diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..dad41505a71913ffb01771d0c055ce44e1859dc6 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,6 +1,5 @@ *.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 @@ -10,7 +9,6 @@ *.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 @@ -35,7 +33,7 @@ saved_model/**/* 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 +*.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 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..58770027dbd0ec54a90378f4cf2079475d05dc54 --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +__pycache__/ +*.pyc +.DS_Store +.ipynb_checkpoints/ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..47b26f9ba34231bdcd96f69a82cd72ea5c7513b4 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 Aiming Lab + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d3530bb5afb843d61908eb00d9605af66b0aa11f --- /dev/null +++ b/README.md @@ -0,0 +1,183 @@ +--- +license: mit +language: +- en +pretty_name: ARC-Bench +size_categories: +- n<1K +task_categories: +- other +tags: +- autonomous-research +- ai-agents +- llm-agents +- scientific-discovery +- benchmark +- machine-learning +- high-energy-physics +- quantum-computing +- systems-biology +- statistics +configs: +- config_name: default + data_files: + - split: test + path: data/arc_bench.jsonl +- config_name: ml + data_files: + - split: test + path: data/ml.jsonl +- config_name: physics + data_files: + - split: test + path: data/physics.jsonl +- config_name: quantum + data_files: + - split: test + path: data/quantum.jsonl +- config_name: biology + data_files: + - split: test + path: data/biology.jsonl +- config_name: statistics + data_files: + - split: test + path: data/statistics.jsonl +--- + +# ARC-Bench + +**ARC-Bench** is a **55-topic, open-ended autonomous-research benchmark** spanning +five scientific domains. Each topic is not a fixed-input/fixed-output task — it is a +*research question* plus a structured briefing. A research agent (or a human) must +take a topic from question → experiment design → code → measurements → claims → +write-up, and the deliverable is graded against a weighted, multi-criteria rubric. + +| Domain | Count | IDs | Typical execution | +|---|---|---|---| +| Machine learning | 25 | `ML01`–`ML25` | CPU, `numpy`/`scipy`/`sklearn`/`statsmodels` | +| High-energy physics | 10 | `P01`–`P10` | Lagrangian → MadGraph MC → analysis → figure (paper reproduction) | +| Quantum | 10 | `Q01`–`Q10` | CPU, Qiskit 2.x statevector / VQE / QML | +| Systems biology | 7 | `B01`–`B07` | Constraint-based modelling (COBRApy / BiGG) | +| Statistics | 3 | `S01`–`S03` | Simulation studies (`numpy`/`scipy`/`statsmodels`) | + +The ML, quantum, and statistics topics are **open research questions** (the agent +designs the experiment); the physics topics are **published-paper reproductions** +(each scoped to a specific reference figure); the biology topics are +**constraint-based metabolic-modelling** studies. + +## Load it + +```python +from datasets import load_dataset + +# all 55 topics +ds = load_dataset("AIMING-Lab-UNC/ARC-Bench", split="test") + +# a single domain subset +ml = load_dataset("AIMING-Lab-UNC/ARC-Bench", "ml", split="test") + +row = ds[0] +print(row["id"], row["title"]) +print(row["metric_key"], row["metric_direction"]) +``` + +Available config names: `default` (all 55), `ml`, `physics`, `quantum`, +`biology`, `statistics`. + +## Schema + +Each row describes one benchmark topic. Deeply-nested / variable-shape fields are +stored as **JSON-encoded strings** so the table schema is stable across all +domains; parse them with `json.loads`. + +| Column | Type | Description | +|---|---|---| +| `id` | string | Topic id (`ML01`, `P03`, `Q07`, `B01`, `S02`, …) | +| `domain` | string | One of `ml` / `physics` / `quantum` / `biology` / `statistics` | +| `title` | string | Human-readable topic title | +| `topic` | string | One-line topic statement (from the domain registry) | +| `domains` | list[string] | Subfield tags (e.g. `["machine-learning","calibration"]`) | +| `arxiv_id` | string \| null | Source paper (physics reproductions; null for open questions) | +| `venue` | string | Benchmark venue label | +| `metric_key` | string | Headline metric name | +| `metric_direction` | string | `maximize` / `minimize` / `match_reference` | +| `gpu_required` | bool | Whether a GPU is needed (all topics are CPU-friendly → `false`) | +| `est_wall_clock_sec` | int | Rough single-run wall-clock budget | +| `synthesis` | string | The research briefing: background + what a credible study includes | +| `num_hypotheses` | int | Number of pre-registered hypotheses | +| `hypotheses` | string (JSON) | List of `{id, statement, measurable}` | +| `experiment_design` | string (JSON) | `research_question`, `conditions`, `baselines`, `metrics`, `datasets`, `compute_requirements` | +| `requirements` | string (JSON) | Agent-mode pass/fail gating items (physics + biology; `""` otherwise) | +| `rubric` | string (JSON) | Hierarchical weighted scoring rubric (code / execution / results buckets) | +| `rubric_num_leaves` | int | Number of leaf criteria in the rubric | +| `manifest_file` | string | Path to the raw manifest inside this repo (`tasks/…`) | +| `rubric_file` | string | Path to the raw rubric inside this repo (`tasks/…`) | + +### Raw inputs + +The flattened `data/*.jsonl` is convenient for `load_dataset`. The authoritative, +human-readable benchmark inputs are also shipped verbatim under `tasks/`: + +``` +tasks/ +├── meta_paper_quality.json # shared paper-quality meta-rubric (manual grading) +└── / + ├── topics.yaml # the domain topic registry + ├── manifests/.yaml # full per-topic briefing + └── rubrics/.json # weighted scoring rubric +``` + +## How a topic is scored + +Each topic carries a hierarchical rubric. For ML / quantum / statistics it has +three buckets — **Code Development**, **Code Execution**, **Result Analysis** +(weighted roughly 25 : 25 : 50). Physics and biology add a fourth +**Reproducibility** bucket. Leaf criteria are graded on scientific substance and +directional correctness of the evidence, not on rigid threshold matching (see each +rubric's `judging_note`). + +A second, optional layer — `tasks/meta_paper_quality.json` — grades the **paper +output** (writing, code orchestration, figure quality, factual accuracy) and is +intended for manual / vision-equipped grading rather than fast automated scoring. + +## Intended use + +- Evaluating autonomous-research / AI-scientist agents end-to-end. +- Studying agent behavior across heterogeneous scientific domains with a *single* + task format. +- As a stimulus set for human-in-the-loop or framework-comparison studies. + +The runner harness, baseline adapters, and judges are **not** part of this dataset; +they live in the source repository (link below). + +## Attribution + +The benchmark glue (manifests, rubrics, registries) is the authors' own work. Some +domain pipelines are driven by **external Claude-Code agents**, which should be +credited when reporting domain results: + +| Topic family | External agent | Upstream | +|---|---|---| +| `P01`–`P10` (HEP) | ColliderAgent | | +| `B01`–`B07` (metabolic) | Biology-Agent | constraint-based modelling pipeline | + +## Links + +- **Code / harness:** +- **Paper page:** + +## Citation + +```bibtex +@misc{arcbench2026, + title = {ARC-Bench: An Open-Ended Autonomous-Research Benchmark Across Five Scientific Domains}, + author = {Qiu, Shi and others}, + year = {2026}, + note = {https://huggingface.co/datasets/AIMING-Lab-UNC/ARC-Bench} +} +``` + +## License + +Released under the [MIT License](LICENSE). diff --git a/data/arc_bench.jsonl b/data/arc_bench.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..8a8ed3d1ba242beadd2ba93bcbd7e9e6853ddcbe --- /dev/null +++ b/data/arc_bench.jsonl @@ -0,0 +1,55 @@ +{"id": "ML01", "domain": "ml", "title": "Dropout regularization strategies on shallow tabular MLPs", "topic": "Comparing dropout regularization strategies (standard, spatial, variational) for preventing overfitting in shallow MLPs on tabular classification benchmarks", "domains": ["machine-learning", "regularization"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 240, "synthesis": "Dropout-style regularization is a cornerstone of neural-net training for\noverfitting control, but its impact on tabular classification with shallow\nMLPs remains subtle. Standard element-wise dropout is the default; spatial\n(feature-wise) dropout treats correlated input features as a block; MC /\nvariational dropout keeps dropout active at test time and averages over\nsamples. For tabular data with small feature counts and mild non-linearity,\nthese variants can produce equal test-set accuracy yet very different\nprobability calibration — a known gotcha that makes accuracy-only\nevaluation misleading.\n\nA credible CPU-scale study of this topic (a) compares at least three\ndropout variants against a no-dropout control on multiple sklearn\nclassification benchmarks, (b) reports both accuracy and calibration\nmetrics (ECE, NLL, Brier), (c) averages over ≥5 seeds with statistical\ntests, and (d) checks for ablation integrity (that different dropout\nvariants actually produce different output distributions, not just the\nsame network). The research question is: *does the choice of dropout\nvariant matter more for calibration than for accuracy on tabular data?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Variational / MC dropout produces lower Expected Calibration Error (ECE) than standard element-wise dropout on at least 2 of 3 tabular classification benchmarks, averaged over ≥5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Test-set accuracy differs by <2 absolute percentage points between dropout variants on datasets where all methods exceed 95% accuracy — i.e. accuracy is non-discriminative and calibration is needed to distinguish methods.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The no-dropout control is strictly worse than at least one dropout variant in ECE on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the choice of dropout variant (standard / spatial / variational) matter more for probability calibration than for test accuracy on tabular classification with shallow MLPs?\", \"conditions\": [{\"name\": \"no_dropout\", \"description\": \"Shallow MLP (2 hidden layers, 64 units, ReLU) trained with no dropout. Control condition.\"}, {\"name\": \"standard_dropout_p30\", \"description\": \"Same MLP with standard element-wise dropout at p=0.3 between hidden layers.\"}, {\"name\": \"standard_dropout_p50\", \"description\": \"Same MLP with standard element-wise dropout at p=0.5.\"}, {\"name\": \"spatial_dropout_p30\", \"description\": \"Same MLP with feature-blockwise dropout at p=0.3 applied to the input layer.\"}, {\"name\": \"mc_dropout_p30_T20\", \"description\": \"Same MLP with standard p=0.3 dropout left active at test time; prediction is the mean of T=20 stochastic forward passes.\"}], \"baselines\": [\"no_dropout is the no-regularization baseline\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Fraction correctly classified on held-out 20% test split, mean over 5 seeds.\"}, {\"name\": \"ece\", \"direction\": \"minimize\", \"description\": \"Expected Calibration Error (15 bins) on the test split, mean over 5 seeds.\"}, {\"name\": \"nll\", \"direction\": \"minimize\", \"description\": \"Mean per-example negative log-likelihood on the test split.\"}, {\"name\": \"brier\", \"direction\": \"minimize\", \"description\": \"Mean Brier score on the test split.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"digits\", \"source\": \"sklearn.datasets.load_digits\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 240}}", "requirements": "", "rubric": "{\"id\": \"ml01-root\", \"requirements\": \"A credible experiment studying whether dropout-variant choice affects calibration more than accuracy on tabular classification: the main dropout variants are implemented, evaluation covers multiple sklearn benchmarks with reasonable seed coverage, and the writeup ties the numeric findings to the three hypotheses in direction and magnitude.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Partial but well-motivated evidence deserves partial credit; rigid wording or name mismatches should not penalize a substantively correct experiment.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml01-code\", \"requirements\": \"The dropout conditions are implemented in a way that allows a meaningful calibration comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml01-code-variants\", \"requirements\": \"The submission implements the main dropout variants relevant to the hypotheses — typically a no-dropout baseline, a standard element-wise dropout at one or more rates, and at least one stochastic-test-time / MC-style dropout variant — as distinct code paths within a shared MLP architecture.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml01-code-mlp\", \"requirements\": \"The model is a shallow tabular MLP (a small number of hidden layers at modest width) trained with a standard supervised classification objective, appropriate for the sklearn benchmarks used.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml01-code-datasets\", \"requirements\": \"The submission loads multiple tabular sklearn datasets (e.g., from {breast_cancer, wine, digits} or comparable benchmarks) and uses a reasonable train/test split.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml01-code-mc-pass\", \"requirements\": \"The MC-dropout-style condition performs several stochastic forward passes at test time and averages the predicted probabilities, rather than collapsing to a single deterministic forward pass.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml01-exec\", \"requirements\": \"Execution produces accuracy and calibration numbers per condition that are adequate to evaluate the hypotheses.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml01-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact (e.g., results/metrics.json or stage-14/experiment_summary.json) with numeric accuracy and a calibration metric such as ECE, covering the implemented conditions on at least one dataset. Other calibration metrics (NLL, Brier) may substitute or supplement ECE.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml01-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (condition, dataset) cell with some form of dispersion reporting (std, stderr, CI, or min/max across seeds). More seeds are better, but a small-but-honest seed count with reported variance is preferable to a single deterministic run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml01-results\", \"requirements\": \"The results analysis addresses the three hypotheses with quantitative evidence and a clear narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml01-result-h1\", \"requirements\": \"The submission compares calibration (ECE or analogous metric) between an MC/variational-style dropout and a standard dropout across the evaluated datasets, and conveys whether MC-style calibration tends to be better — judge whether the evidence is consistent with H1, refutes it, or is inconclusive, based on the reported numbers.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml01-result-h2\", \"requirements\": \"The submission discusses whether accuracy differences among dropout variants are small on datasets where all methods achieve high accuracy — i.e., whether calibration is the discriminative axis rather than accuracy. Exact percentage-point thresholds are not required; a qualitative comparison grounded in the reported numbers suffices.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml01-result-h3\", \"requirements\": \"The submission compares the no-dropout baseline against at least one dropout variant on calibration and conveys whether the baseline is clearly worse, comparable, or better.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml01-result-writeup\", \"requirements\": \"The README or writeup describes the method and setup, presents the key accuracy and calibration numbers, and conveys per-hypothesis outcomes (supported / refuted / inconclusive) with appropriate caveats on seed count, dataset scope, or calibration-metric choice. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML01.yaml", "rubric_file": "tasks/ml/rubrics/ML01.json"} +{"id": "ML02", "domain": "ml", "title": "Bagging vs boosting vs stacking for noisy non-linear regression", "topic": "Evaluating ensemble methods (bagging, boosting, stacking) for regression on synthetic non-linear datasets with varying noise levels", "domains": ["machine-learning", "ensemble-methods"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "rmse", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Ensemble learning is often presented as a broadly reliable way to improve\npredictive performance, but different ensemble families can react very\ndifferently to observation noise in regression. Bagging primarily reduces\nvariance by averaging many weakly correlated models. Boosting is sequential\nand can aggressively fit residuals, which can help on structured non-linear\nsignals but may overfit high-noise targets if regularization is not tuned.\nStacking combines heterogeneous base learners through a meta-model and may\nbenefit from diversity, but it also introduces extra fitting stages that can\nbe sensitive to data size and leakage handling.\n\nA focused CPU-scale benchmark can probe these tradeoffs using synthetic and\nlightweight sklearn regression datasets where non-linearity and noise are\ncontrollable. By varying noise level explicitly in generated data, the study\ncan test whether relative ranking among bagging, boosting, and stacking is\nstable or changes materially as signal-to-noise degrades. This design also\nencourages careful evaluation with repeated random seeds and test-set metrics\nthat capture both average error and fit quality.\n\nA credible implementation should compare at least one representative for each\nfamily (e.g., RandomForestRegressor for bagging, GradientBoostingRegressor\nfor boosting, StackingRegressor with diverse base estimators for stacking),\ninclude a single-model baseline, and report RMSE-centered conclusions. Since\nthis benchmark is intended for fast autonomous execution, datasets and models\nmust remain small enough to run in minutes on one CPU core while still\nproducing clear numeric contrasts across noise regimes.\n\nThe key outcome is not reproducing a known paper result but determining when\neach ensemble strategy is most robust for noisy non-linear regression in a\nconstrained practical setting.\n\n*How does predictive robustness (RMSE) of bagging, boosting, and stacking change as regression noise increases on small non-linear benchmarks?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On the high-noise synthetic dataset (noise >= 25), bagging (RandomForestRegressor) achieves lower mean RMSE than boosting (GradientBoostingRegressor), averaged over at least 5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On at least 2 of 3 evaluated datasets, at least one ensemble method (bagging, boosting, or stacking) improves RMSE by >= 10% relative to the single DecisionTreeRegressor baseline.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"StackingRegressor attains the best (lowest) mean RMSE on at least 1 of 3 datasets while not being worst on more than 1 dataset, averaged over at least 5 seeds.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How does RMSE performance of bagging, boosting, and stacking vary with increasing noise in non-linear regression tasks under CPU-only constraints?\", \"conditions\": [{\"name\": \"decision_tree_baseline\", \"description\": \"Single DecisionTreeRegressor (max_depth tuned over a small grid) as non-ensemble baseline.\"}, {\"name\": \"bagging_random_forest\", \"description\": \"RandomForestRegressor representing bagging-style averaging across trees.\"}, {\"name\": \"boosting_gbrt\", \"description\": \"GradientBoostingRegressor representing sequential boosting on residuals.\"}, {\"name\": \"stacking_heterogeneous\", \"description\": \"StackingRegressor with base learners {kNN regressor, ridge regression, shallow random forest} and a linear meta-regressor.\"}], \"baselines\": [\"decision_tree_baseline is the single-model baseline\", \"bagging_random_forest serves as a strong classical ensemble reference\"], \"metrics\": [{\"name\": \"rmse\", \"direction\": \"minimize\", \"description\": \"Root Mean Squared Error on held-out test split, averaged over seeds.\"}, {\"name\": \"mae\", \"direction\": \"minimize\", \"description\": \"Mean Absolute Error on held-out test split, averaged over seeds.\"}, {\"name\": \"r2\", \"direction\": \"maximize\", \"description\": \"Coefficient of determination on held-out test split, averaged over seeds.\"}], \"datasets\": [{\"name\": \"friedman1_low_noise\", \"source\": \"sklearn.datasets.make_friedman1 (n_samples=1200, n_features=10, noise=1.0)\"}, {\"name\": \"friedman1_high_noise\", \"source\": \"sklearn.datasets.make_friedman1 (n_samples=1200, n_features=10, noise=30.0)\"}, {\"name\": \"diabetes\", \"source\": \"sklearn.datasets.load_diabetes\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml02-root\", \"requirements\": \"A credible experiment studying bagging vs boosting vs stacking for noisy non-linear regression: the main ensemble families are implemented, execution covers multiple regression datasets with reasonable seed coverage, and the analysis addresses H1/H2/H3 using RMSE-centered evidence.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Condition-name variants (e.g., 'RandomForest' vs 'bagging_random_forest') should not be penalized when the underlying method is clearly the intended one.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml02-code\", \"requirements\": \"The regression ensemble conditions and datasets are implemented in a way that supports a fair comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml02-code-conditions\", \"requirements\": \"The submission implements the main ensemble families relevant to the hypotheses — a bagging-style model (e.g., RandomForestRegressor), a boosting-style model (e.g., GradientBoostingRegressor), and a stacking-style model (e.g., StackingRegressor) — alongside a non-ensemble baseline such as a single DecisionTreeRegressor. Equivalent well-motivated choices are acceptable.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml02-code-datasets\", \"requirements\": \"The submission uses multiple regression datasets that allow a noise-robustness comparison — for example, friedman1 at varying noise levels and/or diabetes from sklearn — with a reasonable train/test split.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml02-code-setup\", \"requirements\": \"The pipeline uses a consistent preprocessing and evaluation setup across conditions, with controlled random-state handling and no obvious test-set leakage into training or stacking meta-features.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml02-exec\", \"requirements\": \"Execution logs the core metrics needed to evaluate the hypotheses.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml02-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact containing numeric RMSE for the implemented (dataset, condition) cells, plus at least one complementary error metric (MAE or R²). Complete coverage is preferred; partial coverage should be scored proportional to how much of the hypothesis-relevant grid is populated.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml02-exec-seeds\", \"requirements\": \"Reported dataset-condition results are aggregated over multiple random seeds with some form of dispersion reporting (std, stderr, CI, or min/max). More seeds are better, but an honest small-seed run with reported variance is preferable to a single deterministic run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml02-results\", \"requirements\": \"Results analysis addresses all three hypotheses with quantitative evidence.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml02-result-h1\", \"requirements\": \"The submission compares bagging (e.g., Random Forest) vs boosting (e.g., GBRT) on RMSE in a high-noise regression regime and conveys whether bagging is clearly better, clearly worse, or comparable — judge directionally against H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml02-result-h2\", \"requirements\": \"The submission conveys whether ensembles deliver a meaningful RMSE improvement over the single-tree baseline on most of the evaluated datasets. Exact percentage thresholds are not required; a clear comparison grounded in the reported numbers suffices.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml02-result-h3\", \"requirements\": \"The submission ranks ensemble and baseline conditions by RMSE across the evaluated datasets and discusses whether stacking is competitive (best or near-best on some datasets, not systematically worst) — judge directionally against H3.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml02-result-writeup\", \"requirements\": \"The README or writeup describes the methods and setup, presents the key RMSE/MAE/R² findings, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML02.yaml", "rubric_file": "tasks/ml/rubrics/ML02.json"} +{"id": "ML03", "domain": "ml", "title": "CPU comparison of Nelder-Mead, Powell, and CMA-ES on non-convex functions", "topic": "Comparing gradient-free optimization algorithms (Nelder-Mead, Powell, CMA-ES) for non-convex benchmark functions using only CPU computation", "domains": ["optimization", "numerical-methods"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "primary_metric", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 480, "synthesis": "Gradient-free optimization methods are widely used when derivatives are\nunavailable, unreliable, or expensive to compute. In low-to-medium\ndimensional non-convex landscapes, direct-search methods such as\nNelder-Mead and Powell are frequently used because they are easy to call\nthrough scipy.optimize. Population-based approaches such as CMA-ES can be\nmore robust to local minima and ill-conditioning, but they introduce extra\nhyperparameters and potentially higher per-iteration cost.\n\nA CPU-bounded benchmark can clarify practical trade-offs by evaluating these\nmethods under a fixed function-evaluation budget on standard synthetic\nobjective functions with known global minima. Rather than asking which method\nis universally best, the relevant question is whether one method reaches\nbetter objective values more reliably within the same budget and how much\nruntime overhead that reliability costs.\n\nA credible experiment should evaluate at least three optimization conditions\n(Nelder-Mead, Powell, CMA-ES) across multiple non-convex test functions,\nusing repeated random starts and common stopping/budget rules. Reporting only\nfinal objective value is incomplete; runtime and success-rate-to-threshold\nshould be included to capture both quality and efficiency. Statistical\nsummaries over seeds are required because single runs are high variance.\n\nThe resulting evidence should produce explicit pass/fail verdicts for each\nhypothesis: whether CMA-ES improves best-found objective value, whether\nPowell offers faster wall-clock convergence than CMA-ES at similar budgets,\nand whether Nelder-Mead underperforms on multimodal landscapes. This keeps\nthe study measurable and feasible within a short single-core runtime window.\n\n*Under equal function-evaluation budgets on CPU, which gradient-free optimizer (Nelder-Mead, Powell, CMA-ES) gives the best trade-off between final objective quality, runtime, and success rate on non-convex benchmark functions?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"CMA-ES achieves lower mean best objective value than both Nelder-Mead and Powell on at least 2 of 3 benchmark functions, averaged over >=10 random starts with the same evaluation budget.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Powell has lower median wall-clock runtime than CMA-ES on at least 2 of 3 benchmark functions while finishing within the same function-evaluation cap.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"On the multimodal Rastrigin function, Nelder-Mead attains a lower success rate (fraction of runs reaching f(x) <= 1e-2) than CMA-ES by at least 0.20 absolute.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Under equal evaluation budgets, how do Nelder-Mead, Powell, and CMA-ES compare on objective quality, runtime, and success probability for non-convex synthetic objectives?\", \"conditions\": [{\"name\": \"nelder_mead\", \"description\": \"scipy.optimize.minimize with method='Nelder-Mead', random initial point per run, maxfev budget enforced.\"}, {\"name\": \"powell\", \"description\": \"scipy.optimize.minimize with method='Powell', same initialization protocol and maxfev cap.\"}, {\"name\": \"cma_es\", \"description\": \"CMA-ES implementation (lightweight numpy/scipy variant) with population updates under the same total function-evaluation budget.\"}, {\"name\": \"random_search_baseline\", \"description\": \"Uniform random search within bounded domain using the same number of objective evaluations as other methods.\"}], \"baselines\": [\"random_search_baseline as a budget-matched non-adaptive baseline\"], \"metrics\": [{\"name\": \"primary_metric\", \"direction\": \"minimize\", \"description\": \"Mean best objective value found at termination (lower is better), aggregated over random starts.\"}, {\"name\": \"median_runtime_sec\", \"direction\": \"minimize\", \"description\": \"Median wall-clock runtime per run in seconds for each (method, function).\"}, {\"name\": \"success_rate_eps\", \"direction\": \"maximize\", \"description\": \"Fraction of runs reaching objective value <= 1e-2 by termination.\"}], \"datasets\": [{\"name\": \"rastrigin_10d\", \"source\": \"synthetic numpy implementation of Rastrigin function in 10 dimensions\"}, {\"name\": \"rosenbrock_10d\", \"source\": \"synthetic numpy implementation of Rosenbrock function in 10 dimensions\"}, {\"name\": \"ackley_10d\", \"source\": \"synthetic numpy implementation of Ackley function in 10 dimensions\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 480}}", "requirements": "", "rubric": "{\"id\": \"ml03-root\", \"requirements\": \"A credible experiment comparing Nelder-Mead, Powell, and CMA-ES on non-convex benchmark functions: the optimizers are implemented under matched evaluation budgets, execution covers multiple benchmark functions with repeated random starts, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. If the submission uses different optimizer names or slightly different benchmark functions but addresses the same scientific question, credit it accordingly.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml03-code\", \"requirements\": \"The optimization methods and benchmark setup are implemented in a way that supports a fair comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml03-code-methods\", \"requirements\": \"The submission implements distinct optimizer code paths for a local-simplex method (e.g., Nelder-Mead), a direction-set method (e.g., Powell), and an evolution-strategy method (e.g., CMA-ES or a well-motivated substitute such as a random-search baseline), rather than aliases of a single optimizer.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml03-code-functions\", \"requirements\": \"The submission defines multiple non-convex benchmark functions (e.g., Rastrigin, Rosenbrock, Ackley at a moderate dimension such as 10D) with bounded domains and deterministic objective evaluation.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml03-code-budget\", \"requirements\": \"A shared function-evaluation budget (maxfev or equivalent) is enforced across optimizers so comparisons are budget-matched.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml03-exec\", \"requirements\": \"Execution uses repeated random starts and produces comparable benchmark metrics.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml03-exec-runs\", \"requirements\": \"Execution uses multiple random starts per (optimizer, function) cell across the evaluated benchmark functions. More starts are better for reducing variance, but an honest small-repetition run with reported dispersion is preferable to a single start.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml03-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact with numeric best-objective-value (the primary optimization metric), a wall-clock runtime measure (e.g., median runtime), and a success-rate-style metric (fraction of runs reaching a threshold f(x) ≤ ε) per optimizer and function.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml03-results\", \"requirements\": \"The reported results address all three hypotheses and include a clear narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml03-result-h1\", \"requirements\": \"The submission compares mean best-objective-values across the optimizers for each function and conveys whether CMA-ES (or its stand-in) tends to outperform the local methods on the multimodal functions — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml03-result-h2\", \"requirements\": \"The submission compares runtime between Powell and CMA-ES across the evaluated functions and conveys whether Powell tends to finish meaningfully faster under the matched budget.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml03-result-h3\", \"requirements\": \"The submission compares success-rate on a hard multimodal function (e.g., Rastrigin) between a local method (e.g., Nelder-Mead) and CMA-ES and conveys whether CMA-ES has a clearly higher success rate.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml03-result-writeup\", \"requirements\": \"The README or writeup describes the benchmark setup and metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as budget size, dimensionality, CMA-ES implementation simplifications, and seed variance. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML03.yaml", "rubric_file": "tasks/ml/rubrics/ML03.json"} +{"id": "ML04", "domain": "ml", "title": "Effect of standard, min-max, and robust scaling on KNN classification", "topic": "Analyzing the effect of feature scaling methods (standard, min-max, robust) on k-nearest neighbors classification performance across different data distributions", "domains": ["machine-learning", "data-preprocessing"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 180, "synthesis": "K-nearest neighbors (KNN) is highly sensitive to feature scale because\ndistance calculations implicitly weight dimensions by their numeric ranges.