0.0,continuous_elapsed,9,294,0,0,3959,439.8888888888889,231,11171,231 lines + 11171 chars,25.666666666666668,1241.2222222222222,0,13,Model inspected features directly,0,[],82.56720387182561 +codex_paper_high_noise_3shot_bounceball_all_seeds.json,codex_paper_high_noise_3shot_bounceball_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,gentle_01234-pine_0,gentle_01234-pine,0,direct,high,high,3,10,2026-05-06__01-25-36__gpt_5_5_codex_high__dampedmassbetweenwalls__gentle_01234-pine_0,0.8,0.8,1.0,,,,1.726512,1236426,26833,27,29,411.585,411.585,0.0,continuous_elapsed,16,525,0,0,5563,347.6875,395,19058,395 lines + 19058 chars,24.6875,1191.125,1,25,Model inspected features directly,0,[],80.01534712474516 +codex_paper_high_noise_3shot_bounceball_all_seeds.json,codex_paper_high_noise_3shot_bounceball_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,gentle_01234-pine_1,gentle_01234-pine,1,direct,high,high,3,10,2026-05-06__01-25-36__gpt_5_5_codex_high__dampedmassbetweenwalls__gentle_01234-pine_1,0.6,0.65,1.1,,,,1.952958,1094784,26769,21,22,361.261,361.261,0.0,continuous_elapsed,16,476,0,0,6736,421.0,414,19519,414 lines + 19519 chars,25.875,1219.9375,0,19,Model inspected features directly,0,[],83.55479702547848 +codex_paper_high_noise_3shot_bounceball_all_seeds.json,codex_paper_high_noise_3shot_bounceball_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,gentle_01234-pine_2,gentle_01234-pine,2,direct,high,high,3,10,2026-05-06__01-25-36__gpt_5_5_codex_high__dampedmassbetweenwalls__gentle_01234-pine_2,1.0,1.0,1.0,,,,1.510308,793878,21173,20,23,308.742,308.742,0.0,continuous_elapsed,15,329,2,2,2931,195.4,277,14166,277 lines + 14166 chars,18.466666666666665,944.4,1,18,Model inspected features directly,0,[],73.07558819231332 +codex_paper_high_noise_3shot_bounceball_all_seeds.json,codex_paper_high_noise_3shot_bounceball_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,gentle_01234-pine_3,gentle_01234-pine,3,direct,high,high,3,10,2026-05-06__01-25-36__gpt_5_5_codex_high__dampedmassbetweenwalls__gentle_01234-pine_3,0.9,0.9,1.0,,,,2.002163,1299283,17270,18,19,295.645,295.645,0.0,continuous_elapsed,11,363,4,4,6428,584.3636363636364,323,15491,323 lines + 15491 chars,29.363636363636363,1408.2727272727273,0,16,Model inspected features directly,0,[],87.77165232509347 +codex_paper_high_noise_3shot_bounceball_all_seeds.json,codex_paper_high_noise_3shot_bounceball_all_seeds,PAPER,DONE,gpt_5_5_codex_high,BounceBall,gentle_01234-pine_4,gentle_01234-pine,4,direct,high,high,3,10,2026-05-06__01-25-36__gpt_5_5_codex_high__dampedmassbetweenwalls__gentle_01234-pine_4,0.6,0.6,1.0,,,,2.200909,1541429,32382,26,29,429.201,429.201,0.0,continuous_elapsed,21,478,0,0,9447,449.85714285714283,452,23618,452 lines + 23618 chars,21.523809523809526,1124.6666666666667,0,24,Model inspected features directly,0,[],84.15147926368253 +codex_high_noise_3shot_balldrop_massslide_all_seeds.json,codex_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,gentle_01234-pine_0,gentle_01234-pine,0,direct,high,high,3,10,2026-05-05__23-59-58__gpt_5_5_codex_high__inclinedplane__gentle_01234-pine_0,1.0,1.0,1.0,,,,1.163276,584422,21417,15,17,271.325,271.325,0.0,continuous_elapsed,10,305,0,0,3769,376.9,253,12591,253 lines + 12591 chars,25.3,1259.1,0,13,Model inspected features directly,0,[],79.19882939267501 +codex_high_noise_3shot_balldrop_massslide_all_seeds.json,codex_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,gentle_01234-pine_1,gentle_01234-pine,1,direct,high,high,3,10,2026-05-05__23-59-58__gpt_5_5_codex_high__inclinedplane__gentle_01234-pine_1,1.0,1.0,1.0,,,,1.026105,470661,18448,15,17,235.662,235.662,0.0,continuous_elapsed,11,249,0,0,2855,259.54545454545456,193,10144,193 lines + 10144 chars,17.545454545454547,922.1818181818181,0,13,Model inspected features directly,0,[],75.29243335167722 +codex_high_noise_3shot_balldrop_massslide_all_seeds.json,codex_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,gentle_01234-pine_2,gentle_01234-pine,2,direct,high,high,3,10,2026-05-05__23-59-58__gpt_5_5_codex_high__inclinedplane__gentle_01234-pine_2,0.6,0.6,1.0,,,,2.396212,1098260,33112,22,24,446.388,446.388,0.0,continuous_elapsed,15,248,2,2,3598,239.86666666666667,205,11665,205 lines + 11665 chars,13.666666666666666,777.6666666666666,0,20,Model inspected features directly,0,[],79.61901948551538 +codex_high_noise_3shot_balldrop_massslide_all_seeds.json,codex_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,gentle_01234-pine_3,gentle_01234-pine,3,direct,high,high,3,10,2026-05-05__23-59-58__gpt_5_5_codex_high__inclinedplane__gentle_01234-pine_3,0.8,0.8,1.0,,,,1.825851,1121433,24849,25,26,372.086,372.086,0.0,continuous_elapsed,11,392,27,3,2997,272.45454545454544,299,15109,299 lines + 15109 chars,27.181818181818183,1373.5454545454545,0,23,Model inspected features directly,0,[],79.07109588564786 +codex_high_noise_3shot_balldrop_massslide_all_seeds.json,codex_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,gpt_5_5_codex_high,MassSlide,gentle_01234-pine_4,gentle_01234-pine,4,direct,high,high,3,10,2026-05-05__23-59-58__gpt_5_5_codex_high__inclinedplane__gentle_01234-pine_4,1.0,1.0,1.0,,,,1.273064,454888,24720,14,17,304.673,304.673,0.0,continuous_elapsed,14,288,0,0,2843,203.07142857142858,251,11630,251 lines + 11630 chars,17.928571428571427,830.7142857142857,2,13,Algorithmic method,0,[],76.88287178072984 +minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,minimax-m2.7,BallDrop,gentle_01234-pine_0,gentle_01234-pine,0,direct,high,high,3,10,2026-05-06__01-45-36__minimax-m2-7__balldrop__gentle_01234-pine_0,0.2,0.2,1.0,,,,0.26547426399999996,1380718,39145,34,34,969.852,969.852,0.0,continuous_elapsed,28,1535,0,0,2304,82.28571428571429,2139,88412,2139 lines + 88412 chars,76.39285714285714,3157.5714285714284,0,32,Algorithmic method,0,[],43.47828443710464 +minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,minimax-m2.7,BallDrop,gentle_01234-pine_1,gentle_01234-pine,1,direct,high,high,3,10,2026-05-06__01-45-36__minimax-m2-7__balldrop__gentle_01234-pine_1,0.0,0.05,1.2,,,,0.148056364,922065,32824,26,27,719.123,719.123,0.0,continuous_elapsed,21,1505,0,0,2743,130.61904761904762,1776,79419,1776 lines + 79419 chars,84.57142857142857,3781.8571428571427,0,24,Algorithmic method,0,[],47.63047230327256 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chars,70.26666666666667,3029.6666666666665,0,17,Algorithmic method,0,[],36.71219637685946 +minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,minimax-m2.7,BallDrop,gentle_01234-pine_4,gentle_01234-pine,4,direct,high,high,3,10,2026-05-06__01-45-36__minimax-m2-7__balldrop__gentle_01234-pine_4,0.3,0.3,1.0,,,,0.145687576,1355670,42139,36,36,449.121,449.121,0.0,continuous_elapsed,31,1906,0,0,2850,91.93548387096774,2682,110615,2682 lines + 110615 chars,86.51612903225806,3568.2258064516127,0,33,Algorithmic method,0,[],35.57101190958438 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chars,75.07407407407408,3556.6666666666665,0,27,Algorithmic method,0,[],33.983054376848074 +paper_high_noise_dampedmass_gemini_claude_minimax_missing.json,paper_high_noise_dampedmass_gemini_claude_minimax_missing,PAPER,DONE,minimax-m2.7,BounceBall,gentle_01234-pine_2,gentle_01234-pine,2,direct,high,high,3,10,2026-05-07__01-55-25__minimax-m2-7__dampedmassbetweenwalls__gentle_01234-pine_2,0.0,0.2,2.0,,,,0.031039808,268836,9531,16,17,232.178,232.178,0.0,continuous_elapsed,11,508,0,0,319,29.0,636,26125,636 lines + 26125 chars,57.81818181818182,2375.0,0,14,Algorithmic method,0,[],28.864552620494614 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chars,38.5,1626.6666666666667,0,9,Algorithmic method,0,[],46.13317117664944 +minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds.json,minimax_retry_high_noise_3shot_balldrop_massslide_all_seeds,PAPER,DONE,minimax-m2.7,MassSlide,gentle_01234-pine_3,gentle_01234-pine,3,direct,high,high,3,10,2026-05-06__01-45-36__minimax-m2-7__inclinedplane__gentle_01234-pine_3,0.8,0.8,1.0,,,,0.13477269199999997,680755,20585,24,25,590.788,590.788,0.0,continuous_elapsed,20,565,0,0,1243,62.15,675,28615,675 lines + 28615 chars,33.75,1430.75,0,19,Algorithmic method,0,[],44.7425862451186 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b/website/environments/BallDrop/data_4.json new file mode 100644 index 0000000000000000000000000000000000000000..817abba082283a1c5eaafb4d81058777bf0bd71c --- /dev/null +++ b/website/environments/BallDrop/data_4.json @@ -0,0 +1,4815 @@ +{ + "answer": "no parameter changed", + "columns": [ + "Position", + "Velocity", + "Hard_Stop_f", + "time" + ], + "intervention_parameter": "no parameter changed", + "intervention_time": 5, + "rows": [ + { + "Hard_Stop_f": 0.0, + "Position": 5.686171, + "Velocity": -9.291636, + "time": 0.0025 + }, + { + "Hard_Stop_f": 0.0, + "Position": 5.569368, + "Velocity": -9.379305, + "time": 0.015 + }, + { + "Hard_Stop_f": 0.0, + "Position": 5.451469, + "Velocity": -9.466952, + "time": 0.0275 + }, + { + "Hard_Stop_f": 0.0, + "Position": 5.332475, + "Velocity": -9.554578, + "time": 0.04 + }, + { + "Hard_Stop_f": 0.0, + "Position": 5.212386, + "Velocity": -9.642181, + "time": 0.0525 + }, + { + "Hard_Stop_f": 0.0, + "Position": 5.091202, + "Velocity": -9.729764, + 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"gravity acceleration" + ], + "download_link": "https://huggingface.co/datasets/eth-siplab/tracebench/tree/main/questions/BallDrop", + "environment_id": "BallDrop", + "name": "BallDrop", + "observed_channels": [ + { + "id": "Position", + "label": "ball height", + "unit": "" + }, + { + "id": "Velocity", + "label": "ball velocity", + "unit": "" + }, + { + "id": "Hard_Stop_f", + "label": "contact impulse", + "unit": "N*s" + } + ], + "prompt_combinations": [ + { + "agent_instruction": "You are given multivariate time-series observations from BallDrop.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nby simulating a 1D vertical point-mass ball model under gravity. While the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity. Ground interaction is modeled with a restitution-based hard-stop law. When the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1]. The rebound points upward and its magnitude equals e times the pre-impact speed. When the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.\n\nThe observed channels are ball height, ball velocity, contact impulse.\n\nReturn the final ranked answer directly.", + "desc_level": "high", + "task_type": "direct", + "training_samples": "none" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BallDrop.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nby simulating a 1D vertical point-mass ball model under gravity. While the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity. Ground interaction is modeled with a restitution-based hard-stop law. When the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1]. The rebound points upward and its magnitude equals e times the pre-impact speed. When the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.\n\nThe observed channels are ball height, ball velocity, contact impulse.\n\nUse the provided labeled examples as calibration examples for the same benchmark condition.\n\nReturn the final ranked answer directly.", + "desc_level": "high", + "task_type": "direct", + "training_samples": ">0" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BallDrop.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nby simulating a 1D vertical point-mass ball model under gravity. While the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity. Ground interaction is modeled with a restitution-based hard-stop law. When the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1]. The rebound points upward and its magnitude equals e times the pre-impact speed. When the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.\n\nThe observed channels are ball height, ball velocity, contact impulse.\n\nReturn executable analysis code and the final ranked answer.", + "desc_level": "high", + "task_type": "code", + "training_samples": "none" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BallDrop.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nby simulating a 1D vertical point-mass ball model under gravity. While the ball is above the ground, its motion is governed by gravity and quadratic air drag acting opposite the direction of travel, and the position evolves according to the current velocity. Ground interaction is modeled with a restitution-based hard-stop law. When the ball reaches the ground with impact speed at or above 0.5 m/s, the impact is treated as instantaneous and the post-impact speed is set by the coefficient of restitution e in [0,1]. The rebound points upward and its magnitude equals e times the pre-impact speed. When the impact speed is below the threshold, the ball does not rebound and instead enters static contact at the ground.