\nIn practice, preprocessing decisions such as StandardScaler, MinMaxScaler,\nor RobustScaler can alter neighborhood structure enough to change\nclassification performance as much as model hyperparameters do. Despite this,\nscaling choice is often treated as a default rather than as an experimental\nfactor.\n\nThe core methodological question is not whether scaling helps versus no\nscaling (it usually does), but whether specific scaling families are better\nmatched to particular data distributions. Standard scaling assumes roughly\nGaussian-like behavior, min-max scaling compresses to bounded ranges and can\nbe strongly affected by extrema, and robust scaling centers/scales by median\nand IQR to reduce outlier influence.\n\nA CPU-friendly, credible benchmark should compare these scalers with a fixed\nKNN classifier across multiple datasets that differ in outlier prevalence and\nmarginal feature distributions, while averaging over multiple train/test\nsplits. Because KNN is lightweight on small-to-medium sklearn datasets, the\nstudy can include both predictive quality and stability metrics without\nexceeding strict runtime constraints.\n\nThe experiment should report test accuracy as the primary outcome, plus\nmacro-F1 and split-to-split variability, and include at least one outlier-\nheavy synthetic dataset to stress robust scaling behavior. The goal is to\nproduce falsifiable conclusions about when scaler choice materially changes\nKNN outcomes.\n\n*How does the choice among standard, min-max, and robust feature scaling change KNN classification performance and stability across datasets with different distribution and outlier characteristics?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"RobustScaler + KNN achieves higher mean test accuracy than StandardScaler + KNN on the outlier-heavy synthetic dataset by at least 0.03 absolute accuracy, averaged over ≥5 random splits.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Across the evaluated datasets, at least one real dataset shows an absolute test-accuracy gap of ≥0.02 between the best and worst of {StandardScaler, MinMaxScaler, RobustScaler}, indicating scaler choice materially affects KNN performance.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"No_scaling baseline is not the top-accuracy condition on at least 2 of 3 datasets when compared against the three scaling methods with the same KNN hyperparameters.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How does scaler choice (standard, min-max, robust) affect KNN classification accuracy and stability across datasets with different feature distributions and outlier profiles?\", \"conditions\": [{\"name\": \"no_scaling_knn\", \"description\": \"KNeighborsClassifier with fixed k and distance metric on raw features; control condition.\"}, {\"name\": \"standard_scaler_knn\", \"description\": \"Pipeline(StandardScaler, KNeighborsClassifier) with the same KNN hyperparameters as control.\"}, {\"name\": \"minmax_scaler_knn\", \"description\": \"Pipeline(MinMaxScaler, KNeighborsClassifier) with identical KNN settings.\"}, {\"name\": \"robust_scaler_knn\", \"description\": \"Pipeline(RobustScaler, KNeighborsClassifier) with identical KNN settings.\"}], \"baselines\": [\"no_scaling_knn is the preprocessing-free baseline\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Mean held-out accuracy over at least 5 random stratified splits per dataset.\"}, {\"name\": \"macro_f1\", \"direction\": \"maximize\", \"description\": \"Macro-averaged F1 on held-out data, averaged over splits.\"}, {\"name\": \"accuracy_std\", \"direction\": \"minimize\", \"description\": \"Standard deviation of test accuracy across random splits as a stability indicator.\"}], \"datasets\": [{\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"synthetic_outlier_classification\", \"source\": \"sklearn.datasets.make_classification with injected feature outliers on a subset of samples\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 180}}", "requirements": "", "rubric": "{\"id\": \"ml04-root\", \"requirements\": \"A credible experiment studying how feature scaling (standard, min-max, robust, or none) affects KNN classification on datasets with different distributions: scaling conditions are implemented consistently, execution covers multiple datasets and random splits, and conclusions address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. If the submission uses alternative but well-motivated scalers or datasets that test the same scientific question, credit it accordingly.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml04-code\", \"requirements\": \"The scaling conditions and KNN setup are implemented in a way that supports a fair comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml04-code-conditions\", \"requirements\": \"The submission implements the relevant scaling conditions — typically no-scaling, standard-scaler, min-max-scaler, and robust-scaler — as distinct code paths sharing the same KNN configuration.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml04-code-datasets\", \"requirements\": \"The submission uses multiple datasets including at least one real sklearn dataset and at least one synthetic/outlier-heavy dataset, with a reasonable train/test split.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml04-code-consistency\", \"requirements\": \"The implementation keeps KNN settings identical across scaling conditions and fits preprocessing only on training data within each split.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml04-exec\", \"requirements\": \"Execution produces benchmark metrics adequate to evaluate the hypotheses.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml04-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact with numeric test accuracy and an additional metric such as macro-F1 for each evaluated (condition, dataset) cell.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml04-exec-splits\", \"requirements\": \"Reported metrics are aggregated over multiple random splits or seeds per (condition, dataset) with some dispersion measure (std or CI). More splits are better, but an honest small-split run with variance reported is preferable to a single split.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml04-results\", \"requirements\": \"The analysis evaluates all three hypotheses and presents interpretable findings.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml04-result-h1\", \"requirements\": \"The submission compares robust-scaling vs standard-scaling + KNN on the outlier-heavy dataset and conveys whether robust-scaling shows a meaningful accuracy advantage. Exact percentage-point thresholds are not required.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml04-result-h2\", \"requirements\": \"The submission discusses whether scaler choice materially affects KNN accuracy on at least one real dataset — i.e., whether the best-vs-worst-scaler gap is non-trivial and of practical significance.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml04-result-h3\", \"requirements\": \"The submission compares the no-scaling baseline against the scaled conditions and conveys whether no-scaling tends to be clearly suboptimal across the evaluated datasets.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml04-result-writeup\", \"requirements\": \"The README or writeup describes setup and datasets, reports the key metric values per condition, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as dataset scope, split count, or KNN-hyperparameter sensitivity. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML04.yaml", "rubric_file": "tasks/ml/rubrics/ML04.json"} +{"id": "ML05", "domain": "ml", "title": "Dimensionality reduction methods for preserving cluster structure in synthetic high-dimensional data", "topic": "Investigating dimensionality reduction techniques (PCA, t-SNE, UMAP) for preserving cluster structure in high-dimensional synthetic datasets", "domains": ["machine-learning", "unsupervised-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "silhouette_score", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 600, "synthesis": "Dimensionality reduction is frequently used before visualization and\nclustering, but different methods optimize different objectives and can\ndistort neighborhood and global geometry in distinct ways. PCA is linear and\nemphasizes variance preservation, while t-SNE and UMAP are nonlinear manifold\nmethods designed to preserve local structure. In practice, users often infer\ncluster quality from 2D embeddings without checking whether separability in\nthe embedding reflects true high-dimensional structure.\n\nA CPU-scale investigation can probe this mismatch by generating synthetic\ndatasets where ground-truth cluster labels are known and controllable. By\nvarying cluster overlap, anisotropy, and manifold geometry (e.g., Gaussian\nblobs, anisotropic transforms, and noisy moons embedded in high dimensions),\none can evaluate whether each reducer preserves cluster structure under\ndifferent regimes instead of relying on a single toy example.\n\nA credible experiment should compare PCA, t-SNE, and UMAP under a common\npipeline: reduce to 2 dimensions, then score structure using metrics such as\nsilhouette score and adjusted Rand index after a fixed clustering backend\n(e.g., KMeans with true k). Runtime should also be tracked because nonlinear\nmethods may yield better structure at substantially higher computational cost,\nwhich matters in constrained environments.\n\nThe goal is not to claim a universally best reducer, but to quantify tradeoffs\nbetween structure preservation and efficiency in settings where intrinsic\ngeometry differs. This produces actionable guidance for method choice based on\ndata regime rather than defaults.\n\n*Which dimensionality reduction method (PCA, t-SNE, or UMAP) best preserves cluster structure across distinct synthetic high-dimensional regimes when evaluated by silhouette score, ARI, and runtime?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On nonlinear manifold data (noisy two-moons embedded into 50D), at least one nonlinear reducer (t-SNE or UMAP) achieves silhouette_score at least 0.10 higher than PCA after 2D reduction and KMeans clustering, averaged over >=5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On linearly separable Gaussian blobs in 50D, PCA achieves silhouette_score within 0.05 of the best nonlinear method (t-SNE or UMAP), averaged over >=5 seeds.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"PCA has the lowest mean wall-clock reduction time and is at least 3x faster than both t-SNE and UMAP on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which reducer best balances cluster-structure preservation and compute cost across linear and nonlinear synthetic high-dimensional datasets?\", \"conditions\": [{\"name\": \"pca_2d\", \"description\": \"PCA with n_components=2 using sklearn.decomposition.PCA.\"}, {\"name\": \"tsne_2d\", \"description\": \"t-SNE with n_components=2, perplexity in [20, 40], learning_rate='auto', init='pca'.\"}, {\"name\": \"umap_2d\", \"description\": \"UMAP-like reducer: use umap.UMAP(n_components=2, n_neighbors in [15, 30], min_dist in [0.0, 0.3]) if available; if unavailable, use sklearn.manifold.SpectralEmbedding(n_components=2) as a documented fallback proxy.\"}, {\"name\": \"identity_no_reduction\", \"description\": \"Baseline that applies KMeans directly in the original high-dimensional space (no dimensionality reduction).\"}], \"baselines\": [\"identity_no_reduction as no-DR clustering baseline\", \"pca_2d as linear DR baseline\"], \"metrics\": [{\"name\": \"silhouette_score\", \"direction\": \"maximize\", \"description\": \"Silhouette score computed on the 2D embedding (or original space for identity baseline) using predicted KMeans labels; primary metric.\"}, {\"name\": \"ari\", \"direction\": \"maximize\", \"description\": \"Adjusted Rand Index between KMeans labels and known synthetic ground-truth labels.\"}, {\"name\": \"fit_transform_time_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock time for dimensionality reduction fit_transform only (seconds).\"}], \"datasets\": [{\"name\": \"gaussian_blobs_50d\", \"source\": \"sklearn.datasets.make_blobs with 4 centers, n_features=50, cluster_std=1.2\"}, {\"name\": \"anisotropic_blobs_50d\", \"source\": \"make_blobs in 10D expanded/projection to 50D then linear anisotropic transform\"}, {\"name\": \"embedded_moons_50d\", \"source\": \"sklearn.datasets.make_moons(noise=0.08) embedded into 50D by random linear projection plus Gaussian noise\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 600}}", "requirements": "", "rubric": "{\"id\": \"ml05-root\", \"requirements\": \"A credible experiment studying how linear (PCA) vs non-linear (t-SNE / UMAP-like) dimensionality reduction preserves cluster structure on synthetic high-dimensional datasets: methods share a consistent evaluation pipeline, execution covers multiple datasets with repeated seeds, and conclusions address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. UMAP may be unavailable in the environment — a documented fallback (e.g., Isomap, LLE) that tests the same scientific question should be credited rather than penalized.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml05-code\", \"requirements\": \"The dimensionality-reduction and clustering conditions are implemented in a way that supports a fair comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml05-code-conditions\", \"requirements\": \"The submission implements the main reduction conditions relevant to the hypotheses — a linear method (PCA), at least one nonlinear method (t-SNE, UMAP, or a documented substitute such as Isomap), and preferably a no-reduction identity baseline — as distinct code paths.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml05-code-data\", \"requirements\": \"The submission generates multiple synthetic high-dimensional datasets that stress different geometries (e.g., isotropic blobs, anisotropic blobs, embedded non-linear manifolds such as moons) with reproducible seeds and retained ground-truth labels.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml05-code-pipeline\", \"requirements\": \"A consistent post-reduction clustering backend (e.g., KMeans with k matching the true cluster count) and a shared metric-computation pipeline are applied uniformly across conditions.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml05-exec\", \"requirements\": \"Execution outputs quantitative cluster-quality and runtime metrics.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml05-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact with numeric cluster-quality scores (silhouette, ARI, or equivalents) and a wall-clock reduction-time measure per (dataset, condition) cell.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml05-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (dataset, condition) with some dispersion measure. More seeds are better, but an honest small-seed run with variance reported is preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml05-results\", \"requirements\": \"Results are analyzed against the hypotheses with interpretable evidence.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml05-result-h1\", \"requirements\": \"The submission compares nonlinear methods vs PCA on the nonlinear-manifold dataset using a cluster-quality metric (silhouette or ARI) and conveys whether the nonlinear methods produce meaningfully better cluster separation — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml05-result-h2\", \"requirements\": \"The submission discusses PCA vs the best nonlinear method on the linearly-separable blobs dataset and conveys whether PCA is competitive (comparable quality) in that regime.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml05-result-h3\", \"requirements\": \"The submission reports runtime rankings across methods and conveys whether PCA is markedly faster than the nonlinear methods on most datasets.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml05-result-writeup\", \"requirements\": \"The README or writeup describes setup, reports the key metric numbers, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as synthetic-only scope, hyperparameter sensitivity, or substitutions for unavailable methods. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML05.yaml", "rubric_file": "tasks/ml/rubrics/ML05.json"} +{"id": "ML06", "domain": "ml", "title": "Adaptive learning-rate schedules for logistic-regression convergence on binary classification", "topic": "Comparing adaptive learning rate schedules (cosine annealing, step decay, exponential decay, warm restarts) for logistic regression convergence on binary classification", "domains": ["optimization", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "convergence_epochs", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Logistic regression is often treated as a solved baseline, yet its practical\ntraining behavior still depends strongly on optimization details. In\nfirst-order methods, the learning-rate schedule can dominate time-to-quality:\na constant step size may converge slowly or oscillate, while adaptive\nschedules can accelerate early progress or stabilize late optimization.\nBecause logistic regression has a smooth convex objective, it offers a clean\nsetting to compare schedules without confounds from deep-network\nnon-convexity.\n\nThis topic studies four schedule families in a unified mini-batch gradient\ndescent implementation: step decay, exponential decay, cosine annealing, and\ncosine warm restarts. The focus is not raw final accuracy alone, but\noptimization efficiency measured by epochs needed to reach a predefined\nvalidation-loss threshold, plus robustness across datasets and random seeds.\nA fixed-learning-rate baseline is required so any claimed speedup is\nattributable to schedule design.\n\nA credible CPU-scale experiment should run on multiple binary classification\ndatasets from sklearn, with feature standardization, identical model\nparameterization, and synchronized stopping criteria. Reporting should include\nconvergence epochs, final validation log loss, and test ROC-AUC so that faster\nconvergence is not achieved at the expense of generalization. Since warm\nrestarts can re-increase the learning rate, trajectory logging is important to\nverify that restart behavior is actually implemented.\n\nThe key decision criterion is whether adaptive schedules reduce optimization\neffort in a consistent, measurable way. The study should conclude with clear\nper-hypothesis verdicts and practical limitations (dataset size, threshold\nchoice, and sensitivity to initial learning rate).\n\n*Do adaptive learning-rate schedules (especially cosine variants) reduce logistic-regression convergence epochs versus fixed/monotone decay schedules while maintaining comparable binary-classification performance?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Cosine annealing or cosine warm restarts achieves at least 20% lower mean convergence_epochs than fixed learning rate on at least 2 of 3 evaluated binary datasets, averaged over >=5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Among adaptive schedules (step decay, exponential decay, cosine annealing, warm restarts), the best schedule's final validation log loss is no worse than 0.01 absolute compared with the worst schedule on each dataset (i.e., speed differences exceed quality differences).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Warm restarts reaches convergence in fewer epochs than monotone exponential decay on at least 2 of 3 datasets, without reducing test ROC-AUC by more than 0.01 absolute on those datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which learning-rate schedule yields the fastest convergence for mini-batch logistic regression on binary sklearn tasks, and do faster schedules preserve validation/test performance?\", \"conditions\": [{\"name\": \"fixed_lr\", \"description\": \"Mini-batch logistic regression trained with constant learning rate (baseline).\"}, {\"name\": \"step_decay\", \"description\": \"Learning rate multiplied by gamma at fixed epoch milestones (e.g., every 20 epochs).\"}, {\"name\": \"exponential_decay\", \"description\": \"Learning rate updated each epoch as lr_t = lr0 * exp(-k * t).\"}, {\"name\": \"cosine_annealing\", \"description\": \"Learning rate follows cosine decay from lr0 to lr_min over total epochs without restarts.\"}, {\"name\": \"cosine_warm_restarts\", \"description\": \"Cosine schedule with periodic restarts (SGDR-style) resetting to higher learning rate at restart boundaries.\"}], \"baselines\": [\"fixed_lr is the primary optimization baseline\", \"exponential_decay is a monotone adaptive baseline for comparison to warm restarts\"], \"metrics\": [{\"name\": \"convergence_epochs\", \"direction\": \"minimize\", \"description\": \"Epoch index at which validation log loss first drops below a preset threshold (or max_epochs if never reached), averaged over seeds.\"}, {\"name\": \"val_log_loss\", \"direction\": \"minimize\", \"description\": \"Final validation logistic loss after training, mean over seeds.\"}, {\"name\": \"test_roc_auc\", \"direction\": \"maximize\", \"description\": \"ROC-AUC on held-out test set, mean over seeds.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"synthetic_binary_medium\", \"source\": \"sklearn.datasets.make_classification (n_samples=2000, n_features=30, n_informative=12, class_sep=1.0)\"}, {\"name\": \"synthetic_binary_noisy\", \"source\": \"sklearn.datasets.make_classification (n_samples=2500, n_features=40, n_informative=10, flip_y=0.08, class_sep=0.7)\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml06-root\", \"requirements\": \"A credible experiment studying whether adaptive learning-rate schedules improve logistic-regression convergence on binary classification: schedule conditions are implemented as distinct update rules, runs are executed on multiple datasets with multiple seeds, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Equivalent schedules (e.g., a different decay parameterization) should be credited when the underlying mechanism is clearly the intended one.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml06-code\", \"requirements\": \"The logistic-regression training framework and learning-rate schedule variants are implemented in a way that allows a meaningful convergence comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml06-code-schedules\", \"requirements\": \"The submission implements a fixed-learning-rate baseline and multiple adaptive schedules (e.g., step-decay, exponential-decay, cosine-annealing, cosine-warm-restarts, or comparable alternatives) as distinct update rules, not duplicated code paths.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml06-code-model\", \"requirements\": \"A binary logistic-regression objective (sigmoid + log-loss) is trained via iterative gradient-based optimization with per-epoch control, enabling convergence-epoch tracking.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml06-code-data\", \"requirements\": \"The submission uses multiple datasets (including at least one sklearn built-in or synthetic classification set), performs train/validation/test splitting, and applies feature scaling consistently.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml06-exec\", \"requirements\": \"Execution produces convergence and quality metrics with reasonable seed coverage.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml06-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact with numeric convergence-epochs (or equivalent convergence measure) and validation log-loss for each implemented (dataset, condition) cell.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml06-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (dataset, condition) with some dispersion measure. More seeds are better, but honest small-seed runs with variance reported are preferable to single deterministic runs.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml06-exec-auc\", \"requirements\": \"Execution also reports a final predictive-quality metric (e.g., test ROC-AUC) for each condition on at least one dataset, so readers can check that convergence-speed gains do not collapse classification quality.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml06-results\", \"requirements\": \"The analysis evaluates all three hypotheses and discusses the speed-vs-quality trade-off.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml06-result-h1\", \"requirements\": \"The submission compares cosine-style schedules against the fixed-rate baseline on convergence-epochs across the evaluated datasets and conveys whether cosine schedules converge meaningfully faster — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml06-result-h2\", \"requirements\": \"The submission reports the spread of final validation log-loss among the adaptive schedules and conveys whether the best-vs-worst gap is small (i.e., final-quality differences are minor once converged).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml06-result-h3\", \"requirements\": \"The submission compares cosine-warm-restarts against exponential-decay (or equivalent schedules) on convergence speed and final ROC-AUC and conveys the qualitative outcome for H3.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml06-result-writeup\", \"requirements\": \"The README or writeup describes setup and key metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as convergence-threshold choice, seed count, or dataset scope. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML06.yaml", "rubric_file": "tasks/ml/rubrics/ML06.json"} +{"id": "ML07", "domain": "ml", "title": "Text feature extraction trade-offs: TF-IDF vs count vs hashing with NB/SVM", "topic": "Evaluating text feature extraction methods (TF-IDF, count vectors, hashing) combined with Naive Bayes and SVM for document classification on 20newsgroups", "domains": ["natural-language-processing", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "f1_score", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 600, "synthesis": "Sparse bag-of-words pipelines remain strong baselines for document\nclassification, especially under strict CPU and latency constraints.\nThree common feature extractors — CountVectorizer, TfidfVectorizer, and\nHashingVectorizer — define different bias/variance and efficiency trade-offs.\nCount vectors preserve raw frequency signals useful for generative models;\nTF-IDF often improves linear margin classifiers by downweighting common\ntokens; hashing avoids vocabulary construction and can reduce memory and\nfit-time at the cost of collisions.\n\nClassifier choice interacts strongly with the representation. Multinomial\nNaive Bayes (MNB) is typically paired with nonnegative count-like features,\nwhile linear SVM variants often benefit from TF-IDF normalization. In\npractical workflows, teams frequently need to choose between slightly better\nmacro-F1 and much faster runtime. This makes a pure \"best score\" benchmark\nincomplete unless paired with timing and robustness checks.\n\nA credible CPU-scale study should compare multiple extractor+classifier\ncombinations on at least two document datasets available directly through\nsklearn (or trivially synthesized text), use fixed train/test splits with\nrepeated seeds where relevant, and report both predictive quality and\nefficiency metrics. The experiment should explicitly test whether TF-IDF +\nlinear SVM provides the strongest macro-F1 while hashing offers meaningful\nspeed advantages with limited degradation.\n\n*Does TF-IDF with linear SVM deliver the best macro-F1 on sklearn text benchmarks, and is HashingVectorizer a worthwhile speed/accuracy trade-off versus vocabulary-based features?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"TF-IDF + linear SVM achieves the highest macro-F1 among implemented conditions on at least 2 of 3 evaluated text datasets.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"HashingVectorizer-based pipelines reduce vectorization+fit wall-clock time by at least 20% versus the corresponding TF-IDF pipeline on at least 2 of 3 datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Multinomial Naive Bayes with count vectors attains macro-F1 within 0.05 absolute of TF-IDF + linear SVM on at least 1 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Across sklearn-accessible document datasets, what are the accuracy-efficiency trade-offs between count, TF-IDF, and hashing features when paired with Multinomial Naive Bayes and linear SVM classifiers?\", \"conditions\": [{\"name\": \"count_mnb\", \"description\": \"CountVectorizer (word unigrams, min_df=2) + MultinomialNB(alpha=1.0).\"}, {\"name\": \"tfidf_mnb\", \"description\": \"TfidfVectorizer (word unigrams, min_df=2, norm=l2) + MultinomialNB(alpha=1.0).\"}, {\"name\": \"tfidf_linear_svm\", \"description\": \"TfidfVectorizer (word unigrams, min_df=2, norm=l2) + LinearSVC(C=1.0).\"}, {\"name\": \"hashing_linear_svm\", \"description\": \"HashingVectorizer (word unigrams, n_features=2^18, alternate_sign=False) + LinearSVC(C=1.0).\"}], \"baselines\": [\"count_mnb as the classic sparse-text baseline\", \"tfidf_mnb as a same-classifier feature-ablation baseline\"], \"metrics\": [{\"name\": \"macro_f1\", \"direction\": \"maximize\", \"description\": \"Macro-averaged F1 score on held-out test split.\"}, {\"name\": \"accuracy\", \"direction\": \"maximize\", \"description\": \"Overall test accuracy for comparability with prior text benchmarks.\"}, {\"name\": \"fit_predict_time_sec\", \"direction\": \"minimize\", \"description\": \"End-to-end wall-clock time for vectorization, model fit, and test prediction.\"}], \"datasets\": [{\"name\": \"20newsgroups_4class\", \"source\": \"sklearn.datasets.fetch_20newsgroups with 4 categories (subset=train/test, remove=(\\\"headers\\\",\\\"footers\\\",\\\"quotes\\\"))\"}, {\"name\": \"20newsgroups_8class\", \"source\": \"sklearn.datasets.fetch_20newsgroups with 8 categories (subset=train/test, remove=(\\\"headers\\\",\\\"footers\\\",\\\"quotes\\\"))\"}, {\"name\": \"synthetic_topic_docs\", \"source\": \"Trivially synthesized corpus via templated sentence generation for 4 classes, 2000 documents total\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 600}}", "requirements": "", "rubric": "{\"id\": \"ml07-root\", \"requirements\": \"A credible experiment studying text-feature-extraction trade-offs (count, TF-IDF, hashing) with Naive Bayes and linear SVM for document classification: multiple vectorizer+classifier pipelines are implemented, runs execute on multiple datasets, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Equivalent vectorizer/classifier substitutes (e.g., SGDClassifier in place of LinearSVC) should be credited when the scientific question is preserved.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml07-code\", \"requirements\": \"Feature extractors and classifiers are implemented as distinct pipeline code paths enabling fair comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml07-code-conditions\", \"requirements\": \"The submission implements multiple text-classification pipelines combining a vectorizer (count, TF-IDF, or hashing) with a classifier (Multinomial Naive Bayes or LinearSVC-style linear model) as distinct code paths rather than a single reused model without feature changes.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml07-code-datasets\", \"requirements\": \"The submission uses multiple document datasets (including at least one 20newsgroups subset or comparable) with explicit train/test usage and preprocessing applied consistently across conditions.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml07-code-fairness\", \"requirements\": \"The experiment keeps comparable tokenization / n-gram choices across vectorizers where applicable and uses reasonable, shared hyperparameters (e.g., C for LinearSVC, alpha for MNB) so condition comparisons are fair.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml07-exec\", \"requirements\": \"Execution logs predictive-quality and efficiency metrics per condition.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml07-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact with numeric macro-F1 (or equivalent) and a wall-clock fit+predict time measure for each implemented (condition, dataset) cell.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml07-exec-repeats\", \"requirements\": \"Execution repeats evaluation where randomness exists (e.g., synthetic-data generation or classifier random_state) across multiple seeds and reports dispersion for macro-F1. Deterministic pipelines may report a single run if no stochasticity is involved.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml07-results\", \"requirements\": \"Results directly address H1/H2/H3 with an interpretable narrative of accuracy-efficiency trade-offs.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml07-result-h1\", \"requirements\": \"The submission ranks conditions by macro-F1 per dataset and conveys whether TF-IDF + linear SVM (or equivalent) tends to be the top pipeline — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml07-result-h2\", \"requirements\": \"The submission compares the runtime of hashing-vectorizer-based pipelines vs TF-IDF-based ones and conveys whether hashing yields a meaningful runtime reduction.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml07-result-h3\", \"requirements\": \"The submission compares count + Multinomial NB against TF-IDF + linear SVM on macro-F1 and conveys whether count-MNB is approximately competitive on at least one dataset — judge directionally against H3.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml07-result-writeup\", \"requirements\": \"The README or writeup describes methods and datasets, reports the key metric numbers, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as dataset scope, preprocessing choices, timing noise, and hyperparameter coverage. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML07.yaml", "rubric_file": "tasks/ml/rubrics/ML07.json"} +{"id": "ML08", "domain": "ml", "title": "Impact of imbalance-handling strategies on sklearn binary classifiers", "topic": "Analyzing the impact of class imbalance handling strategies (SMOTE, random oversampling, class weights, undersampling) on binary classification with scikit-learn", "domains": ["machine-learning", "imbalanced-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "balanced_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 360, "synthesis": "Class imbalance is one of the most common practical failure modes in binary\nclassification: a model can achieve high raw accuracy by predicting the\nmajority class while missing the minority class almost entirely. In\nscikit-learn workflows, practitioners often choose among simple data-level\nresampling (random oversampling, random undersampling), algorithm-level\nweighting (class_weight=\"balanced\"), or synthetic methods like SMOTE.\nHowever, these strategies trade off minority recall, precision, and overall\ndiscrimination differently, and their behavior varies with dataset geometry.\n\nA compact CPU-friendly study can still be rigorous if it compares multiple\nimbalance-handling strategies under the same base learner and preprocessing\npipeline, evaluates with metrics appropriate for skewed labels (balanced\naccuracy, F1, PR-AUC), and averages over several random seeds. The focus is\nnot on maximizing one benchmark score but on understanding whether methods\nthat improve minority detection do so consistently across datasets, and at\nwhat precision cost.\n\nTo keep implementation feasible in a short autonomous run, the experiment can\nuse sklearn-native datasets and synthetic imbalance generated from a standard\nsource dataset, with a shared train/test protocol and fixed model family\n(e.g., logistic regression). SMOTE can be implemented in a lightweight way\nusing sklearn.neighbors to interpolate minority-neighbor pairs, avoiding any\nexternal dependency.\n\nThe key analytical goal is to test whether weighted learning or synthetic\noversampling provides the most reliable gain in balanced accuracy over a\nno-treatment baseline, and whether naive random oversampling tends to hurt\nprecision-oriented metrics relative to class weighting.\n\n*Which imbalance-handling strategy (SMOTE, random oversampling, class weights, undersampling) gives the most consistent balanced-accuracy improvement for binary sklearn classification under a fixed model and protocol?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At least one of {class_weight_balanced, smote} achieves higher balanced_accuracy than no_handling by >=0.03 absolute on at least 2 of 3 datasets (mean over >=5 seeds).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Random undersampling yields the highest minority recall among compared strategies on at least 2 of 3 datasets, but does not achieve the best PR-AUC on those same datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Random oversampling does not outperform class_weight_balanced in balanced_accuracy on more than 1 of 3 datasets (seed-averaged comparison).\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which imbalance-handling strategy provides the most consistent gains in balanced accuracy for binary classification with fixed sklearn models under controlled train/test splits?\", \"conditions\": [{\"name\": \"no_handling\", \"description\": \"Baseline logistic regression trained on imbalanced data with no resampling and default class weights.\"}, {\"name\": \"class_weight_balanced\", \"description\": \"Same logistic regression with class_weight='balanced'.\"}, {\"name\": \"random_oversampling\", \"description\": \"Training-set minority class randomly oversampled with replacement to match majority count.\"}, {\"name\": \"random_undersampling\", \"description\": \"Training-set majority class randomly undersampled without replacement to match minority count.\"}, {\"name\": \"smote_k5\", \"description\": \"Training-set minority class augmented with synthetic samples via kNN interpolation (k=5) until class balance is reached.\"}], \"baselines\": [\"no_handling is the primary baseline\", \"class_weight_balanced serves as an algorithm-level baseline against data-level resampling\"], \"metrics\": [{\"name\": \"balanced_accuracy\", \"direction\": \"maximize\", \"description\": \"Mean of sensitivity and specificity on held-out test split, averaged over seeds.\"}, {\"name\": \"minority_recall\", \"direction\": \"maximize\", \"description\": \"Recall for the positive/minority class on test data.\"}, {\"name\": \"average_precision\", \"direction\": \"maximize\", \"description\": \"Area under precision-recall curve (PR-AUC / AP) on test data.\"}, {\"name\": \"f1\", \"direction\": \"maximize\", \"description\": \"Binary F1 score for the minority class at default threshold 0.5.\"}], \"datasets\": [{\"name\": \"breast_cancer_imbalanced\", \"source\": \"sklearn.datasets.load_breast_cancer with induced imbalance by downsampling positive class in training folds\"}, {\"name\": \"make_classification_90_10\", \"source\": \"sklearn.datasets.make_classification(n_samples=4000, n_features=20, weights=[0.9,0.1], random_state=seed)\"}, {\"name\": \"make_classification_95_05\", \"source\": \"sklearn.datasets.make_classification(n_samples=5000, n_features=25, weights=[0.95,0.05], class_sep=1.0, random_state=seed)\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 360}}", "requirements": "", "rubric": "{\"id\": \"ml08-root\", \"requirements\": \"A credible experiment studying imbalance-handling strategies (e.g., class weights, SMOTE, random over-/under-sampling) for binary classification: methods are implemented as distinct conditions, execution covers multiple datasets with multiple seeds, and results address H1/H2/H3 using balanced-accuracy-centered analysis.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. If SMOTE is implemented via a simplified interpolation recipe rather than the imblearn package, credit the scientific intent; alternative imbalance strategies that test the same question should also be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml08-code\", \"requirements\": \"The imbalance-handling conditions are implemented in a way that supports a fair comparison under a shared classifier.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml08-code-conditions\", \"requirements\": \"The submission implements multiple imbalance-handling strategies — typically including a no-handling baseline, class-weight balancing, an oversampling variant (random or SMOTE-like), and an undersampling variant — as distinct code paths under a common binary classifier.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml08-code-smote\", \"requirements\": \"If a SMOTE-style condition is claimed, synthetic minority samples are generated by interpolation between minority neighbors (not mere duplication) and applied only to training data. A simplified SMOTE-style implementation is acceptable if the intent is clear.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml08-code-data\", \"requirements\": \"The submission uses multiple datasets (loaded or synthesized via sklearn utilities) and enforces an imbalanced binary class distribution in training data.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml08-exec\", \"requirements\": \"Execution reports imbalance-aware metrics per condition with reasonable seed coverage.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml08-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact with numeric balanced-accuracy and at least one of {minority recall, average precision, F1} per implemented condition on at least one dataset.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml08-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (dataset, condition) with some dispersion measure. More seeds are better, but honest small-seed runs with variance reported are preferable to single deterministic runs.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml08-results\", \"requirements\": \"The analysis addresses H1/H2/H3 directionally with a clear narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml08-result-h1\", \"requirements\": \"The submission compares no-handling against class-weighting and/or SMOTE-style oversampling on balanced-accuracy and conveys whether imbalance handling yields a meaningful improvement — judge directionally against H1.\", \"weight\": 25.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml08-result-h2h3\", \"requirements\": \"The submission reports the minority-recall / average-precision comparisons needed for H2 and the random-oversampling vs class-weighting comparison needed for H3, conveying qualitative outcomes for both.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml08-result-writeup\", \"requirements\": \"The README or writeup describes setup, reports per-dataset metric outcomes, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as synthetic-dataset realism, threshold dependence, seed count, or SMOTE simplifications. No strict word-count requirement.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 8, "manifest_file": "tasks/ml/manifests/ML08.yaml", "rubric_file": "tasks/ml/rubrics/ML08.json"} +{"id": "ML09", "domain": "ml", "title": "Bayesian optimization vs grid vs random search for random-forest tuning", "topic": "Comparing Bayesian optimization vs grid search vs random search for hyperparameter tuning of random forests on UCI benchmark datasets", "domains": ["automl", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "best_cv_score", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 780, "synthesis": "Hyperparameter tuning often dominates practical AutoML performance, yet in\nCPU-limited settings the search strategy itself can matter as much as the\nmodel family. For random forests, common tunables (number of trees,\nmax_depth, min_samples_split, min_samples_leaf, max_features) define a mixed\ndiscrete-continuous space where exhaustive grids can be wasteful,\nrandom search can be surprisingly strong, and Bayesian optimization can use\nsurrogate modeling to focus evaluations on promising regions.\n\nA concise benchmark can test this tradeoff by fixing a single model class\n(RandomForestClassifier), fixed CV protocol, and comparable evaluation budget\nacross search methods. Grid search should use a compact but principled grid;\nrandom search should sample from the same parameter ranges; Bayesian\noptimization can be implemented with sklearn's Gaussian-process based\noptimizer (e.g., BayesSearchCV if available is not allowed here, so a custom\nlightweight GP + acquisition loop or scipy/sklearn surrogate approach is\nexpected). The key is fairness: equal or near-equal numbers of objective\nevaluations and identical data splits.\n\nSince this benchmark must run in under 15 minutes on one CPU core,\ndatasets should be small-to-medium sklearn/UCI-style classification tasks\nalready available in sklearn (e.g., wine, breast_cancer, digits). The\nanalysis should report best cross-validation score, held-out test accuracy,\nand search efficiency (score gain per evaluation or wall-clock).\nMulti-seed repeats are desirable to reduce noise, but can be kept modest to\nremain within runtime limits.\n\nThe experiment should answer whether Bayesian optimization delivers better\nbest-found configurations under tight evaluation budgets, or whether simpler\nbaselines (especially random search) are effectively equivalent for these\ntabular tasks. Interpretation should explicitly separate optimization quality\nfrom compute cost.\n\n*Under a fixed small evaluation budget, does Bayesian optimization find better random-forest hyperparameters than grid and random search on sklearn UCI-style classification benchmarks?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"With an equal budget of 20 hyperparameter evaluations per dataset, Bayesian optimization achieves higher mean best_cv_score than random search on at least 2 of 3 datasets.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Random search matches or exceeds grid search in best_cv_score on at least 2 of 3 datasets under the same maximum number of evaluated configurations.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The mean held-out test accuracy of the best Bayesian-tuned model is at least 0.5 percentage points higher than the best grid-tuned model on at least 1 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Under a fixed evaluation budget, which tuning strategy (Bayesian optimization, grid search, random search) yields the best random-forest cross-validation and test performance on sklearn UCI-style datasets?\", \"conditions\": [{\"name\": \"grid_search_budget20\", \"description\": \"Grid search over a compact predefined grid for RandomForestClassifier, capped at 20 evaluated configurations per dataset.\"}, {\"name\": \"random_search_budget20\", \"description\": \"Random search over matched hyperparameter ranges with 20 sampled configurations per dataset.\"}, {\"name\": \"bayes_opt_budget20\", \"description\": \"Bayesian optimization using a Gaussian-process surrogate and acquisition-guided proposals for 20 total evaluations per dataset.\"}, {\"name\": \"default_rf\", \"description\": \"Untuned RandomForestClassifier with sklearn default hyperparameters as a reference baseline.\"}], \"baselines\": [\"default_rf as no-tuning baseline\", \"grid_search_budget20 as classical tuning baseline\"], \"metrics\": [{\"name\": \"best_cv_score\", \"direction\": \"maximize\", \"description\": \"Best mean 3-fold cross-validation accuracy discovered by each search method.\"}, {\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Accuracy on a held-out test split using the best hyperparameters found by each method.\"}, {\"name\": \"search_time_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock time spent in hyperparameter search per method and dataset.\"}], \"datasets\": [{\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"digits\", \"source\": \"sklearn.datasets.load_digits\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 780}}", "requirements": "", "rubric": "{\"id\": \"ml09-root\", \"requirements\": \"A credible experiment comparing Bayesian optimization, grid search, and random search for RandomForest hyperparameter tuning: methods are implemented with comparable budgets, executed on multiple datasets, and results are mapped directionally to H1/H2/H3.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. If a well-motivated substitute (e.g., Hyperopt, Optuna, or a custom TPE-like surrogate) is used in place of a canonical Bayesian library, credit the scientific intent.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml09-code\", \"requirements\": \"The tuning strategies and shared evaluation setup are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml09-code-strategies\", \"requirements\": \"The submission implements distinct code paths for grid search, random search, and a Bayesian-style search for RandomForestClassifier (or equivalent) rather than reusing identical sampled configurations across methods.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml09-code-space\", \"requirements\": \"A shared hyperparameter space is defined (covering several meaningful RF hyperparameters such as n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features) so search methods are fairly comparable.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}, {\"id\": \"ml09-code-data\", \"requirements\": \"The code loads multiple sklearn classification datasets (e.g., wine, breast_cancer, digits, or comparable) and uses a consistent train/test split plus CV protocol across search methods.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml09-exec\", \"requirements\": \"The benchmark is executed and logs optimization quality and efficiency metrics.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml09-exec-metrics\", \"requirements\": \"Execution outputs numeric best_cv_score and test_accuracy (or equivalents) for each implemented method on at least one dataset in a machine-readable metrics artifact.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml09-exec-budget\", \"requirements\": \"Execution evidences budget control: non-default search methods evaluate a roughly comparable number of configurations, so quality comparisons are not confounded by wildly different evaluation counts.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml09-exec-time\", \"requirements\": \"Execution reports a wall-clock timing measure (e.g., search_time_sec) per method and dataset.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml09-results\", \"requirements\": \"Results address H1/H2/H3 directionally and convey interpretable tradeoffs.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml09-result-h1\", \"requirements\": \"The submission compares Bayesian optimization vs random search on best_cv_score across the evaluated datasets and conveys whether Bayesian search is meaningfully better on most — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml09-result-h2\", \"requirements\": \"The submission compares random search vs grid search on best_cv_score and conveys whether random is at least competitive with grid on most datasets (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml09-result-h3\", \"requirements\": \"The submission checks test_accuracy differences between the best Bayesian-tuned and best grid-tuned models and conveys whether any practical gain (H3) is observed on at least one dataset.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml09-result-writeup\", \"requirements\": \"The README or writeup reports per-dataset metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations such as small dataset set, budget tightness, surrogate instability, or CV variance. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML09.yaml", "rubric_file": "tasks/ml/rubrics/ML09.json"} +{"id": "ML10", "domain": "ml", "title": "Cross-validation strategy reliability for small-sample model selection", "topic": "Investigating the effectiveness of different cross-validation strategies (k-fold, stratified, repeated, leave-one-out) for model selection with small sample sizes", "domains": ["machine-learning", "model-selection"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "estimation_bias", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 600, "synthesis": "With small datasets, model selection can become highly sensitive to the\nvalidation protocol. Practitioners often choose among k-fold,\nstratified k-fold, repeated k-fold, and leave-one-out cross-validation\n(LOOCV), but these strategies trade off bias, variance, and compute in\ndifferent ways. A method that appears best under one CV scheme may not be\nbest under another, especially when class balance is imperfect and sample\ncounts are low.\n\nThis topic studies CV strategy as the independent variable while keeping\ncandidate models fixed and lightweight. The key quantity is estimation bias:\nthe gap between CV-estimated score used for model selection and the realized\ntest score of the selected model on a held-out test set. In small-sample\nsettings, minimizing this gap is often more important than maximizing raw CV\nscore, because over-optimistic selection can produce brittle deployments.\n\nA credible CPU-scale experiment should evaluate multiple sklearn datasets\nreduced to small training sizes, run several random seeds, and compare at\nleast four CV strategies under the same model grid and scoring rule. The\nstudy should report not only estimation bias but also variance across seeds\nand model-selection stability. Baselines should include standard k-fold and\nstratified k-fold; repeated stratified k-fold and LOOCV serve as contrasting\nalternatives with different variance/compute characteristics.\n\nThe goal is not to prove one universally superior protocol, but to quantify\nwhen more elaborate CV schemes improve reliability enough to justify their\ncost under strict CPU budgets.\n\n*Which cross-validation strategy yields the lowest and most stable model-selection estimation bias for small-sample classification tasks under a fixed candidate-model grid?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"RepeatedStratifiedKFold (5 folds × 3 repeats) achieves lower mean absolute estimation bias than plain KFold (5 folds) on at least 2 of 3 evaluated small-sample datasets, averaged over ≥5 random seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"StratifiedKFold (5 folds) yields lower across-seed standard deviation of selected-model test accuracy than plain KFold (5 folds) on at least 2 of 3 datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"LOOCV does not outperform RepeatedStratifiedKFold in mean absolute estimation bias by more than 0.01 absolute score on any dataset, while requiring greater wall-clock time.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"For small-sample classification model selection, how do KFold, StratifiedKFold, RepeatedStratifiedKFold, and LOOCV compare in estimation bias, stability, and compute cost?\", \"conditions\": [{\"name\": \"kfold_5\", \"description\": \"Model selection via 5-fold KFold (shuffle=True) on the training split.\"}, {\"name\": \"stratified_kfold_5\", \"description\": \"Model selection via 5-fold StratifiedKFold (shuffle=True) on the training split.\"}, {\"name\": \"repeated_stratified_5x3\", \"description\": \"Model selection via RepeatedStratifiedKFold with 5 folds and 3 repeats.\"}, {\"name\": \"loocv\", \"description\": \"Model selection via Leave-One-Out cross-validation on the training split.\"}], \"baselines\": [\"kfold_5 as the non-stratified baseline\", \"stratified_kfold_5 as the standard small-sample baseline\"], \"metrics\": [{\"name\": \"estimation_bias\", \"direction\": \"minimize\", \"description\": \"Absolute difference between best CV mean score and held-out test score of the selected model, aggregated over seeds.\"}, {\"name\": \"selected_model_test_accuracy\", \"direction\": \"maximize\", \"description\": \"Held-out test accuracy of the model selected by each CV strategy, mean over seeds.\"}, {\"name\": \"selection_stability\", \"direction\": \"maximize\", \"description\": \"Fraction of seeds selecting the modal hyperparameter configuration (higher = more stable).\"}, {\"name\": \"wall_clock_sec\", \"direction\": \"minimize\", \"description\": \"Runtime per CV strategy over all seeds and datasets on CPU.\"}], \"datasets\": [{\"name\": \"breast_cancer_small\", \"source\": \"sklearn.datasets.load_breast_cancer (subsample training set to n<=120)\"}, {\"name\": \"wine_small\", \"source\": \"sklearn.datasets.load_wine (subsample training set to n<=100)\"}, {\"name\": \"synthetic_imbalanced_small\", \"source\": \"sklearn.datasets.make_classification (n_samples=160, weights=[0.75,0.25], class_sep tuned for moderate difficulty)\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 600}}", "requirements": "", "rubric": "{\"id\": \"ml10-root\", \"requirements\": \"A credible experiment studying how cross-validation strategy choice (k-fold, stratified k-fold, repeated stratified k-fold, LOOCV) affects small-sample model-selection reliability: strategies are implemented consistently, runs cover multiple small-sample datasets with multiple seeds, and results address H1/H2/H3 directionally around estimation bias, stability, and compute.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative CV strategies or datasets that preserve the scientific question (small-sample bias/stability of CV) should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml10-code\", \"requirements\": \"Cross-validation strategy conditions and the model-selection pipeline are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml10-code-cv-strategies\", \"requirements\": \"The submission implements multiple distinct CV strategies — typically including plain k-fold, stratified k-fold, repeated stratified k-fold, and optionally LOOCV — as separate code paths used for model selection under a shared candidate-model grid.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml10-code-datasets\", \"requirements\": \"The submission uses multiple small-sample datasets (e.g., reduced breast_cancer, wine, or synthetic imbalanced variants) with a held-out test split.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml10-code-bias-metric\", \"requirements\": \"Code computes an estimation-bias style metric (e.g., |best_cv_score - selected_model_test_score|) or equivalent for each (dataset, seed, strategy) and aggregates it reproducibly.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml10-exec\", \"requirements\": \"Execution logs required metrics per strategy with multi-seed evaluation.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml10-exec-metrics-output\", \"requirements\": \"Execution produces a machine-readable results artifact containing estimation bias (or equivalent) and selected-model test accuracy for each implemented strategy on at least one dataset.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml10-exec-seeds-runtime\", \"requirements\": \"Execution uses multiple random seeds per (dataset, strategy) and records timing information sufficient to compare the more expensive strategies (e.g., LOOCV vs repeated stratified). Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml10-results\", \"requirements\": \"Reported results address H1/H2/H3 directionally with interpretable evidence.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml10-result-h1\", \"requirements\": \"The submission compares mean estimation bias of repeated-stratified CV vs plain k-fold per dataset and conveys whether repeated stratified is meaningfully better on most datasets — judge directionally against H1.\", \"weight\": 25.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml10-result-h2h3\", \"requirements\": \"The submission reports selection stability or across-seed variability for stratified k-fold vs plain k-fold (H2) and compares LOOCV versus repeated stratified CV on estimation bias and runtime (H3), conveying qualitative outcomes for both.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml10-result-writeup\", \"requirements\": \"The README or writeup describes the setup, reports per-strategy metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (small dataset scope, candidate-model grid choice, metric sensitivity, compute budget). No strict word-count requirement.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 8, "manifest_file": "tasks/ml/manifests/ML10.yaml", "rubric_file": "tasks/ml/rubrics/ML10.json"} +{"id": "ML11", "domain": "ml", "title": "Benchmarking classical unsupervised outlier detectors under controlled anomaly injection", "topic": "Benchmarking unsupervised outlier detection methods (IsolationForest, LocalOutlierFactor, OneClassSVM, EllipticEnvelope) on UCI datasets with injected anomalies", "domains": ["anomaly-detection", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "roc_auc", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 480, "synthesis": "Classical unsupervised anomaly-detection methods remain widely used in\nproduction tabular pipelines because they are interpretable enough,\nCPU-efficient, and available in standard libraries. Yet their relative\nbehavior can change sharply as anomaly prevalence and feature scaling vary.\nIsolationForest is often robust in heterogeneous feature spaces, while\ndistance/density methods like LocalOutlierFactor can be strong when local\nneighborhoods are informative but may degrade in higher dimensions.\n\nA practical benchmark should avoid external downloads and still provide\nrealistic control over anomaly rate. One reliable strategy is to start from\nclean sklearn classification datasets and inject synthetic anomalies into the\ntest split by sampling uniformly beyond feature-wise quantile ranges of the\ntraining data. This yields known binary ground truth for evaluation while\npreserving a realistic inlier distribution.\n\nA credible CPU-scale study here should compare IsolationForest,\nLocalOutlierFactor (novelty mode), OneClassSVM, and EllipticEnvelope under a\nshared preprocessing pipeline (e.g., StandardScaler), evaluate ROC-AUC and\nPR-AUC across at least two datasets and multiple seeds, and include a simple\nbaseline such as random scoring. Since contamination is rarely known exactly\nin practice, the benchmark should also probe at least two injected anomaly\nrates.\n\nThe main decision-relevant question is not whether any method can detect easy\nanomalies, but which method is consistently best across datasets and\ncontamination levels under strict CPU constraints. A concise, quantitative\nhypothesis-driven comparison is therefore the goal.\n\n*Which classical unsupervised outlier detector is most robust across tabular datasets and injected anomaly rates when measured by ROC-AUC and PR-AUC under a single-core CPU budget?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"IsolationForest achieves higher mean ROC-AUC than OneClassSVM on at least 2 of 3 datasets when averaged over anomaly rates {0.05, 0.10} and ≥3 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At anomaly rate 0.10, LocalOutlierFactor attains the best PR-AUC (rank 1) on at least 1 of 3 datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Each implemented detector (IsolationForest, LocalOutlierFactor, OneClassSVM, EllipticEnvelope) outperforms a random-score baseline in ROC-AUC by at least 0.10 on the pooled mean across all evaluated datasets/rates.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which among IsolationForest, LocalOutlierFactor, OneClassSVM, and EllipticEnvelope is most robust across datasets and injected anomaly rates for unsupervised outlier detection under CPU constraints?\", \"conditions\": [{\"name\": \"isolation_forest\", \"description\": \"sklearn IsolationForest with contamination set to injected rate, 200 trees, default feature subsampling.\"}, {\"name\": \"local_outlier_factor_novelty\", \"description\": \"sklearn LocalOutlierFactor in novelty=True mode, n_neighbors in [20, 35], contamination set to injected rate.\"}, {\"name\": \"one_class_svm_rbf\", \"description\": \"sklearn OneClassSVM with RBF kernel, nu matched to injected rate, gamma='scale'.\"}, {\"name\": \"elliptic_envelope\", \"description\": \"sklearn EllipticEnvelope with contamination set to injected rate and robust covariance estimation.\"}, {\"name\": \"random_score_baseline\", \"description\": \"Baseline assigning i.i.d. uniform random anomaly scores to test points.\"}], \"baselines\": [\"random_score_baseline\", \"one_class_svm_rbf as a classical margin-based reference\"], \"metrics\": [{\"name\": \"roc_auc\", \"direction\": \"maximize\", \"description\": \"ROC-AUC of anomaly scores against injected ground-truth labels on the test set, averaged over seeds.\"}, {\"name\": \"pr_auc\", \"direction\": \"maximize\", \"description\": \"Average precision / PR-AUC for anomaly class on the test set, averaged over seeds.\"}, {\"name\": \"fit_predict_time_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock seconds for model fit plus test scoring per (dataset, rate, seed), reported as mean.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"digits\", \"source\": \"sklearn.datasets.load_digits\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 480}}", "requirements": "", "rubric": "{\"id\": \"ml11-root\", \"requirements\": \"A credible experiment benchmarking unsupervised outlier detectors (IsolationForest, LocalOutlierFactor, OneClassSVM, EllipticEnvelope, or equivalents) with injected anomalies: methods are implemented as distinct code paths, runs cover multiple datasets across anomaly rates, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated substitutes (e.g., HBOS, KNN-based outlier) that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml11-code\", \"requirements\": \"The outlier-detection conditions and anomaly-injection pipeline are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml11-code-methods\", \"requirements\": \"The submission implements multiple distinct detector code paths — typically including IsolationForest, LOF, OneClassSVM, and an elliptic/Gaussian-envelope method — rather than aliases to one shared estimator.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml11-code-injection\", \"requirements\": \"A reproducible anomaly-injection routine is implemented for test data at one or more explicit contamination rates with binary ground-truth anomaly labels.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml11-code-data\", \"requirements\": \"The code uses multiple datasets (sklearn built-ins or comparable) and applies a consistent preprocessing pipeline (including feature scaling) before model fitting.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml11-exec\", \"requirements\": \"Execution records anomaly-detection metrics for each condition.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml11-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact containing numeric ROC-AUC and PR-AUC (or equivalents) for each implemented method on at least one (dataset, rate) cell.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml11-exec-seeds-rates\", \"requirements\": \"Reported results aggregate over multiple random seeds and include at least two anomaly rates, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml11-exec-runtime\", \"requirements\": \"The run logs a wall-clock timing measure per method and demonstrates completion within a CPU-only workflow.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml11-results\", \"requirements\": \"Quantitative analysis addresses H1/H2/H3 directionally.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml11-result-h1\", \"requirements\": \"The submission compares mean ROC-AUC of IsolationForest vs OneClassSVM per dataset and conveys whether IsolationForest is meaningfully better on most datasets — judge directionally against H1.\", \"weight\": 25.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml11-result-h2h3\", \"requirements\": \"The submission conveys whether LOF is competitive on PR-AUC for at least one dataset at a higher anomaly rate (H2) and whether detectors meaningfully outperform a random baseline on pooled ROC-AUC (H3).\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml11-result-writeup\", \"requirements\": \"The README or writeup describes setup and anomaly injection, reports ROC-AUC/PR-AUC results, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (synthetic anomalies, dataset scope, hyperparameter sensitivity, seed count). No strict word-count requirement.