\n\nThe observed channels are ball height, ball velocity, contact impulse.\n\nUse the provided labeled examples as calibration examples for the same benchmark condition.\n\nReturn executable analysis code and the final ranked answer.", + "desc_level": "high", + "task_type": "code", + "training_samples": ">0" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BallDrop.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nThe observed channels are ball height, ball velocity, contact impulse.\n\nReturn the final ranked answer directly.", + "desc_level": "none", + "task_type": "direct", + "training_samples": "none" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BallDrop.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nThe observed channels are ball height, ball velocity, contact impulse.\n\nUse the provided labeled examples as calibration examples for the same benchmark condition.\n\nReturn the final ranked answer directly.", + "desc_level": "none", + "task_type": "direct", + "training_samples": ">0" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BallDrop.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nThe observed channels are ball height, ball velocity, contact impulse.\n\nReturn executable analysis code and the final ranked answer.", + "desc_level": "none", + "task_type": "code", + "training_samples": "none" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BallDrop.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nThe observed channels are ball height, ball velocity, contact impulse.\n\nUse the provided labeled examples as calibration examples for the same benchmark condition.\n\nReturn executable analysis code and the final ranked answer.", + "desc_level": "none", + "task_type": "code", + "training_samples": ">0" + } + ], + "sample_count": 5, + "short_one_line_description": "A ball is dropped from a height and observed over time." +} diff --git a/website/environments/BounceBall/data_1.json b/website/environments/BounceBall/data_1.json new file mode 100644 index 0000000000000000000000000000000000000000..8bc1a3e9fe479806c86c58c0edb3f5270d99ebf4 --- /dev/null +++ b/website/environments/BounceBall/data_1.json @@ -0,0 +1,4815 @@ +{ + "answer": "restitution_left", + "columns": [ + "Initial_Spacer_F_x", + "Initial_Spacer_F_v", + "Right_Hard_Stop_f", + "time" + ], + "intervention_parameter": "restitution_left", + "intervention_time": 5, + "rows": [ + { + "Initial_Spacer_F_v": -3.659051, + "Initial_Spacer_F_x": 4.426819, + "Right_Hard_Stop_f": 0.0, + "time": 0.02 + }, + { + "Initial_Spacer_F_v": 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59.68 + }, + { + "Initial_Spacer_F_v": -0.768318, + "Initial_Spacer_F_x": 10.529298, + "Right_Hard_Stop_f": 0.0, + "time": 59.759998 + }, + { + "Initial_Spacer_F_v": -0.764763, + "Initial_Spacer_F_x": 10.483341, + "Right_Hard_Stop_f": 0.0, + "time": 59.82 + }, + { + "Initial_Spacer_F_v": -0.760025, + "Initial_Spacer_F_x": 10.422398, + "Right_Hard_Stop_f": 0.0, + "time": 59.900002 + }, + { + "Initial_Spacer_F_v": -0.75529, + "Initial_Spacer_F_x": 10.361832, + "Right_Hard_Stop_f": 0.0, + "time": 59.98 + } + ], + "run_id": "4f60288e7a90582a841638ff2aaca5cc", + "source": "questions/BounceBall/dataframes/4f60288e7a90582a841638ff2aaca5cc.parquet" +} diff --git a/website/environments/BounceBall/description.json b/website/environments/BounceBall/description.json new file mode 100644 index 0000000000000000000000000000000000000000..e25bb1cc82a39492be7ead13bdf90714c40689cb --- /dev/null +++ b/website/environments/BounceBall/description.json @@ -0,0 +1,81 @@ +{ + "candidate_parameters": [ + "weight of the mass", + "coefficient of viscous damping", + "coefficient of restitution of the right wall", + "coefficient of restitution of the left wall", + "inclination angle of the rail" + ], + "download_link": "https://huggingface.co/datasets/eth-siplab/tracebench/tree/main/questions/BounceBall", + "environment_id": "BounceBall", + "name": "BounceBall", + "observed_channels": [ + { + "id": "Initial_Spacer_F_x", + "label": "mass position along the rail", + "unit": "" + }, + { + "id": "Initial_Spacer_F_v", + "label": "mass velocity along the rail", + "unit": "" + }, + { + "id": "Right_Hard_Stop_f", + "label": "force on the right wall", + "unit": "" + } + ], + "prompt_combinations": [ + { + "agent_instruction": "You are given multivariate time-series observations from BounceBall.