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML11.yaml", "rubric_file": "tasks/ml/rubrics/ML11.json"} +{"id": "ML12", "domain": "ml", "title": "Comparing clustering algorithms on synthetic non-convex and anisotropic shapes", "topic": "Comparing clustering algorithms (k-means, agglomerative, DBSCAN, HDBSCAN, spectral) on synthetic shapes (moons, circles, blobs, anisotropic) with ground-truth labels", "domains": ["machine-learning", "clustering"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "adjusted_rand_score", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Clustering algorithms encode very different geometric assumptions. K-means\nprefers spherical, equal-variance groups; agglomerative clustering can adapt\nto hierarchical structure depending on linkage; DBSCAN finds dense regions\nand can recover non-convex shapes while labeling outliers as noise; spectral\nclustering can separate manifolds when graph affinity is appropriate.\nBecause these assumptions interact strongly with data geometry, a single\nbenchmark score often hides systematic failure modes.\n\nSynthetic datasets are ideal for a fast but meaningful comparison because we\ncan generate known structures with ground-truth labels: intertwined moons,\nconcentric circles, and anisotropic Gaussian blobs. These patterns expose\nstrengths and weaknesses that are difficult to isolate in real-world data.\nWith labeled synthetic data, external clustering metrics like adjusted rand\nindex (ARI) and normalized mutual information (NMI) directly quantify\npartition recovery quality.\n\nA credible CPU-scale study should compare several algorithms under a shared\nprotocol: standardized inputs, fixed train-size generation per seed, and\nexplicit handling of methods that output noise labels. It should include at\nleast one centroid baseline (k-means) and one density method (DBSCAN), then\ntest whether non-linear methods systematically outperform centroid methods on\nnon-convex shapes while avoiding large regressions on anisotropic blobs.\n\nThe practical goal is not to crown one universally best algorithm, but to\nestablish data-shape-dependent guidance. If shape complexity changes which\nmethod wins, practitioners should choose clustering tools by geometric prior\nrather than habit.\n\n*How strongly does dataset geometry (non-convex vs anisotropic) determine which clustering algorithm achieves the best ground-truth agreement under a fixed CPU-budget protocol?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On the two non-convex datasets (moons and circles), at least one of {DBSCAN, SpectralClustering} achieves mean ARI that is at least 0.15 higher than k-means, averaged over ≥5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On the anisotropic-blobs dataset, agglomerative clustering (ward linkage) achieves ARI greater than or equal to k-means in the seed-averaged result (difference >= 0.00).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"No single algorithm ranks first in ARI on all evaluated datasets; i.e., the best-ARI method differs on at least one dataset from the global winner.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How strongly does dataset geometry (non-convex vs anisotropic) determine which clustering algorithm achieves the best agreement with ground-truth labels under a fixed CPU-budget protocol?\", \"conditions\": [{\"name\": \"kmeans_k2_or_k3\", \"description\": \"KMeans baseline with n_clusters set to dataset ground-truth class count; n_init>=10.\"}, {\"name\": \"agglomerative_ward\", \"description\": \"AgglomerativeClustering with ward linkage and n_clusters equal to ground-truth class count.\"}, {\"name\": \"dbscan_tuned_eps\", \"description\": \"DBSCAN with eps selected from a small grid per dataset (e.g., 0.1 to 0.5) and fixed min_samples (e.g., 5).\"}, {\"name\": \"spectral_rbf\", \"description\": \"SpectralClustering with RBF affinity and n_clusters equal to ground-truth class count.\"}], \"baselines\": [\"kmeans_k2_or_k3 as centroid-based baseline\", \"agglomerative_ward as hierarchical baseline\"], \"metrics\": [{\"name\": \"adjusted_rand_score\", \"direction\": \"maximize\", \"description\": \"Adjusted Rand Index against ground-truth labels, averaged over seeds.\"}, {\"name\": \"normalized_mutual_info\", \"direction\": \"maximize\", \"description\": \"Normalized Mutual Information against ground-truth labels.\"}, {\"name\": \"silhouette_score\", \"direction\": \"maximize\", \"description\": \"Internal cohesion/separation score computed from predicted labels (excluding noise-only failures).\"}], \"datasets\": [{\"name\": \"moons\", \"source\": \"sklearn.datasets.make_moons (n_samples~800, noise~0.08)\"}, {\"name\": \"circles\", \"source\": \"sklearn.datasets.make_circles (n_samples~800, noise~0.06, factor~0.5)\"}, {\"name\": \"anisotropic_blobs\", \"source\": \"sklearn.datasets.make_blobs followed by linear transformation to create anisotropy\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml12-root\", \"requirements\": \"A credible experiment comparing clustering algorithms across synthetic geometric datasets: clustering conditions are implemented, execution covers multiple datasets with repeated seeds and ARI-centric reporting, and the analysis addresses H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated algorithm substitutes (e.g., HDBSCAN for DBSCAN) that test the same question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml12-code\", \"requirements\": \"The clustering conditions and dataset generators are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml12-code-algos\", \"requirements\": \"The submission implements multiple distinct clustering conditions — typically k-means, agglomerative, DBSCAN, and/or spectral — via sklearn APIs with separate code paths and method-specific hyperparameters.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml12-code-data\", \"requirements\": \"The submission generates multiple synthetic datasets with varied geometry (e.g., moons, circles, anisotropic blobs), preserves ground-truth labels, and standardizes features before clustering where appropriate.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml12-code-setup\", \"requirements\": \"The experiment framework includes seed control and a consistent per-dataset protocol for cluster-count-dependent methods.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml12-exec\", \"requirements\": \"Execution produces clustering metrics for each condition.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml12-exec-metrics\", \"requirements\": \"Execution outputs a metrics artifact containing numeric adjusted_rand_score (or equivalent) for each implemented condition on at least one dataset.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml12-exec-repeats\", \"requirements\": \"Reported results are aggregated over multiple random seeds per (dataset, condition) with a dispersion estimate. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml12-exec-tuning\", \"requirements\": \"For DBSCAN (or equivalent density-based method), the run documents a reasonable choice of eps/min_samples (either via a small search or a justified fixed value), logged in the artifacts.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml12-results\", \"requirements\": \"Results address H1/H2/H3 directionally with interpretable evidence.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml12-result-h1\", \"requirements\": \"The submission compares ARI of density-based / spectral methods against k-means on nonlinear-geometry datasets and conveys whether they produce meaningfully better cluster assignments — judge directionally against H1.\", \"weight\": 25.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml12-result-h2h3\", \"requirements\": \"The submission conveys dataset-wise ARI outcomes — whether agglomerative-ward is at least competitive with k-means on anisotropic blobs (H2) and whether any single method wins across all datasets (H3).\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml12-result-writeup\", \"requirements\": \"The README or writeup describes setup, key ARI/NMI findings per dataset, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (synthetic-only scope, hyperparameter sensitivity, seed count, metric dependence). No strict word-count requirement.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML12.yaml", "rubric_file": "tasks/ml/rubrics/ML12.json"} +{"id": "ML13", "domain": "ml", "title": "Evaluating kernel choices for Gaussian Process regression on synthetic 1-D and 5-D functions", "topic": "Evaluating kernel choices (RBF, polynomial, Matern-3/2, Matern-5/2) for Gaussian Process regression on synthetic 1-D and 5-D functions with homoscedastic noise", "domains": ["machine-learning", "kernel-methods"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_nll", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 360, "synthesis": "Gaussian Process (GP) regression performance depends strongly on the kernel,\nwhich encodes assumptions about function smoothness and local structure.\nPractitioners often default to the RBF kernel because it is easy to optimize\nand widely available, but this choice can underperform when the target\nfunction has roughness patterns that are better matched by Matern families or\nwhen polynomial trends dominate part of the signal. Since kernel\nmisspecification can be subtle, a short benchmark should compare predictive\nuncertainty quality as well as point error.\n\nA CPU-scale study can be done entirely with sklearn GaussianProcessRegressor\non synthetic datasets where the data-generating process is controlled. In 1-D,\na mildly nonstationary smooth function with additive Gaussian noise can expose\ndifferences in extrapolation and uncertainty tails. In 5-D, a mixed\nsinusoidal-plus-quadratic target can stress anisotropy and interactions while\nremaining fast enough for repeated fitting under multiple random seeds.\n\nTo make claims measurable, the experiment should evaluate at least four kernel\nchoices (RBF, polynomial, Matern-3/2, Matern-5/2) under the same train/test\nsplits, optimizer restarts, and noise model assumptions. Because the topic's\nkey metric is negative log-likelihood, uncertainty calibration quality should\nbe primary; RMSE and R^2 provide supporting evidence for point prediction.\nSeed averaging is important because synthetic splits and optimizer\ninitialization can change marginal likelihood optimization outcomes.\n\nThe core question is whether smoother kernels (RBF, Matern-5/2) consistently\noutperform rougher or mismatched kernels (Matern-3/2, polynomial) on test NLL\nacross both low- and moderate-dimensional synthetic tasks, and whether ranking\nby NLL aligns with ranking by RMSE.\n\n*Which GP kernel among RBF, polynomial, Matern-3/2, and Matern-5/2 yields the best uncertainty-aware generalization (lowest test NLL) on synthetic 1-D and 5-D noisy regression functions?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Across the two synthetic datasets, at least one of {RBF, Matern-5/2} achieves the lowest mean test NLL on each dataset (averaged over >=5 seeds).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The polynomial kernel has mean test NLL at least 10% worse than the best kernel on at least 1 of the 2 datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Kernel ranking by mean test NLL and by mean RMSE is not identical on at least 1 dataset, indicating uncertainty quality and point error disagree.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do RBF, polynomial, Matern-3/2, and Matern-5/2 kernels compare for Gaussian Process regression on synthetic 1-D and 5-D noisy functions when judged primarily by test NLL?\", \"conditions\": [{\"name\": \"gp_rbf\", \"description\": \"GaussianProcessRegressor with ConstantKernel * RBF + WhiteKernel, hyperparameters optimized by log-marginal-likelihood.\"}, {\"name\": \"gp_poly_deg2\", \"description\": \"GaussianProcessRegressor with ConstantKernel * DotProduct^2 (implemented via polynomial feature mapping + DotProduct kernel) + WhiteKernel as a polynomial-trend proxy.\"}, {\"name\": \"gp_matern32\", \"description\": \"GaussianProcessRegressor with ConstantKernel * Matern(nu=1.5) + WhiteKernel.\"}, {\"name\": \"gp_matern52\", \"description\": \"GaussianProcessRegressor with ConstantKernel * Matern(nu=2.5) + WhiteKernel.\"}], \"baselines\": [\"gp_rbf as the common default-kernel baseline\", \"gp_poly_deg2 as a mismatched-trend baseline\"], \"metrics\": [{\"name\": \"test_nll\", \"direction\": \"minimize\", \"description\": \"Mean negative log predictive density on held-out test data using GP predictive mean and variance, averaged over seeds.\"}, {\"name\": \"rmse\", \"direction\": \"minimize\", \"description\": \"Root mean squared error on test targets, averaged over seeds.\"}, {\"name\": \"r2\", \"direction\": \"maximize\", \"description\": \"Coefficient of determination on test targets, averaged over seeds.\"}], \"datasets\": [{\"name\": \"synthetic_1d_sinmix\", \"source\": \"Generated with numpy: x in [-3,3], y = sin(2x) + 0.3x + epsilon, epsilon~N(0, 0.15^2).\"}, {\"name\": \"synthetic_5d_mixed\", \"source\": \"Generated with numpy: X in [-2,2]^5, y = sin(x1) + 0.5*x2^2 - 0.7*x3 + 0.3*x4*x5 + epsilon, epsilon~N(0, 0.2^2).\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 360}}", "requirements": "", "rubric": "{\"id\": \"ml13-root\", \"requirements\": \"A credible experiment studying kernel-choice effects (RBF, polynomial, Matern-3/2, Matern-5/2, or equivalents) for Gaussian Process regression on synthetic noisy functions: conditions are implemented, execution covers multiple datasets with repeated seeds, and results address H1/H2/H3 directionally using test NLL as the primary metric.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative kernels or dataset dimensions that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml13-code\", \"requirements\": \"The GP kernel conditions and synthetic datasets are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml13-code-kernels\", \"requirements\": \"The submission implements multiple kernel conditions — typically including RBF, a polynomial kernel, and one or more Matern kernels — as distinct GP configurations with comparable noise handling.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml13-code-datasets\", \"requirements\": \"The submission generates synthetic regression datasets with explicit noise (both a low-dimensional and a higher-dimensional case preferred) and creates train/test splits.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml13-code-setup\", \"requirements\": \"Experimental setup controls are consistent across kernels (same split protocol, seed handling, and optimizer restart policy), enabling fair kernel comparison.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml13-exec\", \"requirements\": \"Execution logs primary and secondary metrics for each kernel and dataset.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml13-exec-metrics\", \"requirements\": \"Execution produces a metrics artifact containing numeric test NLL and RMSE (or equivalents) for every implemented (kernel, dataset) pair.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml13-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (kernel, dataset) cell, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml13-results\", \"requirements\": \"Results analysis addresses H1/H2/H3 directionally with quantitative comparisons.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml13-result-h1\", \"requirements\": \"The submission reports per-dataset mean test-NLL ranking and conveys whether smooth kernels (RBF, Matern-5/2) tend to be best — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml13-result-h2\", \"requirements\": \"The submission compares the polynomial-kernel NLL against the best kernel and conveys whether polynomial is meaningfully worse on at least one dataset (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml13-result-h3\", \"requirements\": \"The submission compares kernel rankings by test NLL and RMSE on each dataset and conveys whether rankings can differ between these two metrics (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml13-result-writeup\", \"requirements\": \"The README or writeup describes implementation choices, dataset generation, metric tables, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (synthetic-only scope, kernel parameterization, seed/compute constraints). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML13.yaml", "rubric_file": "tasks/ml/rubrics/ML13.json"} +{"id": "ML14", "domain": "ml", "title": "Comparing split-conformal, Mondrian conformal, and CQR-style intervals on heteroscedastic regression", "topic": "Comparing conformal prediction procedures (split-conformal, Mondrian, CQR) for achieving target coverage on heteroscedastic regression tasks", "domains": ["machine-learning", "uncertainty-quantification"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "coverage_gap", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Conformal prediction provides finite-sample coverage guarantees with minimal\nassumptions, making it attractive for uncertainty quantification in practical\nregression. However, standard split-conformal intervals are often globally\ncalibrated and can be inefficient when noise is heteroscedastic: they may\nover-cover low-noise regions and under-cover high-noise regions. This\nmotivates conditional variants such as Mondrian conformal (group-conditional\ncalibration) and CQR-style approaches that adapt interval width through\nquantile modeling before conformal correction.\n\nIn small, CPU-only settings, one can still run a meaningful comparison by\nusing sklearn-compatible regressors and synthetic data where heteroscedastic\nstructure is controlled. A rigorous setup should evaluate not only marginal\ncoverage but also efficiency (mean interval width) and conditional behavior\n(coverage gap across strata of predicted difficulty or input regions). These\ndiagnostics reveal when methods achieve nominal coverage by producing overly\nwide intervals versus genuinely adapting to varying noise.\n\nA credible benchmark should include at least one homoscedastic control and at\nleast one heteroscedastic dataset, then compare split-conformal, a Mondrian\nvariant (binning by an auxiliary score such as |x| or predicted scale), and a\nCQR-style method based on lower/upper quantile regressors plus conformal\nadjustment. Repeated random splits (multiple seeds) are needed because\nconformal procedures can vary with calibration sample composition.\n\nThe key question is whether adaptive procedures can reduce conditional\nmiscoverage while maintaining near-target marginal coverage and reasonable\ninterval width under strict runtime constraints.\n\n*Do Mondrian and CQR-style conformal procedures achieve lower conditional coverage error than vanilla split-conformal at comparable marginal coverage on heteroscedastic regression tasks?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On heteroscedastic datasets, at least one adaptive method (Mondrian or CQR-style) attains a lower absolute coverage gap to the 90% target than split-conformal by at least 0.02 on at least 2 of 3 datasets, averaged over ≥5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At matched target 90% coverage, CQR-style conformal yields mean interval width no larger than split-conformal on at least 2 of 3 datasets (difference ≤ 0.00).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Mondrian conformal reduces worst-bin conditional miscoverage (max over 4 bins of |bin_coverage-0.90|) by at least 0.03 versus split-conformal on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Do adaptive conformal procedures (Mondrian, CQR-style) improve coverage quality and efficiency over split-conformal for heteroscedastic regression under a fixed 90% target coverage?\", \"conditions\": [{\"name\": \"split_conformal_rf\", \"description\": \"Vanilla split-conformal regression using RandomForestRegressor point predictor and absolute residual conformity scores on a held-out calibration split.\"}, {\"name\": \"mondrian_conformal_rf_4bins\", \"description\": \"Mondrian split-conformal with the same base predictor, calibration and test points partitioned into 4 bins by an auxiliary score (e.g., fitted value or |x0| proxy), with per-bin quantiles.\"}, {\"name\": \"cqr_gbr\", \"description\": \"CQR-style method using GradientBoostingRegressor quantile models (alpha=0.05, 0.95) and split-conformal correction on calibration residuals of quantile bands.\"}, {\"name\": \"naive_quantile_no_conformal\", \"description\": \"Non-conformal baseline using raw quantile regression interval [q0.05, q0.95] without conformal adjustment.\"}], \"baselines\": [\"split_conformal_rf is the primary conformal baseline\", \"naive_quantile_no_conformal is the non-conformal baseline\"], \"metrics\": [{\"name\": \"coverage_gap\", \"direction\": \"minimize\", \"description\": \"Absolute difference between empirical marginal coverage and target 0.90 on the test split, averaged over seeds.\"}, {\"name\": \"mean_interval_width\", \"direction\": \"minimize\", \"description\": \"Average prediction interval width on test samples, averaged over seeds.\"}, {\"name\": \"worst_bin_miscoverage\", \"direction\": \"minimize\", \"description\": \"Maximum across 4 bins of absolute deviation between bin coverage and 0.90, measuring conditional coverage disparity.\"}], \"datasets\": [{\"name\": \"hetero_sine\", \"source\": \"synthetic: y = sin(2πx) + (0.1 + 0.5|x|)ε, x~Uniform(-1,1), n≈2000\"}, {\"name\": \"hetero_friedman1\", \"source\": \"synthetic: sklearn.datasets.make_friedman1 with multiplicative noise scale depending on x0\"}, {\"name\": \"diabetes\", \"source\": \"sklearn.datasets.load_diabetes (tabular regression benchmark)\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml14-root\", \"requirements\": \"A credible experiment comparing split-conformal, Mondrian conformal, CQR-style, and optionally naive-quantile prediction intervals for ~90% target coverage on heteroscedastic regression: methods are implemented, executed on multiple datasets with multiple seeds, and results address H1/H2/H3 directionally with coverage-gap-focused analysis.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative conformal variants (e.g., jackknife+, locally adaptive) that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml14-code\", \"requirements\": \"The conformal and baseline interval-construction conditions are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml14-code-methods\", \"requirements\": \"The submission implements multiple distinct conditions — typically including split conformal, a Mondrian/group-adaptive variant, and a CQR-style or naive-quantile baseline — as separate code paths, not a single shared interval formula.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml14-code-conformal-splits\", \"requirements\": \"Conformal methods use explicit train/calibration/test separation (or equivalent cross-fit logic) so calibration quantiles are computed on data not used to fit base regressors.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml14-code-datasets\", \"requirements\": \"The submission uses multiple datasets (including at least one heteroscedastic synthetic dataset) and prepares regression targets correctly.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml14-exec\", \"requirements\": \"Execution outputs interval-quality metrics for each condition.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml14-exec-metrics\", \"requirements\": \"Execution produces a metrics artifact containing numeric coverage gap and mean interval width (or equivalents) for each implemented condition on at least one dataset.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml14-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (condition, dataset) cell with a dispersion estimate. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml14-exec-conditional\", \"requirements\": \"Execution computes a conditional-coverage diagnostic (e.g., worst-bin miscoverage across several bins) for the conformal methods.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml14-results\", \"requirements\": \"Quantitative analysis addresses H1/H2/H3 directionally and discusses trade-offs between coverage, conditional validity, and interval efficiency.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml14-result-h1\", \"requirements\": \"The submission compares coverage gap of adaptive methods (Mondrian, CQR-style) against plain split conformal per dataset and conveys whether adaptive methods are meaningfully closer to the nominal coverage — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml14-result-h2\", \"requirements\": \"The submission evaluates whether CQR-style intervals achieve mean interval width comparable to or narrower than plain split conformal on most datasets and conveys an H2 outcome.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml14-result-h3\", \"requirements\": \"The submission compares worst-bin miscoverage for Mondrian conformal vs plain split conformal and conveys whether Mondrian yields a meaningful improvement in conditional coverage (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml14-result-writeup\", \"requirements\": \"The README or writeup describes methods and datasets, reports key metric values (coverage gap, interval width, worst-bin miscoverage), conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (finite seeds, binning choice, synthetic-to-real transfer). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML14.yaml", "rubric_file": "tasks/ml/rubrics/ML14.json"} +{"id": "ML15", "domain": "ml", "title": "Filter-based vs embedded L1 feature selection with injected noise features", "topic": "Investigating filter-based feature selection (mutual_info_classif, chi2, f_classif) vs embedded L1-logistic selection on UCI classification datasets with injected irrelevant features", "domains": ["machine-learning", "feature-selection"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Feature selection on tabular classification problems is often presented as a\nchoice between simple univariate filters and embedded sparse models.\nUnivariate filters such as chi-squared, ANOVA F-score, and mutual\ninformation are computationally cheap and easy to apply in a pipeline, but\nthey evaluate each feature independently and can miss interactions. Embedded\nL1-regularized logistic regression performs selection during model fitting,\npotentially yielding feature subsets that better align with predictive\nstructure when many irrelevant variables are present.\n\nA practical stress test is to inject synthetic irrelevant features into\notherwise standard benchmark datasets. This setup creates controlled feature\ndilution while preserving original labels and base difficulty. Under such\ndilution, methods that can ignore noise should maintain accuracy and avoid\nselecting many synthetic features. Conversely, methods sensitive to spurious\nunivariate associations may degrade as noise dimension increases.\n\nA credible CPU-only study should compare at least three filter selectors\n(mutual_info_classif, chi2, f_classif) and an embedded L1 selector in a\nshared downstream classifier pipeline, evaluate on multiple sklearn\nclassification datasets, and report both predictive performance and selection\nquality. Including multiple random seeds is important because both noise\ninjection and train/test splits can change apparent selector rankings.\n\nThe key aim is not to reproduce a single published table, but to determine\nwhether embedded sparsity offers robustness advantages over filters as\nirrelevant dimensions grow, and whether that robustness generalizes across\ndatasets with different feature scales and class structures.\n\n*Does embedded L1-logistic feature selection retain predictive performance and reject injected irrelevant features more reliably than univariate filter methods under controlled feature-noise injection?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"With 5x injected irrelevant features (relative to original feature count), L1-logistic embedded selection achieves higher mean test_accuracy than each filter method (mutual_info_classif, chi2, f_classif) on at least 2 of 3 datasets, averaged over >=5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At fixed selected-feature budget k=20 (or all features if p<20), L1-logistic selects a lower fraction of injected irrelevant features than the average of the three filter methods on all evaluated datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"From no-injection to 5x-injection settings, mean test_accuracy drop for L1-logistic is <= 3 percentage points on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does embedded L1-logistic feature selection outperform univariate filter methods in robustness to injected irrelevant features on sklearn tabular classification datasets?\", \"conditions\": [{\"name\": \"filter_mutual_info_k20\", \"description\": \"SelectKBest(mutual_info_classif, k=20 or p if p<20) followed by LogisticRegression classifier.\"}, {\"name\": \"filter_chi2_k20\", \"description\": \"MinMax scaling to nonnegative domain, then SelectKBest(chi2, k=20 or p if p<20) followed by LogisticRegression classifier.\"}, {\"name\": \"filter_f_classif_k20\", \"description\": \"SelectKBest(f_classif, k=20 or p if p<20) followed by LogisticRegression classifier.\"}, {\"name\": \"embedded_l1_logistic\", \"description\": \"L1-penalized LogisticRegression (saga/liblinear) used as embedded selector; keep top-20 by absolute coefficient magnitude (or nonzero if <=20), retrain LogisticRegression on selected subset.\"}], \"baselines\": [\"filter_f_classif_k20 as a standard univariate linear-statistic baseline\", \"filter_mutual_info_k20 as a nonlinear dependency baseline\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Held-out test accuracy averaged over >=5 seeds for each dataset and injection level.\"}, {\"name\": \"noise_feature_selection_rate\", \"direction\": \"minimize\", \"description\": \"Fraction of selected features that come from injected irrelevant columns.\"}, {\"name\": \"accuracy_drop_0x_to_5x_pp\", \"direction\": \"minimize\", \"description\": \"Absolute percentage-point drop in test_accuracy from 0x to 5x injection.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"digits\", \"source\": \"sklearn.datasets.load_digits\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml15-root\", \"requirements\": \"A credible experiment studying filter-based feature selection (mutual information, chi-square, ANOVA F) versus embedded L1-logistic selection under injected irrelevant features: conditions are implemented, runs cover multiple datasets with multiple seeds and injection levels, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative filter methods or embedded selectors (e.g., tree-based importance) that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml15-code\", \"requirements\": \"Feature-selection conditions and the noise-injection pipeline are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml15-code-conditions\", \"requirements\": \"The submission implements multiple distinct selection conditions — typically including mutual information, chi-square, ANOVA F, and L1-logistic (or a comparable embedded method) — rather than reusing one selector for all conditions.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml15-code-injection\", \"requirements\": \"The pipeline injects synthetic irrelevant features at one or more defined levels (including a no-injection baseline and a higher-injection case) and tracks which columns are injected so a noise-selection rate can be computed.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml15-code-datasets\", \"requirements\": \"The submission uses multiple datasets (sklearn built-ins or comparable) and a reasonable train/test split.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml15-exec\", \"requirements\": \"Execution produces metrics across selectors and injection levels.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml15-exec-metrics\", \"requirements\": \"Execution outputs a structured metrics artifact containing numeric test accuracy and a noise-selection-rate style measure for each implemented condition on at least one dataset with at least two injection levels.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml15-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (dataset, condition, injection level) cell with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml15-results\", \"requirements\": \"Results address H1/H2/H3 directionally with a clear narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml15-result-h1\", \"requirements\": \"The submission compares mean test accuracy at the higher injection level between the embedded L1-logistic method and each filter method per dataset and conveys whether L1 tends to be better — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml15-result-h2\", \"requirements\": \"The submission reports the noise-selection rate for each method and conveys whether the embedded method selects fewer irrelevant features than the filter-method average on most datasets (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml15-result-h3\", \"requirements\": \"The submission reports the accuracy drop from low-injection to high-injection for the embedded method and conveys whether the drop is small on most datasets (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml15-result-writeup\", \"requirements\": \"The README or writeup describes the experimental setup, reports key metric tables, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as synthetic-noise realism, k-choice, classifier dependence, and seed/dataset scope. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML15.yaml", "rubric_file": "tasks/ml/rubrics/ML15.json"} +{"id": "ML16", "domain": "ml", "title": "Bandit algorithm robustness under stationary and drifting reward regimes", "topic": "Comparing multi-armed bandit algorithms (epsilon-greedy, UCB1, Thompson sampling, Exp3) on synthetic Bernoulli and Gaussian arms under stationary and drift regimes", "domains": ["reinforcement-learning", "bandits"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "cumulative_regret", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Multi-armed bandit algorithms encode different assumptions about reward\ngeneration and non-stationarity. Epsilon-greedy is simple and adaptable with\npersistent exploration; UCB1 is optimism-driven and often strong in\nstationary stochastic settings; Thompson sampling can be highly sample\nefficient when model assumptions are matched; Exp3 is designed for\nadversarial settings and may trade off stochastic efficiency for robustness.\nIn small CPU-constrained studies, these differences are often discussed\nqualitatively but not tested with matched synthetic environments.\n\nA meaningful benchmark should evaluate both Bernoulli and Gaussian reward\narms, because posterior/model assumptions and noise scale can materially\nchange outcomes. It should also include stationary and drifting regimes:\nmethods tuned for fixed means can degrade when the best arm changes over\ntime. Regret, not just final reward, is the right primary metric because it\ncaptures online learning efficiency throughout the horizon.\n\nA credible experiment here implements the four algorithms with consistent\naction/reward interfaces, runs repeated simulations over multiple seeds, and\nreports cumulative regret trajectories and endpoint statistics. To keep the\nstudy CPU-friendly, horizons and arm counts should be modest (e.g., 5-10 arms,\n500-2000 steps), with vectorized numpy updates where possible.\n\nThe key analytical value is comparative: identify where UCB1/Thompson excel\nin stationary stochastic settings, and whether epsilon-greedy or Exp3 is more\nresilient under drift. The objective is not reproducing a single paper, but\ntesting algorithm-environment fit under controlled synthetic shifts.\n\n*How do epsilon-greedy, UCB1, Thompson sampling, and Exp3 trade off cumulative regret across Bernoulli vs Gaussian arms and stationary vs drifting reward regimes?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"In stationary Bernoulli environments, either UCB1 or Thompson sampling achieves at least 10% lower mean cumulative regret than epsilon-greedy at horizon T on at least 2 of 3 datasets (averaged over >=20 seeds).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"In drifting environments, epsilon-greedy or Exp3 achieves lower mean cumulative regret than UCB1 on at least 2 of 3 datasets at horizon T (averaged over >=20 seeds).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Across all evaluated datasets, no single algorithm is best (lowest mean cumulative regret) on every dataset, indicating environment-dependent performance.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do epsilon-greedy, UCB1, Thompson sampling, and Exp3 compare in cumulative regret across stationary versus drifting synthetic bandit tasks with Bernoulli and Gaussian rewards?\", \"conditions\": [{\"name\": \"epsilon_greedy_eps0.1\", \"description\": \"Epsilon-greedy with constant epsilon=0.1 and sample-mean value estimates.\"}, {\"name\": \"ucb1\", \"description\": \"UCB1 with exploration bonus sqrt(2 log t / n_a).\"}, {\"name\": \"thompson_sampling\", \"description\": \"Thompson sampling using Beta-Bernoulli updates for Bernoulli arms and Gaussian posterior sampling (known variance assumption) for Gaussian arms.\"}, {\"name\": \"exp3_gamma0.07\", \"description\": \"Exp3 with gamma=0.07 and importance-weighted reward estimates.\"}], \"baselines\": [\"epsilon_greedy_eps0.1 as simple stochastic baseline\", \"ucb1 as canonical optimism baseline\"], \"metrics\": [{\"name\": \"cumulative_regret\", \"direction\": \"minimize\", \"description\": \"Mean cumulative regret at final horizon T relative to oracle best arm per round, averaged over seeds.\"}, {\"name\": \"instantaneous_regret_auc\", \"direction\": \"minimize\", \"description\": \"Area under per-round regret curve over time (lower is better).\"}, {\"name\": \"best_arm_selection_rate\", \"direction\": \"maximize\", \"description\": \"Fraction of rounds selecting the current optimal arm (for drift: time-varying optimum).\"}], \"datasets\": [{\"name\": \"bernoulli_stationary_k10\", \"source\": \"synthetic: 10 Bernoulli arms with fixed means sampled in [0.05, 0.95] and sorted gap >=0.05\"}, {\"name\": \"bernoulli_drift_k10\", \"source\": \"synthetic: 10 Bernoulli arms with piecewise-constant means; best arm switches at predefined changepoints\"}, {\"name\": \"gaussian_drift_k8\", \"source\": \"synthetic: 8 Gaussian arms (sigma=1) with linearly drifting means and periodic rank reversals\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml16-root\", \"requirements\": \"A credible experiment comparing bandit algorithms (epsilon-greedy, UCB1, Thompson sampling, Exp3, or equivalents) under stationary and drifting synthetic regimes: algorithms are implemented with a common interface, experiments cover multiple environments with repeated seeds, and results address H1/H2/H3 directionally using cumulative regret.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated algorithm variants (e.g., UCB-V, linear Thompson) should be credited when they test the same scientific question.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml16-code\", \"requirements\": \"The bandit algorithms and synthetic environments are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml16-code-algos\", \"requirements\": \"The submission implements multiple distinct algorithm code paths — typically including epsilon-greedy, UCB1, Thompson sampling, and/or Exp3 — with per-round action selection and update logic that are not identical wrappers.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml16-code-envs\", \"requirements\": \"The submission defines multiple synthetic bandit environments including at least one stationary and one drifting regime, with reproducible seed control and oracle best-arm rewards per round for regret computation.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml16-code-thompson-exp3\", \"requirements\": \"If Thompson sampling and/or Exp3 are included, implementation uses sampling-based posterior decisions (Thompson) and probability-weighted action selection with importance-weighted updates (Exp3), rather than greedy mean selection.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml16-exec\", \"requirements\": \"Execution produces regret metrics across algorithms and datasets.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml16-exec-runs\", \"requirements\": \"Execution runs multiple seeds per (algorithm, dataset) cell for multiple environments and logs final cumulative-regret values with mean and dispersion. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml16-exec-artifacts\", \"requirements\": \"A machine-readable results artifact is produced containing dataset-wise metrics for each implemented algorithm, including cumulative regret.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml16-results\", \"requirements\": \"Findings address H1/H2/H3 directionally and summarize implications.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml16-result-h1\", \"requirements\": \"The submission compares stationary-regime cumulative regret between epsilon-greedy and {UCB1, Thompson} and conveys whether the principled algorithms are meaningfully better — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml16-result-h2\", \"requirements\": \"The submission evaluates drifting-regime cumulative regret and conveys whether more exploratory algorithms (epsilon-greedy or Exp3) outperform UCB1 on most drifting datasets (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml16-result-h3\", \"requirements\": \"The submission conveys whether any single algorithm dominates across all environments or whether winners are mixed (H3), with supporting tables or summaries.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml16-result-writeup\", \"requirements\": \"The README or writeup describes setup, reports key cumulative-regret results per environment, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (horizon length, hyperparameter sensitivity, synthetic-only scope). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML16.yaml", "rubric_file": "tasks/ml/rubrics/ML16.json"} +{"id": "ML17", "domain": "ml", "title": "Topic-model comparison on small 20newsgroups subsets: LDA vs NMF vs LSA", "topic": "Comparing topic models (LDA, NMF, LSA) on a small subset of 20newsgroups by topic coherence (c_v) and document-cluster adjusted rand score", "domains": ["natural-language-processing", "topic-modeling"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "coherence_cv", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 720, "synthesis": "Topic modeling methods are often compared using either intrinsic quality\nmeasures (such as topic coherence) or extrinsic utility (such as how well\ndocument representations align with known labels), but the two views can\ndisagree. Latent Dirichlet Allocation (LDA), Non-negative Matrix\nFactorization (NMF), and Latent Semantic Analysis (LSA/SVD) each induce\ndifferent assumptions over term-document structure and may therefore rank\ndifferently depending on metric choice.\n\nOn CPU-constrained benchmarks, a practical study should use a compact text\ncorpus and a controlled preprocessing pipeline so that runtime stays short\nwhile still yielding meaningful distinctions. A small subset of\n20newsgroups is ideal because it has accessible labels for external\nclustering evaluation and enough lexical diversity for coherence analysis.\nUsing multiple subset difficulties (well-separated vs more confusable\ncategories) helps test robustness of conclusions.\n\nA credible experiment compares LDA, NMF, and LSA under matched topic counts\nand vectorization settings, then reports both c_v-like coherence and\nadjusted rand index (ARI) from document-cluster assignments against true\nnewsgroup labels. Because exact c_v implementations are not native in\nsklearn, an explicit approximation based on sliding-window co-occurrence\nand normalized PMI should be documented and applied consistently across\nmethods. Repeated runs for stochastic models are needed to avoid\nover-interpreting single-seed noise.\n\nThe core question is whether better intrinsic coherence implies better\nlabel alignment on small real-world corpora, and which model offers the\nbest trade-off under strict CPU budgets.\n\n*Do LDA, NMF, and LSA produce different rankings on coherence versus ARI on small 20newsgroups subsets, and does NMF provide the strongest coherence-structure trade-off under CPU limits?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"NMF achieves higher mean coherence_cv than LSA on at least 2 of 3 evaluated 20newsgroups subsets when using the same number of topics and preprocessing pipeline.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Across the three methods (LDA, NMF, LSA), the method with the highest coherence_cv is also the highest-ARI method on no more than 1 of the 3 subsets, indicating weak metric agreement.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"LDA achieves mean ARI at least 0.03 higher than LSA on at least 2 of 3 subsets when document clusters are obtained by argmax topic assignment.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do LDA, NMF, and LSA compare on coherence_cv versus document-cluster ARI over small 20newsgroups subsets, and do intrinsic/extrinsic rankings diverge?\", \"conditions\": [{\"name\": \"lda_k4\", \"description\": \"LatentDirichletAllocation with n_components=4, max_iter=20, learning_method='batch', CountVectorizer features, repeated over 3 seeds.\"}, {\"name\": \"nmf_k4\", \"description\": \"NMF with n_components=4, init='nndsvda', solver='cd', max_iter=300 on TF-IDF features, repeated over 3 seeds.\"}, {\"name\": \"lsa_k4\", \"description\": \"TruncatedSVD (LSA) with n_components=4 on TF-IDF features; document-topic embeddings clustered via argmax after non-negative shift or via KMeans(k=4) with fixed seed.\"}, {\"name\": \"nmf_k6_sensitivity\", \"description\": \"NMF sensitivity condition with n_components=6 to test topic-count robustness for coherence and ARI trends.\"}], \"baselines\": [\"lsa_k4 as a linear-algebra baseline without probabilistic topic assumptions\", \"lda_k4 as a probabilistic-topic baseline\"], \"metrics\": [{\"name\": \"coherence_cv\", \"direction\": \"maximize\", \"description\": \"Approximate c_v coherence computed from top words per topic using sliding-window co-occurrence and NPMI aggregation, averaged across topics and seeds.\"}, {\"name\": \"ari\", \"direction\": \"maximize\", \"description\": \"Adjusted Rand Index between predicted document clusters (topic argmax or equivalent) and true newsgroup labels.\"}, {\"name\": \"runtime_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock training + inference time per condition/dataset cell on single CPU core.\"}], \"datasets\": [{\"name\": \"20ng_easy4\", \"source\": \"sklearn.datasets.fetch_20newsgroups with categories=['sci.space','rec.autos','comp.graphics','talk.politics.misc']\"}, {\"name\": \"20ng_related4\", \"source\": \"sklearn.datasets.fetch_20newsgroups with categories=['comp.graphics','comp.os.ms-windows.misc','comp.sys.ibm.pc.hardware','comp.sys.mac.hardware']\"}, {\"name\": \"20ng_science4\", \"source\": \"sklearn.datasets.fetch_20newsgroups with categories=['sci.space','sci.med','sci.electronics','sci.crypt']\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 720}}", "requirements": "", "rubric": "{\"id\": \"ml17-root\", \"requirements\": \"A credible experiment comparing LDA, NMF, and LSA topic models on small 20newsgroups subsets: conditions are implemented, execution reports coherence and ARI on multiple subsets with repeated seeds, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative topic-model implementations or coherence approximations that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml17-code\", \"requirements\": \"Topic-model conditions and dataset pipeline are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml17-code-models\", \"requirements\": \"The submission implements LDA, NMF, and LSA (or equivalents such as a truncated SVD for LSA) as distinct modeling code paths with a matched topic-count setting.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml17-code-data\", \"requirements\": \"The submission loads multiple 20newsgroups subsets (or comparable document corpora) with consistent text preprocessing/vectorization documented in code.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml17-code-metrics-impl\", \"requirements\": \"The code computes a coherence-style metric (e.g., c_v or a documented approximation) and ARI from document-cluster assignments for each method.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml17-exec\", \"requirements\": \"Execution produces comparable numeric outputs for all implemented methods.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml17-exec-runs\", \"requirements\": \"Execution evaluates the core methods on multiple datasets and writes per-method, per-dataset coherence and ARI values to a machine-readable artifact.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml17-exec-seeds-runtime\", \"requirements\": \"Execution repeats stochastic methods with multiple seeds per dataset, reports dispersion, and logs a wall-clock timing measure per condition. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml17-results\", \"requirements\": \"Quantitative analysis addresses H1/H2/H3 directionally.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml17-result-h1\", \"requirements\": \"The submission compares NMF vs LSA on coherence for each evaluated dataset and conveys whether NMF tends to yield better coherence — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml17-result-h2\", \"requirements\": \"The submission checks ranking agreement between coherence and ARI across methods per dataset and conveys whether coherence-ranking and ARI-ranking tend to diverge (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml17-result-h3\", \"requirements\": \"The submission reports LDA vs LSA ARI deltas per dataset and conveys whether LDA tends to achieve meaningfully higher document-cluster ARI (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml17-result-writeup\", \"requirements\": \"The README or writeup describes methods and preprocessing, reports coherence/ARI/runtime results, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (subset choice, coherence approximation validity, seed count, clustering-assignment assumptions). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML17.yaml", "rubric_file": "tasks/ml/rubrics/ML17.json"} +{"id": "ML18", "domain": "ml", "title": "Post-hoc calibration methods for sklearn classifiers on tabular benchmarks", "topic": "Evaluating probability calibration methods (Platt scaling, isotonic regression, temperature scaling) applied to scikit-learn classifiers (RandomForest, GBM, SVM-RBF) on UCI benchmarks", "domains": ["machine-learning", "calibration"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "ece", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 480, "synthesis": "Many widely used scikit-learn classifiers optimize discrimination rather\nthan probability quality. Random forests, gradient boosting models, and\nRBF-kernel SVMs can achieve strong accuracy while output probabilities are\nmiscalibrated, especially on small or moderately imbalanced tabular data.\nThis matters in decision settings where thresholds, risk ranking, or cost-\nsensitive actions depend on reliable confidence estimates.\n\nPost-hoc calibration methods are attractive because they can be layered onto\nexisting models without retraining the base learner. Platt scaling fits a\nsigmoid map, isotonic regression fits a non-parametric monotone map, and\ntemperature scaling applies a single-parameter logit rescaling (implemented\nfor binary tasks via optimization on held-out logits/probabilities). These\nmethods differ in flexibility and overfitting risk, so their relative value\nmay depend on classifier family and dataset size.\n\nA credible CPU-scale study should compare uncalibrated outputs versus at\nleast three calibrators across multiple sklearn tabular datasets, using a\nproper train/calibration/test protocol and repeated seeds. Because this is a\ncalibration-centric question, expected calibration error (ECE) and log loss\nshould be primary metrics, with accuracy used as a guardrail to ensure that\ncalibration does not degrade classification utility.\n\nThe goal is not to reproduce a single paper result but to test whether any\none calibrator is consistently superior across heterogeneous models and\ndatasets under tight compute constraints. The key uncertainty is whether\nmethod ranking is stable enough to justify a default choice.\n\n*Which post-hoc calibration method (Platt, isotonic, temperature) most reliably improves ECE across RF/GBM/SVM-RBF on small tabular sklearn benchmarks without materially harming accuracy?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At least one post-hoc calibrator (Platt, isotonic, or temperature) reduces ECE by at least 10% relative to the uncalibrated model for at least 2 of 3 classifiers on at least 2 of 3 datasets, averaged over >=3 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Isotonic regression achieves lower mean ECE than Platt scaling on at least 2 of 3 datasets when calibration-set size is >=150 samples.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Applying post-hoc calibration changes test accuracy by no more than 1.0 absolute percentage point (mean over seeds) for each classifier-dataset pair.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which post-hoc calibration method (Platt, isotonic, temperature) most consistently improves probability calibration for RF/GBM/SVM-RBF on sklearn tabular datasets while preserving accuracy?\", \"conditions\": [{\"name\": \"uncalibrated\", \"description\": \"Base classifier probabilities/decision scores used directly with no calibration.\"}, {\"name\": \"platt_scaling\", \"description\": \"Sigmoid calibration fit on a held-out calibration split (CalibratedClassifierCV with method='sigmoid' or equivalent).\"}, {\"name\": \"isotonic_regression\", \"description\": \"Isotonic calibration fit on a held-out calibration split (CalibratedClassifierCV with method='isotonic' or equivalent).\"}, {\"name\": \"temperature_scaling\", \"description\": \"Single temperature parameter optimized on calibration split to minimize NLL; applied to logits/scores before probability mapping.\"}], \"baselines\": [\"uncalibrated outputs are the primary baseline\", \"platt_scaling serves as a classic parametric calibration baseline\"], \"metrics\": [{\"name\": \"ece\", \"direction\": \"minimize\", \"description\": \"Expected Calibration Error (10-15 bins) on the held-out test split, averaged over seeds.\"}, {\"name\": \"log_loss\", \"direction\": \"minimize\", \"description\": \"Negative log-likelihood on test probabilities.\"}, {\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Classification accuracy on held-out test split to verify discrimination is preserved.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"digits_binary\", \"source\": \"sklearn.datasets.load_digits with target transformed to binary (e.g., digit<5 vs >=5)\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 480}}", "requirements": "", "rubric": "{\"id\": \"ml18-root\", \"requirements\": \"A credible experiment studying post-hoc probability calibration methods (Platt, isotonic, temperature, or equivalents) for sklearn classifiers: calibration conditions are implemented, execution covers multiple datasets/classifiers with repeated seeds, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated calibrator variants or alternative base classifiers that preserve the scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml18-code\", \"requirements\": \"Calibration methods and classifier setup are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml18-code-calibrators\", \"requirements\": \"The submission implements multiple calibration conditions — typically including uncalibrated, Platt, isotonic, and temperature scaling — as distinct code paths applied post-hoc to the same base model outputs.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml18-code-models\", \"requirements\": \"The experiment includes multiple target classifiers (e.g., RandomForest, gradient boosting, SVM-RBF, or equivalents) with consistent train/calibration/test handling.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml18-code-datasets\", \"requirements\": \"The submission uses multiple datasets (sklearn built-ins or comparable) and an explicit calibration split separate from train and test.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml18-exec\", \"requirements\": \"Execution reports calibration-focused metrics across conditions.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml18-exec-metrics\", \"requirements\": \"Execution outputs numeric ECE, log loss, and test accuracy (or equivalents) for each implemented condition on at least one dataset-classifier pair in a machine-readable artifact.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml18-exec-seeds\", \"requirements\": \"Metrics are aggregated over multiple random seeds per evaluated cell with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml18-results\", \"requirements\": \"Results quantitatively address H1/H2/H3 directionally with a clear narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml18-result-h1\", \"requirements\": \"The submission computes relative ECE change versus uncalibrated and conveys whether at least one calibrator yields a meaningful ECE reduction across most classifier/dataset pairs — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml18-result-h2\", \"requirements\": \"The submission compares isotonic vs Platt mean ECE per dataset and conveys the relative calibration quality plus any calibration-set size considerations (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml18-result-h3\", \"requirements\": \"The submission reports accuracy deltas between calibrated and uncalibrated outputs per classifier-dataset pair and conveys whether accuracy changes are small (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml18-result-writeup\", \"requirements\": \"The README or report conveys per-hypothesis outcomes (supported / refuted / inconclusive), cites key ECE/log-loss/accuracy numbers, and discusses limitations (dataset scope, binning sensitivity, seed count, calibration split size). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML18.yaml", "rubric_file": "tasks/ml/rubrics/ML18.json"} +{"id": "ML19", "domain": "ml", "title": "Comparing graph- and pseudo-label-based semi-supervised learning on small tabular datasets", "topic": "Comparing semi-supervised learning strategies (LabelPropagation, LabelSpreading, SelfTrainingClassifier) on small UCI datasets with 10-20% labeled data", "domains": ["machine-learning", "semi-supervised-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Semi-supervised learning is attractive when labels are scarce but unlabeled\nexamples are cheap. In sklearn, LabelPropagation and LabelSpreading provide\ngraph-based transductive approaches, while SelfTrainingClassifier offers a\npseudo-labeling wrapper around a standard supervised base learner. These\nmethods rely on different assumptions: graph smoothness over neighborhoods\nversus confidence-thresholded iterative self-labeling. On small tabular\ndatasets, their relative behavior can change quickly as the labeled fraction\ndrops from moderate (20%) to very low (10%).\n\nA useful CPU-scale study should compare these methods under the same splits,\nfeature preprocessing, and random seeds, with explicit control of labeled\nfraction. Because the key claim of semi-supervision is label efficiency, the\nexperiment should include a supervised-only baseline trained on the same\nlabeled subset and evaluated on a fixed held-out test set. This makes gains\nattributable to unlabeled-data usage rather than favorable splits.\n\nThe study should report not only test accuracy but also balanced accuracy and\nmacro-F1, since small tabular datasets can have class imbalance and accuracy\nalone may hide minority-class degradation. Repeating runs across several seeds\nis necessary because which points are labeled strongly affects outcomes at low\nlabel rates.\n\nPractically, the benchmark should stay lightweight: sklearn datasets only,\nno downloads, and model choices that complete within minutes on a single CPU\ncore. The focus is not SOTA performance but robust relative comparisons across\nlabel regimes and datasets.\n\n*How do LabelPropagation, LabelSpreading, and SelfTrainingClassifier compare in label-efficiency versus a supervised-only baseline when only 10–20% of training labels are available on small sklearn tabular datasets?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At 10% labeled data, at least one semi-supervised method (LabelPropagation, LabelSpreading, or SelfTrainingClassifier) achieves test accuracy at least 3 percentage points higher than the supervised-only baseline on at least 2 of 3 datasets, averaged over ≥5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Across datasets at 10% labeled data, LabelSpreading with RBF kernel attains mean test accuracy greater than or equal to LabelPropagation with RBF kernel in at least 2 of 3 datasets (seed-averaged).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"For SelfTrainingClassifier, increasing labeled fraction from 10% to 20% improves mean test accuracy by at least 1 absolute percentage point on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do graph-based (LabelPropagation/LabelSpreading) and pseudo-labeling (SelfTrainingClassifier) methods compare to supervised-only training under 10% and 20% labeled-data regimes on small tabular benchmarks?\", \"conditions\": [{\"name\": \"supervised_logreg_labeled_only\", \"description\": \"LogisticRegression trained only on the labeled subset of the training split; unlabeled points ignored.\"}, {\"name\": \"label_propagation_rbf\", \"description\": \"LabelPropagation with RBF kernel; unlabeled training labels set to -1 and transductive predictions used for test inference.\"}, {\"name\": \"label_spreading_rbf\", \"description\": \"LabelSpreading with RBF kernel under the same masked-label protocol.\"}, {\"name\": \"self_training_logreg\", \"description\": \"SelfTrainingClassifier wrapping LogisticRegression with confidence threshold (e.g., 0.8), fit on mixed labeled/unlabeled training data.\"}], \"baselines\": [\"supervised_logreg_labeled_only is the primary baseline\", \"10% labeled regime serves as a lower-label baseline versus 20% within each method\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Classification accuracy on held-out test split, averaged over seeds.\"}, {\"name\": \"balanced_accuracy\", \"direction\": \"maximize\", \"description\": \"Balanced accuracy on held-out test split, averaged over seeds.\"}, {\"name\": \"macro_f1\", \"direction\": \"maximize\", \"description\": \"Macro-averaged F1 score on held-out test split, averaged over seeds.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"digits\", \"source\": \"sklearn.datasets.load_digits\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml19-root\", \"requirements\": \"A credible experiment comparing semi-supervised methods (LabelPropagation, LabelSpreading, SelfTrainingClassifier, or equivalents) against a supervised-only baseline under limited-label regimes: methods are implemented correctly, runs cover multiple datasets with repeated seeds, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated alternative semi-supervised methods that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml19-code\", \"requirements\": \"Semi-supervised and baseline conditions are implemented correctly with explicit label masking.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml19-code-methods\", \"requirements\": \"The submission implements multiple distinct conditions — typically including at least one graph-based method (LabelPropagation or LabelSpreading), a self-training method, and a supervised-only baseline — as separate code paths.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml19-code-masking\", \"requirements\": \"Label masking for semi-supervised runs is explicit and correct: only a defined labeled fraction retains class labels and the remaining training labels are set to the unlabeled marker.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml19-code-datasets\", \"requirements\": \"The submission uses multiple datasets (sklearn built-ins or comparable), with a held-out test split and consistent preprocessing across conditions.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml19-exec\", \"requirements\": \"Execution covers multiple label fractions and produces benchmark metrics.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml19-exec-metrics\", \"requirements\": \"Execution outputs numeric test accuracy and at least one additional metric (e.g., balanced accuracy or macro-F1) for each implemented (condition, dataset, label-fraction) cell in a machine-readable artifact.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml19-exec-seeds\", \"requirements\": \"Each reported cell is aggregated over multiple random seeds with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml19-results\", \"requirements\": \"Results address H1/H2/H3 directionally with quantitative comparisons.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml19-result-h1\", \"requirements\": \"The submission compares each semi-supervised method versus the supervised-only baseline at a low labeled fraction and conveys whether any method provides a meaningful gain on most datasets — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml19-result-h2\", \"requirements\": \"The submission reports a direct LabelSpreading vs LabelPropagation comparison per dataset and conveys whether LabelSpreading is at least competitive on most datasets (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml19-result-h3\", \"requirements\": \"The submission reports self-training accuracy at both low and higher labeled fractions and conveys whether the gain with more labels is meaningful (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml19-result-writeup\", \"requirements\": \"The README or writeup describes setup and methods, reports per-dataset metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (dataset scope, seed count variance, sensitivity to masking/hyperparameters). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML19.yaml", "rubric_file": "tasks/ml/rubrics/ML19.json"} +{"id": "ML20", "domain": "ml", "title": "Classical forecaster robustness on synthetic seasonal time series", "topic": "Benchmarking classical time-series forecasters (AR, ARIMA, SARIMAX, ETS, Theta) on synthetic seasonal series with varying noise, trend, and seasonality amplitudes", "domains": ["time-series", "forecasting"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "smape", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 720, "synthesis": "Classical univariate forecasting methods remain widely used because they are\ninterpretable and lightweight, yet their comparative behavior can flip under\ndifferent data-generating regimes. AR models often perform well when dynamics\nare mostly autoregressive and weakly seasonal; ARIMA can absorb trend and\ndifferencing structure; SARIMAX can explicitly represent seasonal lag effects;\nETS captures error-trend-seasonal decomposition; and Theta is often a strong\nlow-variance baseline. In small CPU-constrained settings, practitioners need\npractical guidance about which method is robust to changing signal-to-noise\nand seasonality strength.\n\nA synthetic benchmark is appropriate here because we can precisely control\ntrend slope, seasonal amplitude, and observation noise while keeping compute\nmodest. By generating multiple families of monthly-like series with known\nperiodicity (e.g., period 12), we can evaluate whether model rankings are\nstable or regime-dependent. This avoids overfitting conclusions to one\nreal-world dataset and enables direct stress testing under high-noise versus\nhigh-seasonality conditions.\n\nA credible study should compare at least four of {AR, ARIMA, SARIMAX, ETS,\nTheta} on 2–3 synthetic dataset families, using rolling-origin or holdout\nforecasts with a fixed horizon. It should report scale-robust error metrics\n(especially sMAPE), include at least one baseline (e.g., seasonal naive), and\naggregate over multiple random seeds. The analysis should explicitly test\nwhether one method is consistently best overall or whether the best choice\ndepends on data regime.