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nby simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R. The rail may be tilted by a fixed inclination angle. When the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail. When the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.\n\nThe observed channels are mass position along the rail, mass velocity along the rail, force on the right wall.\n\nReturn the final ranked answer directly.", + "desc_level": "high", + "task_type": "direct", + "training_samples": "none" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BounceBall.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nby simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R. The rail may be tilted by a fixed inclination angle. When the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail. When the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.\n\nThe observed channels are mass position along the rail, mass velocity along the rail, force on the right wall.\n\nUse the provided labeled examples as calibration examples for the same benchmark condition.\n\nReturn the final ranked answer directly.", + "desc_level": "high", + "task_type": "direct", + "training_samples": ">0" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BounceBall.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nby simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R. The rail may be tilted by a fixed inclination angle. When the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail. When the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.\n\nThe observed channels are mass position along the rail, mass velocity along the rail, force on the right wall.\n\nReturn executable analysis code and the final ranked answer.", + "desc_level": "high", + "task_type": "code", + "training_samples": "none" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BounceBall.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nby simulating a damped mass moving along a one-dimensional rail between two rigid walls located at positions x_L and x_R. The rail may be tilted by a fixed inclination angle. When the mass is not in contact with either wall, its motion along the rail is governed by viscous drag, proportional to velocity and opposite the direction of motion, and, when the inclination angle is nonzero, by the component of gravity along the rail. When the mass hits a wall, the collision is modeled as an instantaneous impact: the direction of motion reverses and the post-impact speed is reduced according to that walls's restitution coefficient.\n\nThe observed channels are mass position along the rail, mass velocity along the rail, force on the right wall.\n\nUse the provided labeled examples as calibration examples for the same benchmark condition.\n\nReturn executable analysis code and the final ranked answer.", + "desc_level": "high", + "task_type": "code", + "training_samples": ">0" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BounceBall.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nThe observed channels are mass position along the rail, mass velocity along the rail, force on the right wall.\n\nReturn the final ranked answer directly.", + "desc_level": "none", + "task_type": "direct", + "training_samples": "none" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BounceBall.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nThe observed channels are mass position along the rail, mass velocity along the rail, force on the right wall.\n\nUse the provided labeled examples as calibration examples for the same benchmark condition.\n\nReturn the final ranked answer directly.", + "desc_level": "none", + "task_type": "direct", + "training_samples": ">0" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BounceBall.