\n\nThe key outcome is actionable selection logic: if seasonal structure is strong\nand noise is low, do seasonal/state-space methods dominate; and if noise is\nhigh, do simpler methods become competitive? These are concrete decisions an\nautonomous forecasting pipeline can apply when only limited data and CPU are\navailable.\n\n*How do AR, ARIMA, SARIMAX, ETS, and Theta compare in sMAPE robustness across synthetic seasonal series with controlled trend, seasonality amplitude, and noise levels?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"SARIMAX or ETS achieves the lowest mean sMAPE on at least 2 of 3 synthetic dataset families when seasonality amplitude is high (amplitude >= 8) and noise is low (sigma <= 1.0), averaged over >=5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Under high-noise settings (sigma >= 3.0), the sMAPE gap between the best advanced method (ARIMA/SARIMAX/ETS/Theta) and a seasonal_naive baseline is <= 5 percentage points on at least 2 of 3 dataset families.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"No single method among {AR, ARIMA, SARIMAX, ETS, Theta} ranks first in mean sMAPE on all evaluated dataset families, indicating regime-dependent winners.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which classical forecasting method is most robust in sMAPE across controlled synthetic regimes of trend, seasonality amplitude, and noise?\", \"conditions\": [{\"name\": \"ar_lag12\", \"description\": \"Autoregressive model with lag order selected from {3,6,12} by AIC on training split.\"}, {\"name\": \"arima_auto_small\", \"description\": \"Non-seasonal ARIMA with (p,d,q) searched over small grid p,q in [0,2], d in [0,1], selected by AIC.\"}, {\"name\": \"sarimax_seasonal12\", \"description\": \"Seasonal ARIMA/SARIMAX with seasonal period 12 and small grid over (p,d,q)x(P,D,Q), selected by AIC.\"}, {\"name\": \"ets_additive\", \"description\": \"Exponential smoothing with additive trend/seasonality (period 12), damped trend optional by AIC.\"}, {\"name\": \"theta\", \"description\": \"ThetaModel forecast with default theta decomposition for univariate series.\"}], \"baselines\": [\"seasonal_naive (forecast y_t = y_{t-12}) as a simple seasonal baseline\", \"naive_last (random walk) as a non-seasonal baseline\"], \"metrics\": [{\"name\": \"smape\", \"direction\": \"minimize\", \"description\": \"Symmetric Mean Absolute Percentage Error on forecast horizon, averaged over seeds and series instances.\"}, {\"name\": \"mae\", \"direction\": \"minimize\", \"description\": \"Mean Absolute Error on forecast horizon.\"}, {\"name\": \"fit_time_sec\", \"direction\": \"minimize\", \"description\": \"Per-method wall-clock fit+forecast runtime in seconds.\"}], \"datasets\": [{\"name\": \"syn_low_noise_high_season\", \"source\": \"Synthetic monthly series: length 180, period 12, trend slope in [0.0,0.2], season amplitude in [8,12], gaussian noise sigma in [0.5,1.0].\"}, {\"name\": \"syn_medium_noise_medium_season\", \"source\": \"Synthetic monthly series: length 180, period 12, trend slope in [0.1,0.4], season amplitude in [4,8], gaussian noise sigma in [1.5,2.5].\"}, {\"name\": \"syn_high_noise_low_season\", \"source\": \"Synthetic monthly series: length 180, period 12, trend slope in [0.0,0.3], season amplitude in [1,4], gaussian noise sigma in [3.0,4.0].\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 720}}", "requirements": "", "rubric": "{\"id\": \"ml20-root\", \"requirements\": \"A credible experiment benchmarking classical forecasters (AR, ARIMA, SARIMAX, ETS, Theta, or equivalents) on synthetic seasonal series: model conditions are implemented, execution covers multiple synthetic regimes with repeated seeds, and results address H1/H2/H3 directionally using sMAPE-centered analysis.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative forecaster implementations or substitutes that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml20-code\", \"requirements\": \"The forecasting conditions and synthetic data generation pipeline are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml20-code-methods\", \"requirements\": \"The submission implements multiple distinct forecasting methods — typically AR/ARIMA, a seasonal model (SARIMAX or ETS), and Theta or equivalents — as separate code paths, and includes at least one explicit naive/seasonal-naive baseline.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml20-code-synthdata\", \"requirements\": \"The submission generates multiple synthetic seasonal dataset families with controllable seasonality, trend, and noise, each with a train/test split suitable for forecasting.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml20-code-setup\", \"requirements\": \"Forecasting setup uses a fixed holdout horizon (or rolling-origin equivalent) and computes comparable forecasts for every (method, dataset, seed) cell.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml20-exec\", \"requirements\": \"Benchmark execution logs required metrics with repeated trials.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml20-exec-metrics\", \"requirements\": \"Execution produces a structured metrics artifact containing numeric sMAPE and MAE (or equivalents) for each implemented method on at least one dataset family.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml20-exec-seeds\", \"requirements\": \"Each reported method-dataset metric is aggregated over multiple random seeds with dispersion statistics. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml20-exec-runtime\", \"requirements\": \"The run logs per-method wall-clock timing and completes within a CPU-only budget.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml20-results\", \"requirements\": \"Results analysis addresses H1/H2/H3 directionally with quantitative comparisons.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml20-result-h1\", \"requirements\": \"The submission isolates high-seasonality low-noise results and conveys whether SARIMAX or ETS tends to attain the best mean sMAPE — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml20-result-h2\", \"requirements\": \"The submission compares the best advanced method against seasonal-naive under high-noise settings and conveys whether the sMAPE gap collapses to a small margin (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml20-result-h3\", \"requirements\": \"The submission ranks methods by mean sMAPE per dataset family and conveys whether no single method dominates across all families (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml20-result-writeup\", \"requirements\": \"The README or writeup reports key metric tables, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (synthetic realism, grid-search scope, seed count, horizon sensitivity). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML20.yaml", "rubric_file": "tasks/ml/rubrics/ML20.json"} +{"id": "ML21", "domain": "ml", "title": "PC vs GES vs NOTEARS-linear for recovering small Gaussian linear-SEM DAGs", "topic": "Comparing causal structure learning algorithms (PC, GES, NOTEARS-linear) on small synthetic linear-SEM DAGs of 5-15 nodes with Gaussian noise", "domains": ["causal-inference", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "structural_hamming_distance", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 720, "synthesis": "Causal structure learning methods often claim asymptotic guarantees, but in\npractical settings researchers usually face small graphs, finite samples, and\nimperfect hyperparameter choices. For Gaussian linear structural equation\nmodels (SEMs), three widely discussed paradigms are constraint-based (PC),\nscore-based (GES), and continuous optimization (NOTEARS-linear). Even when\ndata are generated from the model class these methods assume, finite-sample\nbehavior can differ sharply in edge recovery and orientation errors.\n\nA compact CPU-scale benchmark can isolate these differences by generating\nsynthetic DAGs with known ground truth over 5-15 nodes, sampling linear-Gaussian\nobservations, and evaluating structural recovery against the true adjacency.\nThis avoids data-download overhead and lets the study vary sample size and\ngraph density in a controlled way. The key metric is Structural Hamming\nDistance (SHD), which penalizes missing, extra, and wrongly oriented edges.\n\nA credible study should implement all three algorithms with transparent\nassumptions: PC via partial-correlation CI tests, GES via greedy score search\n(e.g., BIC), and NOTEARS-linear via a differentiable acyclicity penalty and\nL1 regularization. Multiple random seeds and at least two synthetic regimes\n(sparser/easier vs denser/harder) are needed to reduce variance and avoid\nover-interpreting one-off runs.\n\nBecause these algorithms expose different tuning knobs (significance level,\nregularization strength, score penalties), the comparison should include one\nclear default setting per method plus modest tuning on validation seeds. The\nresulting analysis should report SHD and at least one precision/recall-style\nedge metric, then map outcomes back to explicit hypotheses about relative\nranking and sample-efficiency.\n\n*On small Gaussian linear-SEM DAGs, which of PC, GES, and NOTEARS-linear most reliably minimizes structural recovery error under realistic finite-sample budgets?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At n_samples=1000, NOTEARS-linear achieves lower mean SHD than PC on at least 2 of 3 synthetic DAG regimes (averaged over >=5 random DAG/data seeds per regime).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At n_samples=300, GES achieves mean SHD <= PC in all evaluated regimes, and strictly lower SHD in at least 1 regime.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The best method at n_samples=1000 reduces mean SHD by at least 20% relative to the worst method in at least 2 of 3 regimes.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"For 5-15 node Gaussian linear-SEM DAGs, how do PC, GES, and NOTEARS-linear compare in SHD and edge recovery across sparse/medium/dense synthetic regimes and finite sample sizes?\", \"conditions\": [{\"name\": \"pc_partial_corr\", \"description\": \"Constraint-based PC using Gaussian partial-correlation conditional independence tests with significance level alpha (default 0.01, optional small grid).\"}, {\"name\": \"ges_bic\", \"description\": \"Score-based greedy equivalence search using Gaussian/BIC score with forward-backward edge operations.\"}, {\"name\": \"notears_linear_l1\", \"description\": \"Continuous optimization NOTEARS-linear with squared loss, acyclicity constraint, and L1 sparsity penalty tuned on a small lambda grid.\"}, {\"name\": \"pc_alpha_tuned\", \"description\": \"PC variant selecting alpha from {0.005, 0.01, 0.05} by lowest validation-seed SHD, then evaluated on held-out seeds.\"}], \"baselines\": [\"pc_partial_corr is the classical constraint-based baseline\", \"ges_bic is the classical score-based baseline\"], \"metrics\": [{\"name\": \"shd\", \"direction\": \"minimize\", \"description\": \"Structural Hamming Distance between learned and true DAG adjacency (lower is better), averaged over seeds.\"}, {\"name\": \"edge_f1\", \"direction\": \"maximize\", \"description\": \"F1 score on directed edge existence/orientation against ground truth adjacency.\"}, {\"name\": \"runtime_sec\", \"direction\": \"minimize\", \"description\": \"Per-run wall-clock time in seconds to assess CPU practicality.\"}], \"datasets\": [{\"name\": \"synth_linear_sem_sparse\", \"source\": \"Synthetic 8-node DAGs generated via Erdos-Renyi with expected degree ~1.5; linear weights sampled from [-2,-0.5] U [0.5,2], Gaussian noise.\"}, {\"name\": \"synth_linear_sem_medium\", \"source\": \"Synthetic 12-node DAGs with expected degree ~2.5 using same linear-Gaussian SEM generator.\"}, {\"name\": \"synth_linear_sem_dense\", \"source\": \"Synthetic 15-node DAGs with expected degree ~3.5 using same linear-Gaussian SEM generator.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 720}}", "requirements": "", "rubric": "{\"id\": \"ml21-root\", \"requirements\": \"A credible experiment comparing causal structure learning methods (PC, GES, NOTEARS-linear, or equivalents) on small synthetic Gaussian linear-SEM DAGs: methods are implemented, runs cover multiple synthetic regimes with multiple seeds, and results address H1/H2/H3 directionally using SHD-centered evidence.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative structure-learning methods or linear-SEM variants that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml21-code\", \"requirements\": \"Core causal structure learning conditions and synthetic data generation are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml21-code-methods\", \"requirements\": \"The submission implements distinct code paths for multiple structure learning methods — typically PC, GES, and NOTEARS-linear or equivalents — rather than reusing one estimator under different names.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml21-code-synth\", \"requirements\": \"A reproducible synthetic linear-Gaussian SEM DAG generator is implemented with moderate node counts, including ground-truth adjacency export and sample generation.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml21-code-hparams\", \"requirements\": \"At least one tunable hyperparameter is exposed and used for the constraint-based method (e.g., PC's alpha) and the continuous-optimization method (e.g., NOTEARS' sparsity weight), with documented default or grid values.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml21-exec\", \"requirements\": \"The benchmark produces comparable metrics across methods/regimes.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml21-exec-coverage\", \"requirements\": \"Execution covers multiple synthetic regimes and reports results for the primary methods at one or more sample sizes.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml21-exec-metrics\", \"requirements\": \"A machine-readable results artifact includes numeric SHD for each (method, regime) and at least one additional metric such as edge-F1 or runtime.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml21-exec-seeds\", \"requirements\": \"Each reported cell is averaged over multiple random seeds with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml21-results\", \"requirements\": \"Results analysis addresses H1/H2/H3 directionally.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml21-result-h1\", \"requirements\": \"The submission compares NOTEARS-linear vs PC at a reasonable sample size and conveys whether NOTEARS achieves meaningfully lower SHD on most regimes — judge directionally against H1.\", \"weight\": 25.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml21-result-h2h3\", \"requirements\": \"The submission conveys qualitative verdicts for H2 and H3 using SHD aggregates, including whether larger sample sizes or denser-graph regimes yield meaningful SHD improvements.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml21-result-writeup\", \"requirements\": \"The README or writeup describes setup, reports key SHD/edge-F1/runtime findings, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (synthetic-only scope, small-node regime, hyperparameter sensitivity, finite seeds). No strict word-count requirement.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML21.yaml", "rubric_file": "tasks/ml/rubrics/ML21.json"} +{"id": "ML22", "domain": "ml", "title": "Active learning query strategies for logistic regression on small tabular pools", "topic": "Evaluating active learning query strategies (uncertainty sampling, margin sampling, query-by-committee, expected-error reduction) for logistic regression on small UCI pools", "domains": ["machine-learning", "active-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "accuracy_at_budget", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 600, "synthesis": "Pool-based active learning is attractive when labels are expensive but a\nmodest unlabeled pool is available. For linear probabilistic models such as\nlogistic regression, classic query policies include uncertainty sampling,\nmargin sampling, query-by-committee (QBC), and expected-error reduction\nstyle lookahead approximations. These methods are often taught as broadly\nuseful, but on small tabular datasets their gains can be inconsistent due to\nmodel misspecification, class imbalance, and high variance from tiny initial\nlabeled sets.\n\nA useful CPU-bounded benchmark should compare these query strategies under a\nfixed annotation budget and shared learner, rather than mixing in model\narchitecture changes. The core signal is label efficiency: how quickly test\nperformance improves as labels are acquired. Because final accuracy alone can\nhide early-budget differences, the study should include both\naccuracy-at-budget and a budget-curve summary metric such as area under the\nlearning curve.\n\nThe experiment should therefore run multiple seeds, use at least two\nsklearn-resident tabular classification datasets, and enforce identical\ninitialization, batch size, and stopping budget across strategies. A random\nquery baseline is essential to determine whether sophisticated policies\nprovide real value beyond chance selection. A passive full-data reference is\nalso useful for contextualizing attainable performance ceilings under the\nsame logistic-regression family.\n\nPractical constraints matter: expected-error reduction can be expensive if\nnaively recomputing retrains for every candidate. A tractable variant can\nevaluate a capped candidate subset per round and use one-step hypothetical\nlabel outcomes, preserving the spirit of expected future loss minimization\nwhile staying within single-core runtime limits.\n\n*Do uncertainty, margin, QBC, and approximate expected-error reduction deliver better label efficiency than random sampling for logistic regression on small tabular pools under a fixed annotation budget?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At 30% labeling budget, at least 2 of {uncertainty_sampling, margin_sampling, qbc, expected_error_reduction} achieve higher mean test accuracy than random_sampling by ≥1.5 percentage points, averaged over ≥5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"expected_error_reduction attains the highest mean area_under_learning_curve (AULC) on at least 1 of 3 datasets, and its AULC is not lower than random_sampling on any evaluated dataset.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"qbc outperforms uncertainty_sampling in mean test accuracy at early budget (10% labels) on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which active learning query strategy provides the best label efficiency for logistic regression on small tabular pool-based classification tasks under fixed budgets?\", \"conditions\": [{\"name\": \"random_sampling\", \"description\": \"Baseline pool-based active learning that queries unlabeled points uniformly at random each round.\"}, {\"name\": \"uncertainty_sampling\", \"description\": \"Queries samples with highest predictive entropy from current logistic regression model.\"}, {\"name\": \"margin_sampling\", \"description\": \"Queries samples with smallest top-2 class probability margin (binary: |p-0.5|).\"}, {\"name\": \"qbc\", \"description\": \"Query-by-committee with 5 bootstrapped logistic regression committee members; selects by vote entropy/disagreement.\"}, {\"name\": \"expected_error_reduction\", \"description\": \"Approximate one-step expected-error reduction over a capped candidate subset per round using hypothetical labels and expected future log-loss reduction.\"}], \"baselines\": [\"random_sampling is the primary active-learning baseline\", \"passive_full_data logistic regression reference trained once on all labels for context\"], \"metrics\": [{\"name\": \"accuracy_at_budget\", \"direction\": \"maximize\", \"description\": \"Test accuracy at 30% labeled budget, averaged over seeds.\"}, {\"name\": \"aulc\", \"direction\": \"maximize\", \"description\": \"Area under the test-accuracy-vs-labeled-fraction curve from initial seed set to 30% budget.\"}, {\"name\": \"early_accuracy_10pct\", \"direction\": \"maximize\", \"description\": \"Test accuracy when 10% of pool labels have been acquired.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"digits\", \"source\": \"sklearn.datasets.load_digits\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 600}}", "requirements": "", "rubric": "{\"id\": \"ml22-root\", \"requirements\": \"A credible experiment studying active-learning query strategies (random, uncertainty, margin, QBC, expected error reduction, or equivalents) for logistic regression: strategies are implemented as distinct code paths, execution covers multiple datasets with repeated seeds under a fixed budget, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative acquisition functions or base classifiers that preserve the scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml22-code\", \"requirements\": \"Active-learning strategies and shared logistic-regression pipeline are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml22-code-strategies\", \"requirements\": \"The submission implements multiple distinct query conditions — typically random plus several of {uncertainty, margin, QBC, expected error reduction} — with genuinely different acquisition logic.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml22-code-loop\", \"requirements\": \"There is a pool-based active-learning loop with an initial labeled seed set, iterative querying, model retraining/update, and stopping at a defined label budget.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml22-code-datasets\", \"requirements\": \"The submission uses multiple datasets (sklearn built-ins or comparable) with consistent train/pool/test handling for all strategies.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml22-exec\", \"requirements\": \"Execution emits budget-aware performance metrics.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml22-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact containing numeric accuracy-at-budget and area-under-learning-curve (or equivalents) for each implemented strategy on at least one dataset.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml22-exec-seeds\", \"requirements\": \"Each reported (strategy, dataset) result is aggregated over multiple random seeds, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml22-exec-budget-curve\", \"requirements\": \"The run logs performance across multiple budget checkpoints so early-budget accuracy and AULC are computable from recorded traces.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml22-results\", \"requirements\": \"Quantitative analysis addresses H1/H2/H3 directionally.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml22-result-h1\", \"requirements\": \"The submission compares non-random strategies to random sampling at the final budget and conveys whether informative strategies meaningfully outperform random — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml22-result-h2\", \"requirements\": \"The submission reports per-dataset AULC rankings and conveys whether expected-error-reduction (or a comparable principled strategy) is at least competitive, never falling below random (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml22-result-h3\", \"requirements\": \"The submission compares QBC vs uncertainty sampling at an early-budget checkpoint per dataset and conveys a qualitative verdict (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml22-result-writeup\", \"requirements\": \"The README or report describes setup, reports key numeric outcomes, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (runtime approximations, seed count, dataset scope, strategy sensitivity). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML22.yaml", "rubric_file": "tasks/ml/rubrics/ML22.json"} +{"id": "ML23", "domain": "ml", "title": "Pointwise vs pairwise vs listwise learning-to-rank on synthetic query-document benchmarks", "topic": "Comparing learning-to-rank approaches (pointwise linear, pairwise RankNet-lite, listwise ListMLE-lite) on synthetic query-document pairs with known relevance grades", "domains": ["information-retrieval", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "ndcg_at_10", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 480, "synthesis": "Learning-to-rank (LTR) objectives are often grouped into pointwise, pairwise,\nand listwise families, each optimizing a different surrogate of retrieval\nquality. Pointwise methods reduce ranking to regression/classification on\nindividual query-document pairs; pairwise methods optimize relative order\nbetween document pairs; listwise methods directly shape permutations or top-k\nemphasis. In production IR systems, metric alignment (especially with NDCG)\nis often cited as a reason to prefer pairwise/listwise objectives, but this\nclaim is difficult to test cleanly on public collections with many confounds.\n\nA compact synthetic benchmark with known latent relevance structure allows a\ncontrolled comparison. If query-specific utility functions generate graded\nrelevance labels (0-4), we can produce query groups with explicit train/test\nsplits and evaluate whether objectives aligned with ranking structure obtain\nbetter NDCG@10 than a strong linear pointwise baseline. Because the data are\nsynthetic, we can vary noise and query heterogeneity while keeping runtime low\nenough for single-core CPU execution.\n\nA credible study should implement three distinct training paradigms in the\nsame feature space: (1) pointwise linear regression on grades, (2) a\nRankNet-lite pairwise logistic objective over within-query document pairs,\nand (3) a ListMLE-lite listwise objective using per-query permutations sorted\nby true grades. Evaluation should report NDCG@10 as primary, plus at least one\nsecondary ranking metric and a sanity metric such as training loss. Results\nshould be averaged across multiple seeds and at least two synthetic dataset\nregimes.\n\nThe key scientific goal is not reproducing a specific paper, but testing\nwhether increasing objective-level ranking awareness yields measurable gains\nin top-k ranking quality under controlled conditions and limited compute.\n\n*Do pairwise and listwise lightweight objectives consistently outperform a pointwise linear baseline on NDCG@10 across synthetic query-document datasets with graded relevance?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"ListMLE-lite achieves higher mean NDCG@10 than pointwise linear regression on at least 2 of 3 synthetic datasets, with an absolute improvement of at least 0.03 on each winning dataset (averaged over >=3 seeds).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At least one of {RankNet-lite, ListMLE-lite} outperforms pointwise linear regression in mean NDCG@10 on all evaluated datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The best ranking-aware method (max of RankNet-lite and ListMLE-lite per dataset) improves mean MAP@10 over pointwise linear by at least 0.02 on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Do pairwise/listwise lightweight ranking objectives provide consistent top-k retrieval gains over a pointwise linear baseline on synthetic grouped query-document data?\", \"conditions\": [{\"name\": \"pointwise_linear\", \"description\": \"Linear model trained pointwise on query-document feature vectors to predict graded relevance via squared error; ranking by predicted score within each query.\"}, {\"name\": \"ranknet_lite\", \"description\": \"Pairwise logistic ranking objective on within-query document pairs using a linear scorer; optimized with mini-batch gradient descent in numpy.\"}, {\"name\": \"listmle_lite\", \"description\": \"Listwise ListMLE-style objective with a linear scorer, optimizing likelihood of ground-truth relevance-sorted document permutations per query.\"}, {\"name\": \"pointwise_ridge_tuned\", \"description\": \"Pointwise linear baseline with L2 regularization tuned over a small grid to ensure a competitive non-ranking-aware baseline.\"}], \"baselines\": [\"pointwise_linear is the primary baseline\", \"pointwise_ridge_tuned is a strengthened linear baseline\"], \"metrics\": [{\"name\": \"ndcg_at_10\", \"direction\": \"maximize\", \"description\": \"Mean NDCG@10 across test queries; primary metric.\"}, {\"name\": \"map_at_10\", \"direction\": \"maximize\", \"description\": \"Mean Average Precision@10 across test queries using relevance>=1 as relevant.\"}, {\"name\": \"pairwise_accuracy\", \"direction\": \"maximize\", \"description\": \"Fraction of correctly ordered document pairs within test queries (based on graded relevance order).\"}], \"datasets\": [{\"name\": \"synth_ltr_easy\", \"source\": \"Synthetic generator: 80 queries x 25 docs/query, 20 dense features, low noise, relevance grades 0-4 from latent linear utility.\"}, {\"name\": \"synth_ltr_noisy\", \"source\": \"Synthetic generator: 100 queries x 30 docs/query, 25 features, higher Gaussian noise and feature corruption, grades 0-4.\"}, {\"name\": \"synth_ltr_sparse\", \"source\": \"Synthetic generator: 90 queries x 20 docs/query, 40 features with sparsity mask, query-specific weight drift, grades 0-4.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 480}}", "requirements": "", "rubric": "{\"id\": \"ml23-root\", \"requirements\": \"A credible experiment comparing pointwise, pairwise (RankNet-style), and listwise (ListMLE-style) ranking approaches on synthetic query-document datasets: methods are implemented as distinct objectives, execution reports ranking metrics on multiple datasets with multiple seeds, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated ranking-objective substitutes (e.g., LambdaRank-style, softrank) that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml23-code\", \"requirements\": \"The ranking methods and synthetic grouped datasets are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml23-code-methods\", \"requirements\": \"The submission implements multiple distinct conditions — typically a pointwise baseline plus at least one pairwise and one listwise method — with genuinely different objective computations, not merely different hyperparameters of one loss.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml23-code-data\", \"requirements\": \"A synthetic query-document generator is implemented with grouped queries, per-query document lists, and graded relevance labels, and multiple dataset regimes are instantiated.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml23-code-setup\", \"requirements\": \"The experimental setup includes query-level train/test splitting (no leakage of documents from the same query across splits) and a shared scoring-function interface across methods.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml23-exec\", \"requirements\": \"Execution logs ranking metrics per method and dataset.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml23-exec-metrics\", \"requirements\": \"Execution produces a metrics artifact containing numeric NDCG@k and MAP@k (or equivalents) for each implemented condition on at least one dataset.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml23-exec-seeds\", \"requirements\": \"Reported metrics are averaged over multiple random seeds per (condition, dataset) cell with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml23-exec-tuning\", \"requirements\": \"At least one non-trivial hyperparameter search or documented default is executed (e.g., learning rate/regularization) and the chosen value is logged.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml23-results\", \"requirements\": \"Results analysis addresses H1/H2/H3 directionally.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml23-result-h1\", \"requirements\": \"The submission compares the listwise method vs the pointwise baseline in mean NDCG@k per dataset and conveys whether listwise meaningfully improves ranking quality — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml23-result-h2h3\", \"requirements\": \"The submission conveys whether at least one ranking-aware method beats the pointwise baseline on NDCG across all datasets (H2) and whether the best ranking-aware method yields a meaningful MAP improvement on most datasets (H3).\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml23-result-writeup\", \"requirements\": \"The README or writeup describes methods, datasets, and metric outcomes; conveys per-hypothesis outcomes (supported / refuted / inconclusive); and discusses limitations (synthetic-data realism, scorer capacity, seed count, metric sensitivity). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML23.yaml", "rubric_file": "tasks/ml/rubrics/ML23.json"} +{"id": "ML24", "domain": "ml", "title": "Online binary classification under concept drift: SGD, PA, NB, and FTRL", "topic": "Benchmarking online learning algorithms (SGD logistic, Passive-Aggressive, online Naive Bayes, Follow-the-Regularized-Leader) on streaming binary classification with concept drift", "domains": ["online-learning", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "prequential_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "In streaming classification, data arrive sequentially and model updates must\nbe cheap and immediate. Under concept drift, the relationship between\nfeatures and labels changes over time, so static train/test evaluation can\nbe misleading. Prequential (test-then-train) evaluation better reflects\ndeployment: each incoming point is first predicted, then used for an update.\nThis setting creates practical trade-offs among stability, adaptability, and\ncomputational cost.\n\nSeveral lightweight online learners in sklearn-style workflows represent\ndifferent adaptation biases. SGD logistic regression can track drift via\ngradient updates but may require careful learning-rate choices. Passive-\nAggressive updates can react strongly to mistakes and often adapt quickly to\nabrupt shifts. Online Naive Bayes is extremely cheap and robust but may lag\nwhen feature dependence structure changes. A simple FTRL-style proximal\nlogistic update (implemented with diagonal accumulators) offers adaptive\nper-feature learning rates and implicit regularization.\n\nA credible CPU-only benchmark should compare these methods on at least one\nsynthetic abrupt-drift stream and one real sklearn dataset converted to a\nstream with induced drift (e.g., blockwise class-prior or feature transform\nshift). It should report prequential accuracy as the primary metric, plus at\nleast one drift-sensitive secondary metric such as post-drift recovery and\ncumulative log loss. Multi-seed runs are important because stream order and\ndrift-point randomness can materially change outcomes.\n\nThe study should also verify that drift actually hurts models by comparing a\nno-drift control stream against drifted streams, and should discuss which\nalgorithms recover fastest after drift while maintaining overall accuracy.\nThis makes the benchmark more diagnostic than a single aggregate score.\n\n*Which lightweight online learner provides the best trade-off between overall prequential accuracy and adaptation speed after concept drift in binary streams?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On drifted streams, Passive-Aggressive or FTRL achieves higher prequential_accuracy than online Naive Bayes by at least 0.02 absolute on at least 2 of 3 datasets, averaged over >=5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"For at least 2 of 3 drifted datasets, the best post-drift recovery accuracy in a fixed window of 200 samples after each drift point is achieved by either Passive-Aggressive or FTRL.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"For SGD logistic and Passive-Aggressive, prequential_accuracy on a drifted version of each stream is at least 0.03 lower than on its matched no-drift control, on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which online method among SGD logistic, Passive-Aggressive, online Naive Bayes, and FTRL-style logistic best balances prequential accuracy and post-drift recovery on binary data streams with concept drift?\", \"conditions\": [{\"name\": \"sgd_logistic\", \"description\": \"SGDClassifier with log_loss and partial_fit in prequential test-then-train loop.\"}, {\"name\": \"passive_aggressive\", \"description\": \"PassiveAggressiveClassifier with partial_fit in the same loop.\"}, {\"name\": \"online_naive_bayes\", \"description\": \"GaussianNB updated incrementally via partial_fit.\"}, {\"name\": \"ftrl_prox_logistic\", \"description\": \"Custom numpy FTRL-proximal logistic regression with diagonal accumulator and L1/L2 regularization.\"}], \"baselines\": [\"online_naive_bayes as a simple low-cost baseline\", \"sgd_logistic as a standard linear online-learning baseline\"], \"metrics\": [{\"name\": \"prequential_accuracy\", \"direction\": \"maximize\", \"description\": \"Mean test-then-train accuracy over the full stream, averaged over 5 seeds.\"}, {\"name\": \"post_drift_recovery_accuracy\", \"direction\": \"maximize\", \"description\": \"Accuracy in the first 200 samples after each drift point, averaged across drift events and seeds.\"}, {\"name\": \"prequential_log_loss\", \"direction\": \"minimize\", \"description\": \"Cumulative mean log loss computed prequentially when probabilistic outputs are available (or clipped score-to-prob mapping).\"}], \"datasets\": [{\"name\": \"synthetic_abrupt_drift\", \"source\": \"numpy synthesis (piecewise make_classification with coefficient/sign flips at fixed indices)\"}, {\"name\": \"breast_cancer_stream\", \"source\": \"sklearn.datasets.load_breast_cancer transformed into a repeated/shuffled stream with blockwise feature scaling shift\"}, {\"name\": \"spambase_like_synthetic\", \"source\": \"numpy synthesis with sparse informative features and class-prior shift across blocks\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml24-root\", \"requirements\": \"A credible experiment studying online learners (SGD logistic, Passive-Aggressive, online Naive Bayes, FTRL-style logistic, or equivalents) under concept drift: streaming prequential setup is implemented, execution covers multiple datasets with multiple seeds, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated online-learner substitutes or drift-stream variants that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml24-code\", \"requirements\": \"Online-learning conditions and drifted streaming setup are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml24-code-methods\", \"requirements\": \"The submission implements multiple distinct online methods, each as a real incremental update path (e.g., partial_fit or equivalent), not batch retraining.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml24-code-streaming\", \"requirements\": \"A prequential test-then-train loop is implemented: each sample (or mini-batch) is predicted before being used for model update, with ordered stream processing.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml24-code-datasets\", \"requirements\": \"The submission uses multiple streams including at least one drifted stream and one no-drift or matched-control stream, generated from sklearn datasets or numpy synthesis.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml24-exec\", \"requirements\": \"Execution reports prequential metrics for each condition.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml24-exec-metrics\", \"requirements\": \"Execution produces a metrics artifact containing numeric prequential accuracy (or equivalent) for each implemented method on at least one dataset.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml24-exec-recovery\", \"requirements\": \"Execution includes a post-drift recovery measure (e.g., fixed-window post-drift accuracy) with a documented computation referencing known drift points.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml24-exec-seeds\", \"requirements\": \"Each reported (dataset, method) metric is averaged over multiple random seeds, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml24-results\", \"requirements\": \"Quantitative analysis addresses H1/H2/H3 directionally.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml24-result-h1\", \"requirements\": \"The submission compares prequential accuracy of margin-based online methods (Passive-Aggressive, FTRL) against online Naive Bayes across drifted datasets and conveys whether margin-based methods are meaningfully better — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml24-result-h2\", \"requirements\": \"The submission reports post-drift recovery comparisons and conveys whether Passive-Aggressive or FTRL tends to lead on most datasets (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml24-result-h3\", \"requirements\": \"The submission compares drifted vs no-drift control performance for the implemented methods and conveys whether drift produces a meaningful accuracy degradation (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml24-result-writeup\", \"requirements\": \"The README or writeup describes setup, key metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (stream realism, drift design choices, seed count, metric assumptions). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML24.yaml", "rubric_file": "tasks/ml/rubrics/ML24.json"} +{"id": "ML25", "domain": "ml", "title": "Reservoir computing vs MLP vs GP for short-horizon Lorenz-63 forecasting", "topic": "Testing whether a random-feature echo state network (reservoir computing) predicts Lorenz-63 short-horizon trajectories more accurately than a matched-parameter MLP and a Gaussian Process at small training-data sizes (N=200-2000 points)", "domains": ["dynamical-systems", "machine-learning", "reservoir-computing"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "valid_prediction_time", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 720, "synthesis": "Reservoir computing (a.k.a. echo state networks, ESN) is a lightweight\napproach for learning dynamical systems: a fixed random recurrent network\nprojects the input into a high-dimensional reservoir state, and only a\nlinear readout layer is trained. For chaotic systems, ESNs have repeatedly\nbeen reported to match or outperform trainable recurrent nets on\nshort-horizon trajectory prediction, despite training only a ridge\nregression on the readout.\n\nA credible CPU-scale study of this claim must compare (i) a reservoir\nnetwork with a random sparse internal matrix and fixed spectral radius,\n(ii) a parameter-matched fully-connected MLP that reads a fixed window of\npast states, and (iii) a Gaussian Process regressor with an RBF kernel on\nthe same windowed input. The target signal is Lorenz-63 integrated at\ndt=0.02 with the classical (σ=10, ρ=28, β=8/3) parameters. Training-set\nsizes N ∈ {200, 500, 1000, 2000} are small enough that the standard\nreservoir-computing folklore — \"ESNs win when data is scarce\" — can\nactually be tested rather than assumed.\n\nThe key dynamics-aware metric is the *valid prediction time* (VPT):\nthe first time step at which the normalized prediction error exceeds a\nthreshold (commonly 0.4 of the attractor standard deviation). Unlike\nper-step RMSE, VPT tracks how long a model's forecast remains useful on\nthe chaotic attractor. Reporting both RMSE and VPT reveals whether any\nobserved \"superiority\" is numerical or dynamical.\n\nThe research question is: *does a small reservoir (N_res ≤ 500 units) beat\na matched-parameter MLP and a Gaussian Process on valid prediction time\nfor Lorenz-63, and at which training-set size does the gap appear?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"An echo-state network with spectral radius ≈ 0.9 and ≤ 500 reservoir units achieves a longer mean Valid Prediction Time (VPT ≥ 1.2×) than a parameter-matched MLP on Lorenz-63 at training-set sizes N ≤ 500.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The Gaussian Process regressor is competitive with (VPT within ±20% of) the ESN at N=200 but is decisively beaten (ESN VPT at least 2× the GP's) at N=2000, reflecting the GP's O(N^3) training bottleneck and the ESN's ability to exploit more data without re-inverting a Gram matrix.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Per-step RMSE at one-step-ahead prediction is NOT a reliable proxy for VPT: at least one condition exists where model A has lower one-step RMSE than model B but a shorter VPT, showing that per-step error understates the divergence on chaotic attractors.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"On Lorenz-63 short-horizon forecasting, does a small echo-state network achieve a longer valid prediction time than a parameter-matched MLP and a Gaussian Process regressor, and how does the ranking depend on training-set size?\", \"conditions\": [{\"name\": \"esn_N200\", \"description\": \"Echo-state network with 300 reservoir units, spectral radius 0.9, input scaling 1.0, leak rate 0.3, ridge regression readout (alpha=1e-5). Trained on N=200 consecutive Lorenz-63 samples.\"}, {\"name\": \"esn_N500\", \"description\": \"Same ESN configuration, N=500 training samples.\"}, {\"name\": \"esn_N1000\", \"description\": \"Same ESN configuration, N=1000 training samples.\"}, {\"name\": \"esn_N2000\", \"description\": \"Same ESN configuration, N=2000 training samples.\"}, {\"name\": \"mlp_N{200,500,1000,2000}\", \"description\": \"Fully-connected MLP with 2 hidden layers whose total parameter count is within ±10% of the ESN readout parameter count. Reads a 5-step window of past (x,y,z) states as input. Adam optimizer, early stopping on a 10% validation split.\"}, {\"name\": \"gp_N{200,500,1000,2000}\", \"description\": \"Gaussian Process regressor with an RBF kernel and WhiteNoise term, hyperparameters optimised by marginal likelihood maximisation. Reads the same 5-step window as the MLP. At N=2000 the GP may time out or run out of memory; record the failure.\"}], \"baselines\": [\"Persistence baseline: predicted next state = previous state. Establishes a floor for VPT.\", \"Linear autoregressive baseline: one-step-ahead linear regression on the 5-step window. Establishes whether the non-linear methods' gains are real.\"], \"metrics\": [{\"name\": \"valid_prediction_time\", \"direction\": \"maximize\", \"description\": \"First forecast step (in units of integration dt) at which the normalized L2 error exceeds 0.4 × attractor std. Averaged over 10 random initial conditions on an independent test trajectory.\"}, {\"name\": \"one_step_rmse\", \"direction\": \"minimize\", \"description\": \"Root-mean-square error of one-step-ahead prediction on the test trajectory.\"}, {\"name\": \"train_wall_clock_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock time to fit the model, including hyperparameter optimisation where applicable.\"}, {\"name\": \"param_count\", \"direction\": \"report\", \"description\": \"Number of trainable parameters. Must be reported to demonstrate that the MLP is parameter-matched to the ESN.\"}], \"datasets\": [{\"name\": \"lorenz63_train\", \"source\": \"synthetic — integrate dxdt=σ(y-x), dy/dt=x(ρ-z)-y, dz/dt=xy-βz at dt=0.02 with σ=10, ρ=28, β=8/3. Use scipy.integrate.solve_ivp or a hand-rolled RK4.\"}, {\"name\": \"lorenz63_test\", \"source\": \"independent Lorenz-63 trajectory (different IC, 10× longer warmup) with 10 evaluation windows for VPT averaging.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 720}}", "requirements": "", "rubric": "{\"id\": \"ml25-root\", \"requirements\": \"A credible CPU-scale study of reservoir computing (echo-state network), a matched-parameter MLP, and a Gaussian Process on Lorenz-63 short-horizon forecasting: Lorenz-63 is integrated, the three model families are implemented, multiple training-set sizes are swept, valid-prediction-time is reported, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Reasonable substitutes (e.g., alternative chaotic system, library-based ESN when clearly documented, Matern-kernel GP) that test the same scientific question should be credited. A GP running out of memory at large N is a valid reported outcome.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml25-code\", \"requirements\": \"The three model families and the dynamical system are implemented.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml25-code-lorenz\", \"requirements\": \"The submission integrates the Lorenz-63 system with classical parameters at a small time step using scipy.integrate or a hand-rolled Runge-Kutta integrator, and uses separate train/test trajectories.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml25-code-esn\", \"requirements\": \"An echo-state network is implemented with a sparse random reservoir scaled to a target spectral radius below ~1.1, driven by the input sequence with a leak-rate update and a linear readout fit by ridge regression. A from-scratch implementation is preferred; a clearly documented library-based ESN that exposes the same mechanics is acceptable.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml25-code-mlp\", \"requirements\": \"An MLP is implemented that reads a fixed window of past states and predicts the next state, with its total parameter count reported and roughly matched to the ESN readout parameter count.\", \"weight\": 4.1667, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml25-code-gp\", \"requirements\": \"A Gaussian Process regressor with an RBF or Matern kernel is fit on the same windowed input, with marginal-likelihood hyperparameter optimisation enabled.\", \"weight\": 4.1667, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml25-exec\", \"requirements\": \"Execution produces per-method, per-N quantitative results.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml25-exec-sweep\", \"requirements\": \"The experiment sweeps multiple training-set sizes for each of the three methods and reports numeric results per cell.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml25-exec-vpt\", \"requirements\": \"A valid-prediction-time (VPT) metric is computed per method per N — the first step at which the normalised prediction error exceeds a threshold — averaged over multiple independent initial conditions or seeds.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml25-exec-rmse\", \"requirements\": \"A one-step-ahead RMSE (or equivalent pointwise error) is computed per method per N on the test trajectory and reported in the metrics file.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml25-results\", \"requirements\": \"The writeup addresses H1/H2/H3 directionally and contextualises numeric findings.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml25-result-h1\", \"requirements\": \"The submission reports VPT for the ESN vs MLP at small training sizes and conveys whether the ESN meaningfully outperforms the MLP in the small-N regime — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml25-result-h2\", \"requirements\": \"The submission compares ESN and GP VPT at both small-N and larger-N and conveys whether the GP is competitive at small N and falls behind (or becomes infeasible) at large N (H2). Reporting a GP memory/time failure at large N is a valid outcome.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml25-result-h3\", \"requirements\": \"The submission examines whether one-step RMSE ranking tracks VPT ranking across method-N cells and conveys whether at least one rank inversion is observed (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml25-result-writeup\", \"requirements\": \"The README or writeup describes the Lorenz-63 setup, the three methods, the N-sweep, the VPT and RMSE numbers, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (single dynamical system, no Lyapunov-time normalisation, GP memory cliff). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 11, "manifest_file": "tasks/ml/manifests/ML25.yaml", "rubric_file": "tasks/ml/rubrics/ML25.json"} +{"id": "P01", "domain": "physics", "title": "Reproducing the Randall-Sundrum KK-graviton resonance pattern in $e^+e^- \\to \\mu^+\\mu^-$", "topic": "Reproducing warped extra dimension Kaluza-Klein graviton resonance from arXiv:hep-ph/9909255", "domains": ["high-energy-physics", "bsm-phenomenology", "extra-dimensions"], "arxiv_id": "hep-ph/9909255", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the cross-section vs $\\sqrt{s}$ scan of $e^+e^- \\to \\mu^+\\mu^-$\nin the Randall-Sundrum warped extra dimension scenario as a benchmark of\nthe agent's ability to assemble a full Lagrangian -> MC -> analysis\npipeline. The model adds a tower of massive spin-2 KK gravitons\n$h^{(n)}_{\\alpha\\beta}$ ($n=1,\\ldots,5$) coupled to the SM\nenergy-momentum tensor with strength $1/\\Lambda_\\pi$, on top of the\nmassless zero-mode coupled with $1/\\bar M_{Pl}$. The diagnostic\nobservable is $\\sigma(e^+e^- \\to \\mu^+\\mu^-)$ scanned over\n$\\sqrt{s}\\in\\{200,300,\\ldots,1200\\}$ GeV, which exhibits sharp\nresonance peaks at the KK masses $m_n = \\{600, 1098, 1592, 2086, 2580\\}$\nGeV where they sit in the scan window, and an off-resonance continuum\nenhancement above SM Drell-Yan elsewhere.\n\nA credible study (a) implements the spin-2 graviton-stress-tensor\nLagrangian (FeynRules + UFO or an equivalent analytic propagator),\n(b) generates parton-level events at each of the 11 energy points with\nproper handling of the unitary-gauge graviton propagator,\n(c) extracts $\\sigma(\\sqrt{s})$ from the MadGraph banner for each run,\n(d) plots $\\sigma$ vs $\\sqrt{s}$ on a log-y axis over [50, 1500] GeV with\nrange $[400, 5\\times 10^6]$ fb, and (e) compares the resonance positions\nand continuum heights against the published Figure 2. Research question:\n*to what extent does an autonomous HEP pipeline reproduce the RS\nKK-graviton resonance spectrum imprinted on dilepton production at a\n500 GeV-1 TeV $e^+e^-$ collider?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The first KK resonance $m_1 = 600$ GeV produces a peak in $\\\\sigma(e^+e^- \\\\to \\\\mu^+\\\\mu^-)$ at $\\\\sqrt{s} = 600$ GeV that lies within 5% of the input mass.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Off-resonance cross sections at $\\\\sqrt{s}=200$ and $\\\\sqrt{s}=400$ GeV agree with the published Figure 2 values within 30%, demonstrating correct continuum (zero-mode + virtual KK) interference.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"At least 2 of the 5 KK resonances within the [50,1500] GeV scan window ($m_1=600$, $m_2=1098$ GeV) are visible as $\\\\geq 1$-decade enhancements above the SM Drell-Yan baseline at $\\\\sqrt{s}=m_n$.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the Randall-Sundrum KK-graviton resonance pattern in $e^+e^- \\\\to \\\\mu^+\\\\mu^-$ across the $\\\\sqrt{s}=200$-$1200$ GeV scan, matching the published positions and continuum heights of Figure 2 of arXiv:hep-ph/9909255?\", \"conditions\": [{\"name\": \"sqrt_s_scan\", \"description\": \"Scan $\\\\sqrt{s} \\\\in \\\\{200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200\\\\}$ GeV with $E^+ = E^- = \\\\sqrt{s}/2$, 100 events per energy point. KK masses fixed at $m_n = \\\\{600, 1098, 1592, 2086, 2580\\\\}$ GeV; $\\\\Lambda_\\\\pi = 522$ GeV; $\\\\bar M_{Pl} = 2.4 \\\\times 10^{18}$ GeV.\"}, {\"name\": \"sm_only_baseline\", \"description\": \"Same energy scan with all KK gravitons decoupled ($1/\\\\Lambda_\\\\pi \\\\to 0$) to obtain the pure SM $\\\\gamma^*/Z$ Drell-Yan baseline for resonance-significance comparison.\"}], \"baselines\": [\"Pure SM $e^+e^- \\\\to \\\\mu^+\\\\mu^-$ (Drell-Yan via $\\\\gamma^*/Z$) at the same energy points\"], \"metrics\": [{\"name\": \"cross_section_pb\", \"direction\": \"match_reference\", \"description\": \"Total $\\\\sigma(e^+e^- \\\\to \\\\mu^+\\\\mu^-)$ in pb at each of the 11 $\\\\sqrt{s}$ points.\"}, {\"name\": \"peak_position_gev\", \"direction\": \"match_reference\", \"description\": \"Reconstructed $\\\\sqrt{s}$ at which the first KK resonance peaks; target = $600 \\\\pm 30$ GeV.\"}, {\"name\": \"resonance_to_continuum_ratio\", \"direction\": \"match_reference\", \"description\": \"Ratio $\\\\sigma(\\\\sqrt{s}=m_1) / \\\\sigma(\\\\sqrt{s}=m_1 - 200\\\\,\\\\mathrm{GeV})$; should exceed $\\\\sim 10$.\"}], \"datasets\": [{\"process_id\": \"ee_to_mumu_RS\", \"sqrt_s_TeV\": 1.2, \"description\": \"$e^+e^- \\\\to \\\\mu^+\\\\mu^-$ in the RS warped-graviton model.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_kk1_peak_position\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report a numeric `peak_position_gev` (or equivalent first-KK-resonance position) within ±5% of 600 GeV — i.e. in the interval [570, 630] GeV — after the RS scan over √s ∈ {200..1200} GeV.\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p01-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for RS warped extra dimension KK-graviton resonance in $e^+e^- \\\\to \\\\mu^+\\\\mu^-$. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p01-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p01-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Randall-Sundrum spin-2 KK graviton tower coupled to SM stress-energy tensor with $1/\\\\Lambda_\\\\pi$ (massive modes) and $1/\\\\bar M_{Pl}$ (zero mode); five massive modes $m_n \\\\in \\\\{600, 1098, 1592, 2086, 2580\\\\}$ GeV with $\\\\Lambda_\\\\pi = 522$ GeV.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p01-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $e^+e^- \\\\to \\\\mu^+\\\\mu^-$ at energies $\\\\sqrt{s} \\\\in \\\\{200,\\\\ldots,1200\\\\}$ GeV with KK-graviton tower.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p01-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: Lepton-collider beams $E^\\\\pm = \\\\sqrt{s}/2$, no proton PDF; widths of all KK gravitons computed automatically (two-body decays only); 100 events per energy point.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p01-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p01-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p01-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p01-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p01-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p01-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p01-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — First KK resonance peaks within 5% of input $m_1=600$ GeV (i.e. peak in $[570, 630]$ GeV) — score 100% if rel. err. < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p01-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — Off-resonance cross sections at $\\\\sqrt{s} \\\\in \\\\{200, 400\\\\}$ GeV agree with the published Figure 2 within $\\\\pm 30\\\\%$. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p01-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — At least 2 of the 5 KK resonances within the [50,1500] GeV scan window appear as $\\\\geq 1$-decade enhancements above the SM Drell-Yan baseline. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p01-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Reproduced figure shows $\\\\sigma$ vs $\\\\sqrt{s}$ with x-axis $\\\\sqrt{s}$ in GeV (linear, [50,1500]), y-axis $\\\\sigma$ in fb (log, $[400, 5\\\\times 10^6]$), blue line, all resonances visible.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p01-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p01-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p01-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p01-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p01-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P01.yaml", "rubric_file": "tasks/physics/rubrics/P01.json"} +{"id": "P02", "domain": "physics", "title": "Reproducing $pp \\to \\mu^\\pm N$ cross section vs heavy-Majorana-neutrino mass at $\\sqrt{s} = 7, 8, 14$ TeV", "topic": "Reproducing heavy Majorana neutrino production $pp \\to \\mu^\\pm N$ at LHC 7/8/14 TeV from arXiv:1308.2209", "domains": ["high-energy-physics", "bsm-phenomenology", "neutrino-physics"], "arxiv_id": "1308.2209", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the LHC sensitivity to a single heavy Majorana neutrino $N$\nin the muon channel as a benchmark of the agent's ability to assemble a\nhadron-collider Lagrangian -> MC -> analysis pipeline. The BSM extension\nadds a right-handed singlet $N$ mixing with $\\nu_\\mu$ via the\ndimensionless real parameter $V_{\\mu N}$, so the relevant interaction is\n$\\mathcal{L}_N \\supset -(g_2/\\sqrt{2})\\,V_{\\mu N}\\,(\\bar\\mu\\gamma^\\mu P_L\nN)\\,W^-_\\mu + (\\text{NC term}) + \\text{h.c.}$ The diagnostic observable\nis the $W^*$-mediated cross section $\\sigma(pp \\to \\mu^\\pm N)$ scanned\nover $m_N \\in \\{100, 200, \\ldots, 1000\\}$ GeV with $|V_{\\mu N}|=1$, at\nthree LHC energies $\\sqrt{s} \\in \\{7, 8, 14\\}$ TeV with the CTEQ6L PDF.\n\nA credible study (a) implements the heavy-Majorana-neutrino Lagrangian\n(FeynRules .fr or analytic UFO modification), (b) sets up MadGraph runs\nfor the 30 (mass, energy) cells with the CTEQ6L PDF, (c) extracts the\ncross section per cell, and (d) plots three side-by-side panels of\n$\\sigma(m_N)$ on a log-y axis over $[100,1000]$ GeV vs $[0.1, 4\\times\n10^4]$ fb. Research question: *does the agent reproduce the steep\nPDF-driven fall-off of $\\sigma(pp \\to \\mu^\\pm N)$ with $m_N$ and the\ncorrect ordering between $\\sqrt{s}=7$, 8, and 14 TeV predicted by\narXiv:1308.2209 Fig. 3?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At $m_N = 200$ GeV and $\\\\sqrt{s}=14$ TeV, $\\\\sigma(pp \\\\to \\\\mu^\\\\pm N)$ falls within $\\\\pm 30\\\\%$ of the published reference value.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Cross-section ordering $\\\\sigma(14\\\\,\\\\mathrm{TeV}) > \\\\sigma(8\\\\,\\\\mathrm{TeV}) > \\\\sigma(7\\\\,\\\\mathrm{TeV})$ holds at every mass point, with the 14/7 ratio at $m_N = 500$ GeV in $[5, 30]$.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"$\\\\sigma(m_N)$ falls by at least 2 orders of magnitude between $m_N = 100$ GeV and $m_N = 1000$ GeV at every LHC energy.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the cross-section curves for $pp \\\\to \\\\mu^\\\\pm N$ as a function of the heavy-Majorana-neutrino mass at the 7, 8, and 14 TeV LHC, matching Figure 3 of arXiv:1308.2209?\", \"conditions\": [{\"name\": \"lhc_7tev\", \"description\": \"$pp$ collisions at $\\\\sqrt{s}=7$ TeV with CTEQ6L PDF, mass scan $m_N \\\\in \\\\{100, 200, 300, 400, 500, 600, 700, 800, 900, 1000\\\\}$ GeV, $|V_{\\\\mu N}|=1$.\"}, {\"name\": \"lhc_8tev\", \"description\": \"Same setup at $\\\\sqrt{s}=8$ TeV (LHC Run 1 final dataset energy).\"}, {\"name\": \"lhc_14tev\", \"description\": \"Same setup at $\\\\sqrt{s}=14$ TeV (HL-LHC design).\"}], \"baselines\": [\"Charged-current SM $pp \\\\to \\\\mu \\\\nu_\\\\mu$ (W production) cross check at the same energies\"], \"metrics\": [{\"name\": \"cross_section_fb\", \"direction\": \"match_reference\", \"description\": \"Total $\\\\sigma(pp \\\\to \\\\mu^\\\\pm N)$ in fb at each (mass, energy) cell.\"}, {\"name\": \"energy_ratio_14_to_7\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma(14\\\\,\\\\mathrm{TeV})/\\\\sigma(7\\\\,\\\\mathrm{TeV})$ at $m_N = 500$ GeV.\"}, {\"name\": \"mass_dropoff_factor\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma(m_N=100)/\\\\sigma(m_N=1000)$ at each $\\\\sqrt{s}$.\"}], \"datasets\": [{\"process_id\": \"pp_to_muN_via_Wstar\", \"sqrt_s_TeV\": 14, \"description\": \"$pp \\\\to \\\\mu^\\\\pm N$ via $W^*$ at the LHC (CTEQ6L PDF). Three sub-samples for $\\\\sqrt{s} \\\\in \\\\{7,8,14\\\\}$ TeV.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_xsec_at_mN_200\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report a numeric `sigma_pp_to_muN_at_mN200_14TeV_fb` (or equivalent) within ±30% of the published reference value at m_N=200 GeV, √s=14 TeV.\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p02-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for Heavy Majorana neutrino $pp \\\\to \\\\mu^\\\\pm N$ at LHC 7/8/14 TeV. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p02-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p02-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): BSM single right-handed Majorana neutrino $N$ mixing with the muon flavor: $\\\\mathcal{L}_N \\\\supset -(g_2/\\\\sqrt 2) V_{\\\\mu N} (\\\\bar\\\\mu \\\\gamma^\\\\mu P_L N) W^-_\\\\mu - (g_2/2 c_W) V_{\\\\mu N} (\\\\bar\\\\nu_\\\\mu \\\\gamma^\\\\mu P_L N) Z_\\\\mu + \\\\mathrm{h.c.}$ at $|V_{\\\\mu N}| = 1$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p02-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\\\to \\\\mu^\\\\pm N$ via $W^*$ at the LHC, mass scan $m_N \\\\in \\\\{100,\\\\ldots,1000\\\\}$ GeV at three energies.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p02-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: CTEQ6L PDF for all $\\\\sqrt{s}$ values; 30 (mass, energy) cells (10 masses x 3 energies).\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p02-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p02-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p02-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p02-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p02-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p02-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p02-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — $\\\\sigma(pp \\\\to \\\\mu^\\\\pm N)$ at $m_N=200$ GeV, $\\\\sqrt{s}=14$ TeV agrees with reference within $\\\\pm 30\\\\%$.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p02-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — Energy ordering $\\\\sigma(14) > \\\\sigma(8) > \\\\sigma(7)$ TeV holds at every mass; 14/7 ratio at $m_N=500$ GeV in $[5, 30]$. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p02-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — $\\\\sigma(m_N)$ falls $\\\\geq 2$ orders of magnitude from $m_N=100$ to $m_N=1000$ GeV at every $\\\\sqrt{s}$. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p02-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Three-panel figure (one per $\\\\sqrt{s}$), 1:1 aspect, x-axis $m_N \\\\in [100,1000]$ GeV linear, y-axis $\\\\sigma \\\\in [0.1, 4\\\\times10^4]$ fb log.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p02-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p02-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p02-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p02-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p02-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P02.yaml", "rubric_file": "tasks/physics/rubrics/P02.json"} +{"id": "P03", "domain": "physics", "title": "Reproducing 8 TeV LHC dilepton exclusion contours for a B-L Z' boson with kinetic mixing", "topic": "Reproducing 8 TeV LHC dilepton exclusion contours for B-L Z' with kinetic mixing from arXiv:1605.02910", "domains": ["high-energy-physics", "bsm-phenomenology", "z-prime", "statistical-recast"], "arxiv_id": "1605.02910", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the LHC Run 1 sensitivity to a B-L gauge boson Z' with\nkinetic mixing as a benchmark of the agent's ability to combine MC\nparameter scans with an analytic statistical recast. The model has a\nZ' coupling to SM fermions via a left/right combination\n$C_{f,L/R}(\\tilde g, g_1', M_{Z'})$ where $g_1'$ is the B-L gauge\ncoupling, $\\tilde g$ is the kinetic mixing, and the production cross\nsection is the exact quadratic form\n$\\sigma(pp \\to Z') = A g_1'^2 + B g_1' \\tilde g + C \\tilde g^2$ — so\nthree runs per mass point determine $(A,B,C)$ for the entire $(\\tilde\ng, g_1')$ plane. The diagnostic observable is the three-panel exclusion\ncontour at $\\mathrm{Sig}\\equiv 2(\\sqrt{S+B}-\\sqrt B) = 2$ for\n$M_{Z'} \\in \\{2.0, 2.5, 3.0\\}$ TeV at $\\mathcal{L} = 20$ fb$^{-1}$.\n\nA credible study (a) implements the kinetic-mixing B-L Lagrangian and\n$C_{f,L/R}$ couplings, (b) runs the 9 signal MadGraph runs (3 mass\npoints x 3 coupling points) plus 3 SM Drell-Yan background runs with\n$m_{\\ell\\ell} > 0.9 M_{Z'}$ generator cut, (c) computes the analytic\n$Z' \\to e^+e^- + \\mu^+\\mu^-$ branching ratio including 3 degenerate\nheavy Majorana neutrinos $m_{\\nu_h}=95$ GeV, (d) applies the published\nNNLO k-factors $k\\in\\{1.35,1.40,1.50\\}$ and acceptance\n$\\epsilon_\\mathrm{acc}=0.6$, (e) solves the $\\mathrm{Sig}=2$ equation\non a 2D grid in $(\\tilde g, g_1')$, and (f) plots three side-by-side\ncontours. Research question: *does the agent reproduce the three-panel\n$(\\tilde g, g_1')$ exclusion contours at $M_{Z'}=2.0, 2.5, 3.0$ TeV from\nFigure 1 of arXiv:1605.02910?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"For the $M_{Z'}=2$ TeV panel, the exclusion contour crosses the $\\\\tilde g = 0$ axis at $g_1' \\\\in [0.10, 0.20]$ (matching the published value within 30%).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The $(A, B, C)$ quadratic coefficients extracted at $M_{Z'}=2$ TeV satisfy $A > 0$ and $|B| \\\\le 4\\\\sqrt{AC}$ (positivity of the cross section over the full parameter plane).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The maximum reach in $g_1'$ at $\\\\tilde g = 0$ degrades by at least a factor of 2 going from $M_{Z'}=2$ TeV to $M_{Z'}=3$ TeV, reflecting the PDF suppression at high mass.