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nThe observed channels are mass position along the rail, mass velocity along the rail, force on the right wall.\n\nReturn executable analysis code and the final ranked answer.", + "desc_level": "none", + "task_type": "code", + "training_samples": "none" + }, + { + "agent_instruction": "You are given multivariate time-series observations from BounceBall.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nThe observed channels are mass position along the rail, mass velocity along the rail, force on the right wall.\n\nUse the provided labeled examples as calibration examples for the same benchmark condition.\n\nReturn executable analysis code and the final ranked answer.", + "desc_level": "none", + "task_type": "code", + "training_samples": ">0" + } + ], + "sample_count": 5, + "short_one_line_description": "A bouncing ball trajectory with impacts, velocity changes, and contact impulses." +} diff --git a/website/environments/MassSlide/data_1.json b/website/environments/MassSlide/data_1.json new file mode 100644 index 0000000000000000000000000000000000000000..bea7dfe39deec789141a174592d69d15f17f65ec --- /dev/null +++ b/website/environments/MassSlide/data_1.json @@ -0,0 +1,3009 @@ +{ + "answer": "coulomb_friction_coefficient", + "columns": [ + 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"mass_velocity": -70.401703, + "normal_force": 2.9886, + "time": 9.98 + } + ], + "run_id": "95357a192b404d9d942b699a96f3f91d", + "source": "questions/MassSlide/dataframes/95357a192b404d9d942b699a96f3f91d.parquet" +} diff --git a/website/environments/MassSlide/description.json b/website/environments/MassSlide/description.json new file mode 100644 index 0000000000000000000000000000000000000000..363e55d93bb9765bccb3930d79234cde923e18b4 --- /dev/null +++ b/website/environments/MassSlide/description.json @@ -0,0 +1,80 @@ +{ + "candidate_parameters": [ + "gravity acceleration", + "plane inclination angle", + "Coulomb friction coefficient", + "breakaway friction coefficient" + ], + "download_link": "https://huggingface.co/datasets/eth-siplab/tracebench/tree/main/questions/MassSlide", + "environment_id": "MassSlide", + "name": "MassSlide", + "observed_channels": [ + { + "id": "mass_velocity", + "label": "velocity of the mass along the plane", + "unit": "" + }, + { + "id": "friction_force", + "label": "friction force", + "unit": "" + }, + { + "id": "normal_force", + "label": "normal force", + "unit": "" + } + ], + "prompt_combinations": [ + { + "agent_instruction": "You are given multivariate time-series observations from MassSlide.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nby simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction. The coordinate axis is aligned with the plane. The block is also subject to an externally applied periodic force along the plane. The friction force acts along the plane and opposes motion. When the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction. A breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.\n\nThe observed channels are velocity of the mass along the plane, friction force, normal force.\n\nReturn the final ranked answer directly.", + "desc_level": "high", + "task_type": "direct", + "training_samples": "none" + }, + { + "agent_instruction": "You are given multivariate time-series observations from MassSlide.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nby simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction. The coordinate axis is aligned with the plane. The block is also subject to an externally applied periodic force along the plane. The friction force acts along the plane and opposes motion. When the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction. A breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.\n\nThe observed channels are velocity of the mass along the plane, friction force, normal force.\n\nUse the provided labeled examples as calibration examples for the same benchmark condition.\n\nReturn the final ranked answer directly.", + "desc_level": "high", + "task_type": "direct", + "training_samples": ">0" + }, + { + "agent_instruction": "You are given multivariate time-series observations from MassSlide.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nby simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction. The coordinate axis is aligned with the plane. The block is also subject to an externally applied periodic force along the plane. The friction force acts along the plane and opposes motion. When the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction. A breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.\n\nThe observed channels are velocity of the mass along the plane, friction force, normal force.\n\nReturn executable analysis code and the final ranked answer.", + "desc_level": "high", + "task_type": "code", + "training_samples": "none" + }, + { + "agent_instruction": "You are given multivariate time-series observations from MassSlide.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nby simulating a block of mass m moving along an infinitely long rigid plane inclined at an angle theta under gravitational acceleration g and Coulomb friction. The coordinate axis is aligned with the plane. The block is also subject to an externally applied periodic force along the plane. The friction force acts along the plane and opposes motion. When the block is moving, that is, when v(t) is nonzero, friction is modeled as kinetic Coulomb friction. A breakaway static-friction threshold is also modeled: when the block is at rest, motion starts only if the net driving force along the plane exceeds a breakaway limit.\n\nThe observed channels are velocity of the mass along the plane, friction force, normal force.\n\nUse the provided labeled examples as calibration examples for the same benchmark condition.\n\nReturn executable analysis code and the final ranked answer.", + "desc_level": "high", + "task_type": "code", + "training_samples": ">0" + }, + { + "agent_instruction": "You are given multivariate time-series observations from MassSlide.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nThe observed channels are velocity of the mass along the plane, friction force, normal force.\n\nReturn the final ranked answer directly.", + "desc_level": "none", + "task_type": "direct", + "training_samples": "none" + }, + { + "agent_instruction": "You are given multivariate time-series observations from MassSlide.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nThe observed channels are velocity of the mass along the plane, friction force, normal force.\n\nUse the provided labeled examples as calibration examples for the same benchmark condition.\n\nReturn the final ranked answer directly.", + "desc_level": "none", + "task_type": "direct", + "training_samples": ">0" + }, + { + "agent_instruction": "You are given multivariate time-series observations from MassSlide.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nThe observed channels are velocity of the mass along the plane, friction force, normal force.\n\nReturn executable analysis code and the final ranked answer.", + "desc_level": "none", + "task_type": "code", + "training_samples": "none" + }, + { + "agent_instruction": "You are given multivariate time-series observations from MassSlide.\n\nIdentify which physical parameter changed, or answer that no intervention occurred.\n\nThe observed channels are velocity of the mass along the plane, friction force, normal force.\n\nUse the provided labeled examples as calibration examples for the same benchmark condition.\n\nReturn executable analysis code and the final ranked answer.", + "desc_level": "none", + "task_type": "code", + "training_samples": ">0" + } + ], + "sample_count": 5, + "short_one_line_description": "A mass slides under force while friction and velocity are observed over time." +} diff --git a/website/leaderboard.json b/website/leaderboard.json new file mode 100644 index 0000000000000000000000000000000000000000..916d1c2bf34d20751a0b296e7cfc902bdb0b77ae --- /dev/null +++ b/website/leaderboard.json @@ -0,0 +1,760 @@ +{ + "canonical_scope": { + "context": "High", + "examples": "Three Examples", + "noise": "Low", + "required_seeds_per_simulator": 5, + "task_mode": "Code" + }, + "filters": { + "context": [ + "High", + "None" + ], + "examples": [ + "None", + "One Example", + 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