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the three-panel $(\\\\tilde g, g_1')$ 2$\\\\sigma$ exclusion contours at $M_{Z'}=2.0, 2.5, 3.0$ TeV from the LHC Run 1 dilepton search at $\\\\sqrt{s}=8$ TeV with $\\\\mathcal{L}=20$ fb$^{-1}$ (Figure 1 of arXiv:1605.02910)?\", \"conditions\": [{\"name\": \"mzp_2tev\", \"description\": \"$M_{Z'}=2$ TeV: 3 signal runs at $(g_1', \\\\tilde g) \\\\in \\\\{(0.10, 0), (0.10, -0.10), (0.15, -0.05)\\\\}$.\"}, {\"name\": \"mzp_2p5tev\", \"description\": \"$M_{Z'}=2.5$ TeV: 3 signal runs at the same three $(g_1', \\\\tilde g)$ points.\"}, {\"name\": \"mzp_3tev\", \"description\": \"$M_{Z'}=3$ TeV: 3 signal runs at $(g_1', \\\\tilde g) \\\\in \\\\{(0.30, 0), (0.30, -0.30), (0.50, -0.60)\\\\}$.\"}], \"baselines\": [\"SM Drell-Yan $pp \\\\to \\\\ell^+\\\\ell^-$ at $\\\\sqrt{s}=8$ TeV with generator-level cut $m_{\\\\ell\\\\ell} > 0.9 M_{Z'}$, one per mass point\"], \"metrics\": [{\"name\": \"exclusion_g1p_at_kinmix0\", \"direction\": \"match_reference\", \"description\": \"Value of $g_1'$ at which the $\\\\mathrm{Sig}=2$ contour crosses $\\\\tilde g = 0$, per mass point.\"}, {\"name\": \"quadratic_coefficients_ABC\", \"direction\": \"match_reference\", \"description\": \"$(A, B, C)$ extracted from the 3 signal runs per mass; positive-definite check.\"}, {\"name\": \"br_zprime_to_dilepton\", \"direction\": \"match_reference\", \"description\": \"Analytic $\\\\mathrm{BR}(Z' \\\\to e^+e^-+\\\\mu^+\\\\mu^-)$ at the three benchmark masses.\"}], \"datasets\": [{\"process_id\": \"pp_to_Zprime_BL_kinmix\", \"sqrt_s_TeV\": 8, \"description\": \"$pp \\\\to Z'$ with kinetic-mixing B-L Z' at the LHC, dilepton final state. Background: SM Drell-Yan with $m_{\\\\ell\\\\ell}$ cut.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_g1prime_threshold\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the g1' coordinate where the 2σ exclusion contour at M_Z'=2 TeV crosses the g̃=0 axis, in the interval [0.07, 0.26] (i.e. published [0.10, 0.20] ±30%).\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p03-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for B-L $Z'$ kinetic mixing, three-panel exclusion in $(\\\\tilde g, g_1')$. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p03-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p03-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Kinetic-mixing B-L $Z'$ with chiral couplings $C_{f,L/R}(\\\\tilde g, g_1', M_{Z'})$ encoding $g_1'$ (B-L gauge), $\\\\tilde g$ (kinetic mixing), and 3 heavy Majorana neutrinos $\\\\nu_h$ at $m_{\\\\nu_h}=95$ GeV.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p03-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\\\to Z'$ at $\\\\sqrt{s}=8$ TeV with kinetic mixing; Drell-Yan dilepton search recast.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p03-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: 9 signal runs (3 mass x 3 coupling points), 3 SM Drell-Yan background runs with generator cut $m_{\\\\ell\\\\ell} > 0.9 M_{Z'}$ at $\\\\sqrt{s}=8$ TeV; mass-dependent NNLO k-factors $\\\\{1.35, 1.40, 1.50\\\\}$ and $\\\\epsilon_\\\\mathrm{acc}=0.6$.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p03-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p03-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p03-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p03-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p03-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p03-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p03-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — $M_{Z'}=2$ TeV exclusion contour crosses the $\\\\tilde g=0$ axis at $g_1' \\\\in [0.10, 0.20]$ (within 30% of published).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p03-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — Quadratic coefficients $(A,B,C)$ at $M_{Z'}=2$ TeV satisfy $A>0$ and $|B| \\\\le 4\\\\sqrt{AC}$ (positive-definite cross section everywhere in plane). Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p03-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — Maximum reach in $g_1'$ at $\\\\tilde g=0$ degrades by $\\\\geq 2\\\\times$ from $M_{Z'}=2$ TeV to $M_{Z'}=3$ TeV. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p03-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Three side-by-side panels (one per $M_{Z'}$), 1:1 aspect, exclusion contour at $\\\\mathrm{Sig}\\\\equiv 2(\\\\sqrt{S+B}-\\\\sqrt B)=2$ in the $(\\\\tilde g, g_1')$ plane.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p03-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p03-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p03-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p03-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p03-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P03.yaml", "rubric_file": "tasks/physics/rubrics/P03.json"} +{"id": "P04", "domain": "physics", "title": "Reproducing $\\sigma(pp \\to Z')$ vs $g_1'$ slices for kinetic-mixing B-L Z' at $\\sqrt{s}=13$ TeV", "topic": "Reproducing $\\sigma(pp \\to Z')$ vs $g_1'$ slices for kinetic-mixing B-L Z' at 13 TeV from arXiv:1605.02910", "domains": ["high-energy-physics", "bsm-phenomenology", "z-prime"], "arxiv_id": "1605.02910", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the $\\sqrt{s}=13$ TeV LHC production cross section\n$\\sigma(pp \\to Z')$ as a function of $g_1'$ along three constant-\n$\\tilde g$ slices, for the same kinetic-mixing B-L $Z'$ model as P03.\nThe cross section is exactly quadratic in the couplings,\n$\\sigma(g_1', \\tilde g) = A g_1'^2 + B g_1' \\tilde g + C \\tilde g^2$,\nso 3 MadGraph runs per mass point determine $(A,B,C)$ on the entire\n$(g_1', \\tilde g)$ plane. The diagnostic figure has two panels\n(Panel A: $M_{Z'}=2$ TeV with $\\tilde g\\in\\{0, -0.05, -0.10\\}$;\nPanel B: $M_{Z'}=3$ TeV with $\\tilde g\\in\\{0, -0.30, -0.60\\}$).\n\nA credible study (a) reuses the kinetic-mixing B-L $Z'$ model\n(FeynRules + UFO), (b) executes 6 MadGraph runs (3 per mass) at\n$\\sqrt{s}=13$ TeV, (c) solves the linear system for $(A,B,C)$ at each\nmass, (d) computes the analytic curves $\\sigma(g_1')|_{\\tilde g}$ for\nthe requested slices, and (e) plots two panels with log-y on the\nprescribed ranges (Panel A: $g_1' \\in [0, 0.20]$, $\\sigma \\in\n[10^{-4}, 10^{-1}]$ pb; Panel B: $g_1' \\in [0, 0.70]$, $\\sigma \\in\n[10^{-4}, 10^{1}]$ pb). Research question: *does the agent recover the\nquadratic-in-couplings parameterization of $Z'$ production well enough\nto reproduce the constant-$\\tilde g$ cross-section slices in Figure 10\nof arXiv:1605.02910?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At Panel A ($M_{Z'}=2$ TeV, $\\\\tilde g = 0$, $g_1' = 0.10$), the reconstructed $\\\\sigma(pp \\\\to Z')$ lies within $\\\\pm 30\\\\%$ of the published value.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The curve at $\\\\tilde g = -0.10$ in Panel A lies above the $\\\\tilde g = 0$ curve at $g_1' = 0.05$ but below at $g_1' = 0.20$, demonstrating the destructive-then-constructive interference pattern of the kinetic mixing.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"$\\\\sigma$ at ($M_{Z'}=2$ TeV, $g_1'=0.10$, $\\\\tilde g=0$) exceeds $\\\\sigma$ at ($M_{Z'}=3$ TeV, $g_1'=0.10$, $\\\\tilde g=0$) by a factor in $[10, 200]$, reflecting the steep PDF suppression.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce $\\\\sigma(pp \\\\to Z')$ vs $g_1'$ for fixed-$\\\\tilde g$ slices at $M_{Z'}=2$ and 3 TeV at $\\\\sqrt{s}=13$ TeV (Figure 10 of arXiv:1605.02910)?\", \"conditions\": [{\"name\": \"mzp_2tev_3runs\", \"description\": \"$M_{Z'}=2$ TeV at $\\\\sqrt{s}=13$ TeV: 3 signal runs at $(g_1', \\\\tilde g) \\\\in \\\\{(0.10, 0), (0.10, -0.10), (0.15, -0.05)\\\\}$.\"}, {\"name\": \"mzp_3tev_3runs\", \"description\": \"$M_{Z'}=3$ TeV at $\\\\sqrt{s}=13$ TeV: 3 signal runs at $(g_1', \\\\tilde g) \\\\in \\\\{(0.30, 0), (0.30, -0.30), (0.50, -0.60)\\\\}$.\"}], \"baselines\": [\"Self-consistency cross-check: predicted $\\\\sigma$ at the 3 input points must reproduce the simulated $\\\\sigma$ exactly (closure of the $A,B,C$ fit)\"], \"metrics\": [{\"name\": \"cross_section_pb\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma(pp \\\\to Z')$ in pb on the requested slice points.\"}, {\"name\": \"quadratic_coefficients_ABC_13tev\", \"direction\": \"match_reference\", \"description\": \"$(A, B, C)$ coefficients per mass point at $\\\\sqrt{s}=13$ TeV.\"}, {\"name\": \"mass_pdf_suppression_ratio\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma(M_{Z'}=2)/\\\\sigma(M_{Z'}=3)$ at $\\\\tilde g = 0$, $g_1' = 0.10$.\"}], \"datasets\": [{\"process_id\": \"pp_to_Zprime_BL_kinmix_13tev\", \"sqrt_s_TeV\": 13, \"description\": \"$pp \\\\to Z'$ for the kinetic-mixing B-L Z' model at LHC Run 2 energy.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_xsec_Zprime_2TeV\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report `sigma_pp_to_Zprime_2TeV_panelA_fb` (at M_Z'=2 TeV, g̃=0, g1'=0.10) within ±30% of the published Panel A reference cross section.\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p04-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for B-L $Z'$ kinetic mixing $\\\\sigma(g_1')$ slices at $\\\\sqrt{s}=13$ TeV. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p04-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p04-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Same kinetic-mixing B-L $Z'$ as P03; cross section $\\\\sigma = A g_1'^2 + B g_1'\\\\tilde g + C \\\\tilde g^2$ exact.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p04-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\\\to Z'$ at $\\\\sqrt{s}=13$ TeV; $\\\\sigma$ vs $g_1'$ at fixed $\\\\tilde g$ slices.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p04-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: 6 signal runs (3 per mass) at $\\\\sqrt{s}=13$ TeV; standard LHC PDF set.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p04-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p04-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p04-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p04-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p04-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p04-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p04-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — $\\\\sigma(M_{Z'}=2\\\\,\\\\mathrm{TeV}, \\\\tilde g=0, g_1'=0.10)$ within $\\\\pm 30\\\\%$ of published Panel A value.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p04-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — $\\\\tilde g=-0.10$ curve in Panel A lies above $\\\\tilde g=0$ at $g_1'=0.05$ but below at $g_1'=0.20$ (interference sign change). Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p04-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — $\\\\sigma(M_{Z'}=2)/\\\\sigma(M_{Z'}=3)$ at $\\\\tilde g=0, g_1'=0.10$ is in $[10, 200]$ (PDF suppression). Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p04-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Two-panel figure (Panel A: $M_{Z'}=2$ TeV, $g_1'\\\\in[0,0.20]$, $\\\\sigma \\\\in [10^{-4}, 10^{-1}]$ pb; Panel B: $M_{Z'}=3$ TeV, $g_1'\\\\in[0,0.70]$, $\\\\sigma \\\\in [10^{-4}, 10^1]$ pb), three colored curves per panel.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p04-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p04-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p04-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p04-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p04-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P04.yaml", "rubric_file": "tasks/physics/rubrics/P04.json"} +{"id": "P05", "domain": "physics", "title": "Reproducing the normalized $E_T^\\mathrm{miss}$ shape in $pp \\to a W^\\pm \\gamma$ from a photophobic ALP EFT", "topic": "Reproducing photophobic ALP EFT $E_T^\\mathrm{miss}$ shape in $pp \\to a W^\\pm \\gamma$ from arXiv:1701.05379", "domains": ["high-energy-physics", "bsm-phenomenology", "axion-like-particles", "effective-field-theory"], "arxiv_id": "1701.05379", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the normalized $E_T^\\mathrm{miss}$ distribution for the\nprocess $pp \\to a W^\\pm (\\to \\ell^\\pm \\nu)\\, \\gamma$ at $\\sqrt{s}=13$\nTeV in the bosonic ALP EFT, as a benchmark of the agent's ability to\nhandle a higher-dimensional EFT operator and parton-level missing-energy\nreconstruction. The Lagrangian is\n$\\delta\\mathcal{L}_a = c_{\\tilde W} \\mathcal{A}_{\\tilde W} +\nc_{\\tilde B} \\mathcal{A}_{\\tilde B}$ with the photophobic constraint\n$c_{\\tilde B} = -\\tan^2\\theta_W \\cdot c_{\\tilde W}$ (so the\n$a\\gamma\\gamma$ coupling vanishes), giving a single free Wilson\ncoefficient $c_{\\tilde W}$. The diagnostic observable is the\nnormalized parton-level $E_T^\\mathrm{miss} = |\\vec p_T^{\\,a} +\n\\vec p_T^{\\,\\nu}|$ shape in 50 bins over [0, 1000] GeV, with\ngeneration cuts $p_T^{\\gamma,\\ell} > 20$ GeV, $|\\eta^{\\gamma,\\ell}| < 2.5$.\n\nA credible study (a) implements the photophobic ALP EFT (FeynRules\nbosonic operators with $c_{\\tilde B} = -\\tan^2\\theta_W \\cdot c_{\\tilde W}$),\n(b) generates 500k LHE events at $f_a = 1000$ GeV, $m_a = 0.001$ GeV,\n$c_{\\tilde W} = 1$ with the nn23lo1 PDF, (c) sums $\\vec p_T^{\\,a}$ +\n$\\vec p_T^{\\,\\nu}$ from invisible final-state particles to reconstruct\n$E_T^\\mathrm{miss}$, and (d) plots the normalized histogram on log-y\n$[10^{-4}, 1]$ over [0, 1000] GeV. Research question: *does the agent\nreproduce the falling-then-flattening MET shape characteristic of a\nderivative ALP coupling versus the steeply falling SM-like baseline,\nmatching Figure 8 of arXiv:1701.05379?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The peak of the normalized $E_T^\\\\mathrm{miss}$ distribution lies in the bin range $[100, 250]$ GeV, consistent with the published shape.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The integrated normalized fraction of events with $E_T^\\\\mathrm{miss} > 500$ GeV is in $[0.05, 0.30]$ at $c_{\\\\tilde W}=1$, $f_a=1$ TeV (long high-MET tail from the derivative ALP coupling).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Acceptance after the published photon and lepton cuts ($p_T > 20$ GeV, $|\\\\eta| < 2.5$) is $\\\\geq 50\\\\%$ of generated events.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the parton-level $E_T^\\\\mathrm{miss}$ shape for $pp \\\\to a W^\\\\pm \\\\gamma$, $W \\\\to \\\\ell\\\\nu$ at $\\\\sqrt{s}=13$ TeV under the photophobic ALP EFT (Figure 8 of arXiv:1701.05379)?\", \"conditions\": [{\"name\": \"alp_cw1_fa1tev\", \"description\": \"Bosonic ALP EFT at $c_{\\\\tilde W}=1$, $c_{\\\\tilde B}=-\\\\tan^2\\\\theta_W$, $f_a=1000$ GeV, $m_a=0.001$ GeV, 500k events at $\\\\sqrt{s}=13$ TeV with nn23lo1 PDF.\"}, {\"name\": \"alp_cw_variant\", \"description\": \"Sanity-check run at $c_{\\\\tilde W}=2$, $f_a=1000$ GeV; cross section should scale as $c_{\\\\tilde W}^2$ but normalized shape should be invariant.\"}], \"baselines\": [\"Shape-only normalization is its own baseline: deviation between $c_{\\\\tilde W}=1$ and $c_{\\\\tilde W}=2$ shapes should be statistical only\"], \"metrics\": [{\"name\": \"etmiss_peak_position_gev\", \"direction\": \"match_reference\", \"description\": \"$E_T^\\\\mathrm{miss}$ bin center of the peak of the normalized histogram.\"}, {\"name\": \"high_metmiss_tail_fraction\", \"direction\": \"match_reference\", \"description\": \"Fraction of normalized events with $E_T^\\\\mathrm{miss} > 500$ GeV.\"}, {\"name\": \"cut_acceptance\", \"direction\": \"match_reference\", \"description\": \"Acceptance fraction after photon + lepton fiducial cuts.\"}], \"datasets\": [{\"process_id\": \"pp_to_aWgamma_Wlnu\", \"sqrt_s_TeV\": 13, \"description\": \"Parton-level $pp \\\\to a W^\\\\pm \\\\gamma$ with $W \\\\to \\\\ell\\\\nu$ in the photophobic ALP EFT.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_met_peak_bin\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the peak bin of the normalized E_T^miss distribution; the bin center must lie in [100, 250] GeV and the distribution must integrate to 1.0 (normalized).\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p05-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for Photophobic ALP EFT $E_T^\\\\mathrm{miss}$ in $pp \\\\to a W^\\\\pm \\\\gamma$. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p05-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p05-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Bosonic ALP EFT operators $\\\\mathcal{A}_{\\\\tilde W}, \\\\mathcal{A}_{\\\\tilde B}$ with photophobic relation $c_{\\\\tilde B} = -\\\\tan^2\\\\theta_W \\\\cdot c_{\\\\tilde W}$; $f_a = 1$ TeV, $m_a = 1$ MeV, $c_{\\\\tilde W} = 1$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p05-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: Parton-level $pp \\\\to a W^\\\\pm (\\\\to \\\\ell^\\\\pm\\\\nu) \\\\gamma$ at $\\\\sqrt{s}=13$ TeV.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p05-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: nn23lo1 PDF; photon $p_T > 20$ GeV, $|\\\\eta_\\\\gamma| < 2.5$; lepton $p_T > 20$ GeV, $|\\\\eta_\\\\ell| < 2.5$; 500k events, parton level (no shower/detector).\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p05-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p05-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p05-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p05-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p05-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p05-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p05-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — Peak of normalized $E_T^\\\\mathrm{miss}$ in $[100, 250]$ GeV bin range.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p05-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — Integrated normalized fraction with $E_T^\\\\mathrm{miss} > 500$ GeV in $[0.05, 0.30]$ (long high-MET tail from derivative coupling). Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p05-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — Acceptance after photon + lepton cuts $\\\\geq 50\\\\%$. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p05-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Normalized $E_T^\\\\mathrm{miss}$ histogram, 50 bins over [0,1000] GeV, log-y $[10^{-4}, 1]$, 4:3 aspect.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p05-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p05-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p05-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p05-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p05-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P05.yaml", "rubric_file": "tasks/physics/rubrics/P05.json"} +{"id": "P06", "domain": "physics", "title": "Reproducing $U_1$ vector-leptoquark $\\sqrt{|g_c^* g_b|}$ vs $M_{U_1}$ exclusion from ATLAS+CMS $pp \\to \\tau\\nu$", "topic": "Reproducing $U_1$ vector leptoquark exclusion from combined ATLAS+CMS $pp \\to \\tau\\nu$ recast from arXiv:1811.07920", "domains": ["high-energy-physics", "bsm-phenomenology", "leptoquark", "flavor-anomaly", "statistical-recast"], "arxiv_id": "1811.07920", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the LHC sensitivity to a $U_1$ vector leptoquark from the\n$pp \\to \\tau\\nu$ channel via $bc \\to U_1 \\to \\tau\\nu$ exchange,\ncombining ATLAS + CMS searches in a binned profile-likelihood, as a\nbenchmark of the agent's ability to (i) generate LQ events with\nbottom-quark and charm-quark initial-state partons, (ii) run two\nparallel detector-card simulations (Delphes ATLAS card vs CMS card),\n(iii) recast experimental data into a profile-likelihood fit. The\nLagrangian couples $U_1$ to $(\\bar c \\gamma^\\mu P_L \\nu_\\tau)$ and\n$(\\bar b \\gamma^\\mu P_L \\tau)$ with couplings $g_c, g_b$, and the\ndiagnostic figure is the 2$\\sigma$ exclusion contour in the\n$(\\sqrt{|g_c^* g_b|}, M_{U_1})$ plane along with the LH and RH\n$R_{D^{(*)}}$ flavor-anomaly bands.\n\nA credible study (a) implements the $U_1$ Lagrangian (FeynRules .fr +\nUFO), (b) runs MadGraph + Pythia8 + Delphes for $M_{U_1} \\in \\{750,\n1000, 1250, 1500, 2000, 2500, 3000, 4000, 5000\\}$ GeV with both ATLAS\nand CMS cards (LHCO output), (c) applies the published ATLAS and CMS\n$\\tau$-channel selections (lepton veto, hadronic-$\\tau$ requirement,\n$E_T^\\mathrm{miss}$ cut, $m_T$ cut), (d) builds a binned signal\ntemplate that scales as $g^4$ and runs the per-bin profile likelihood\nwith nuisance parameters $\\theta_i$, (e) combines ATLAS + CMS, finds\nthe 2$\\sigma$ exclusion via $-2\\Delta\\ln\\mathcal{L} = 4$, and (f)\noverlays the analytic LH band $g_\\mathrm{LH}(M) = (M/v)\\sqrt{2 V_{cb}\\,\n\\epsilon_L}$ with $\\epsilon_L = 0.11 \\pm 0.02$ and the analogous RH band.\nResearch question: *does the agent reproduce the $(\\sqrt{|g_c^* g_b|},\nM_{U_1})$ exclusion contour and the $R_{D^{(*)}}$ overlay bands of\nFigure 3 of arXiv:1811.07920?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The combined ATLAS+CMS 2$\\\\sigma$ exclusion at $M_{U_1} = 1$ TeV gives $\\\\sqrt{|g_c^* g_b|} \\\\in [0.3, 1.5]$, matching the published value within 30%.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The reconstructed exclusion contour intersects the LH $R_{D^{(*)}}$ band ($\\\\epsilon_L = 0.11$) at an excluded mass $M_{U_1}^* \\\\in [1.0, 2.5]$ TeV.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Selection efficiency on signal events is $\\\\geq 5\\\\%$ at $M_{U_1}=1$ TeV in both ATLAS and CMS analyses (gauge of pipeline working end-to-end through Delphes).\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the 2$\\\\sigma$ exclusion contour for the $U_1$ vector leptoquark in the $(\\\\sqrt{|g_c^* g_b|}, M_{U_1})$ plane from combined ATLAS+CMS $pp \\\\to \\\\tau\\\\nu$ recast (Figure 3 of arXiv:1811.07920)?\", \"conditions\": [{\"name\": \"atlas_recast\", \"description\": \"Mass scan $M_{U_1} \\\\in \\\\{750, 1000, 1250, 1500, 2000, 2500, 3000, 4000, 5000\\\\}$ GeV at $\\\\sqrt{s}=13$ TeV, 10k events each, Pythia8 shower, Delphes ATLAS card, LHCO output. Selection: lepton veto, hadronic-$\\\\tau$ $p_T > 80$ GeV, $|\\\\eta_\\\\tau| < 2.3$, $E_T^\\\\mathrm{miss} > 150$ GeV, $m_T > 250$ GeV.\"}, {\"name\": \"cms_recast\", \"description\": \"Same mass scan with Delphes CMS card; selection: lepton veto, $\\\\tau$ $p_T>80$ GeV, $|\\\\eta|<2.1$, $E_T^\\\\mathrm{miss}>200$ GeV, $0.7 < p_T^\\\\tau / E_T^\\\\mathrm{miss} < 1.3$, $\\\\Delta\\\\phi(\\\\tau, E_T^\\\\mathrm{miss}) > 2.4$, $m_T > 320$ GeV.\"}], \"baselines\": [\"Published ATLAS background table (22 bins, 250-3200 GeV log-spaced) and CMS background table (3 bins) act as the SM-only null hypothesis\"], \"metrics\": [{\"name\": \"exclusion_coupling_at_1tev\", \"direction\": \"match_reference\", \"description\": \"$\\\\sqrt{|g_c^* g_b|}$ at the 2$\\\\sigma$ exclusion boundary at $M_{U_1}=1$ TeV from combined fit.\"}, {\"name\": \"selection_efficiency\", \"direction\": \"match_reference\", \"description\": \"Fraction of generated signal events passing the ATLAS or CMS selection at $M_{U_1}=1$ TeV.\"}, {\"name\": \"rdstar_band_intersection_tev\", \"direction\": \"match_reference\", \"description\": \"Mass at which the LH $R_{D^{(*)}}$ band crosses the exclusion contour.\"}], \"datasets\": [{\"process_id\": \"pp_to_taunu_via_U1\", \"sqrt_s_TeV\": 13, \"description\": \"$pp \\\\to \\\\tau\\\\nu$ via $bc \\\\to U_1$ exchange. Two detector simulations (Delphes ATLAS, Delphes CMS).\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_lq_coupling_band\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the 2σ combined ATLAS+CMS √(g_c* g_b) exclusion band at M_U1=1 TeV; the band lower edge must be in [0.21, 0.39] and the upper edge in [1.05, 1.95] (published [0.3, 1.5] ±30%).\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p06-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for $U_1$ vector-leptoquark recast of LHC $pp \\\\to \\\\tau\\\\nu$ (ATLAS+CMS). The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p06-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p06-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): $U_1$ vector LQ in $(\\\\mathbf{3},\\\\mathbf{1},2/3)$ rep coupling to $(\\\\bar c\\\\gamma^\\\\mu P_L\\\\nu_\\\\tau)$ and $(\\\\bar b\\\\gamma^\\\\mu P_L\\\\tau)$ with couplings $g_c, g_b$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p06-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\\\to \\\\tau\\\\nu$ via $bc \\\\to U_1 \\\\to \\\\tau\\\\nu$ exchange at $\\\\sqrt{s}=13$ TeV, mass scan.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p06-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: Standard 13 TeV proton PDF; 9 mass points $\\\\{750,\\\\ldots,5000\\\\}$ GeV with $b$ and $c$ parton initial states; 10k events per (mass, detector); Pythia8 shower; Delphes ATLAS card and Delphes CMS card runs in parallel; LHCO output.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p06-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p06-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p06-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p06-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p06-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p06-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p06-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — Combined ATLAS+CMS 2$\\\\sigma$ exclusion at $M_{U_1}=1$ TeV gives $\\\\sqrt{|g_c^* g_b|} \\\\in [0.3, 1.5]$.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p06-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — Reconstructed exclusion contour intersects the LH $R_{D^{(*)}}$ band ($\\\\epsilon_L=0.11$) at excluded mass $M_{U_1}^* \\\\in [1.0, 2.5]$ TeV. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p06-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — Selection efficiency $\\\\geq 5\\\\%$ on signal at $M_{U_1}=1$ TeV in both ATLAS and CMS recasts. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p06-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Exclusion contour in $(\\\\sqrt{|g_c^* g_b|}, M_{U_1})$ plane, 4:3 aspect, x in TeV $[0.8, 5.0]$, y $[0, 4.0]$, with LH band (blue dashed + light-blue band) and RH band (red dashed + light-red band).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p06-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p06-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p06-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p06-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p06-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P06.yaml", "rubric_file": "tasks/physics/rubrics/P06.json"} +{"id": "P07", "domain": "physics", "title": "Reproducing the $m_{ej}$ resonance in $pp \\to \\mathrm{LQ} \\to ej$ via the LUXlep proton lepton PDF", "topic": "Reproducing scalar leptoquark $m_{ej}$ resonance via LUXlep proton lepton PDF from arXiv:2005.06475", "domains": ["high-energy-physics", "bsm-phenomenology", "leptoquark", "lepton-pdf"], "arxiv_id": "2005.06475", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the LHC sensitivity to a scalar leptoquark in the\n$pp \\to \\mathrm{LQ} \\to ej$ channel as a benchmark of the agent's\nability to assemble a full Lagrangian -> MC -> shower -> detector ->\nanalysis pipeline using a non-trivial PDF (LUXlep, with electrons inside\nthe proton). The Lagrangian\n$\\mathcal{L} = \\lambda_{eu}\\,\\mathrm{LQ}_{eu}\\,e_R^T i\\sigma^2 u_R +\n\\mathrm{h.c.}$ describes an $SU(2)_L$-singlet, color-triplet,\n$Q=-1/3$ scalar leptoquark coupling to a right-handed lepton-quark pair.\nThe diagnostic observable is the reconstructed invariant mass $m_{ej}$\nof the leading electron and leading jet, peaked at the input\n$M_\\mathrm{LQ}=3$ TeV with $\\Gamma_\\mathrm{LQ}=60$ GeV after Pythia8 +\nDelphes ATLAS detector smearing.\n\nA credible study (a) implements the scalar-LQ Lagrangian (FeynRules .fr\n+ UFO), (b) generates 100k events at $\\sqrt{s}=13$ TeV with the LUXlep\nPDF (proton content redefined to include leptons + photon) and\ngeneration cuts $p_T(\\ell, j) > 500$ GeV, $|\\eta| < 2.5$, (c) applies\nthe Pythia8 lepton-to-photon workaround (replace initial-state leptons\nwith photons in the LHE; set Check:event=off; shower; Delphes ATLAS\ncard with anti-$k_T$ R=0.4 jets; LHCO output), (d) selects events with\nexactly one $e$ ($p_T>500$ GeV, $|\\eta|<2.5$), one $j$ ($p_T>500$ GeV,\n$|\\eta|<2.5$), $E_T^\\mathrm{miss} < 50$ GeV, lepton veto, jet veto,\n(e) histograms $m_{ej}$ in 100 GeV bins from 0 to 5000 GeV weighted by\n$\\sigma\\mathcal{L}/N_\\mathrm{gen}$ at $\\mathcal{L}=100$ fb$^{-1}$, and\n(f) plots on log-y $[10^{-3}, 5\\times 10^2]$ events/bin over [1000,\n5000] GeV. Research question: *does the agent reproduce the scalar-LQ\nresonance peak at $m_{ej} \\approx 3$ TeV from Figure 2 of arXiv:2005.06475?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Reconstructed $m_{ej}$ peaks within $\\\\pm 5\\\\%$ of the input $M_\\\\mathrm{LQ}=3000$ GeV after Pythia8+Delphes smearing (i.e., peak in [2850, 3150] GeV).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The generation-level $p_T(\\\\ell, j) > 500$ GeV cut yields $\\\\geq 50\\\\%$ acceptance for the benchmark $M_\\\\mathrm{LQ}=3$ TeV signal.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Total event count in the [2.5, 3.5] TeV $m_{ej}$ window at $\\\\mathcal{L} = 100$ fb$^{-1}$ matches the published value within $\\\\pm 30\\\\%$.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the $m_{ej}$ invariant-mass distribution for $pp \\\\to \\\\mathrm{LQ} \\\\to ej$ at $\\\\sqrt{s}=13$ TeV with LUXlep PDF and $M_\\\\mathrm{LQ}=3$ TeV (Figure 2 of arXiv:2005.06475)?\", \"conditions\": [{\"name\": \"lq_3tev_lambda1\", \"description\": \"Scalar LQ at $M_\\\\mathrm{LQ}=3000$ GeV, $\\\\Gamma_\\\\mathrm{LQ}=60$ GeV, $\\\\lambda_{eu}=1$, 100k events at $\\\\sqrt{s}=13$ TeV, LUXlep PDF, $p_T > 500$ GeV gen cuts, Pythia8 + Delphes ATLAS, LHCO output.\"}, {\"name\": \"lq_2tev_check\", \"description\": \"Cross-check at $M_\\\\mathrm{LQ}=2000$ GeV with $\\\\lambda_{eu}=1$, same setup; verify that $m_{ej}$ peak shifts to 2 TeV.\"}], \"baselines\": [\"Background-only sanity check: $\\\\lambda_{eu}=0$ (LQ decoupled) should produce zero $m_{ej}$ events above the gen-level cut\"], \"metrics\": [{\"name\": \"peak_position_gev\", \"direction\": \"match_reference\", \"description\": \"Bin-center of the maximum of the reconstructed $m_{ej}$ histogram for the 3 TeV signal.\"}, {\"name\": \"acceptance_pt500\", \"direction\": \"match_reference\", \"description\": \"Fraction of generated events with both lepton and jet $p_T > 500$ GeV at $M_\\\\mathrm{LQ}=3$ TeV.\"}, {\"name\": \"integrated_yield_in_window\", \"direction\": \"match_reference\", \"description\": \"Sum of weighted events in $m_{ej} \\\\in [2500, 3500]$ GeV at $\\\\mathcal{L}=100$ fb$^{-1}$.\"}], \"datasets\": [{\"process_id\": \"pp_to_LQ_to_ej_LUXlep\", \"sqrt_s_TeV\": 13, \"description\": \"Single resonant scalar leptoquark production via lepton-quark fusion using LUXlep proton PDF.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_mej_peak_position\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the reconstructed m_ej peak position (after Pythia8+Delphes smearing) in the interval [2850, 3150] GeV (input M_LQ=3 TeV ±5%).\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p07-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for Scalar leptoquark resonance $m_{ej}$ via LUXlep proton lepton PDF. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p07-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p07-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Scalar LQ in $SU(2)_L$ singlet, color triplet, $Q=-1/3$; $\\\\mathcal{L} = \\\\lambda_{eu}\\\\,\\\\mathrm{LQ}_{eu}\\\\,e_R^T i\\\\sigma^2 u_R + \\\\mathrm{h.c.}$; $M_\\\\mathrm{LQ}=3$ TeV, $\\\\Gamma=60$ GeV, $\\\\lambda_{eu}=1$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p07-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\\\to \\\\mathrm{LQ} \\\\to ej$ resonant single LQ production at $\\\\sqrt{s}=13$ TeV.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p07-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: LUXlep PDF (proton content includes electrons and photon); generation cuts $p_T(\\\\ell, j) > 500$ GeV, $|\\\\eta|<2.5$; Pythia8 with lepton-to-photon LHE workaround (Check:event=off); Delphes ATLAS card with anti-$k_T$ R=0.4 jets; LHCO output.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p07-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p07-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p07-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p07-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p07-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p07-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p07-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — Reconstructed $m_{ej}$ peaks within $\\\\pm 5\\\\%$ of input $M_\\\\mathrm{LQ}=3000$ GeV (peak in $[2850, 3150]$ GeV).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p07-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — Generation-level $p_T > 500$ GeV cut yields $\\\\geq 50\\\\%$ acceptance on the 3 TeV signal. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p07-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — Total weighted yield in $m_{ej} \\\\in [2500, 3500]$ GeV at $\\\\mathcal{L}=100$ fb$^{-1}$ within $\\\\pm 30\\\\%$ of published value. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p07-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Reconstructed $m_{ej}$ histogram, 100 GeV bins from 0 to 5000 GeV, weighted by $\\\\sigma\\\\mathcal{L}/N_\\\\mathrm{gen}$, log-y $[10^{-3}, 5\\\\times 10^2]$ events/bin, 1:1 aspect, title 'LHC, $\\\\sqrt{s}=13$ TeV'.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p07-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p07-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p07-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p07-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p07-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P07.yaml", "rubric_file": "tasks/physics/rubrics/P07.json"} +{"id": "P08", "domain": "physics", "title": "Reproducing $\\sigma(pp \\to Z' \\to \\mu^+\\mu^-)$ vs $M_{Z'}$ for SSM and E6-$\\psi$ scenarios at $\\sqrt{s}=13$ TeV", "topic": "Reproducing SSM and $E_6$-$\\psi$ Z' dimuon cross sections at 13 TeV LHC from arXiv:2103.02708", "domains": ["high-energy-physics", "bsm-phenomenology", "z-prime"], "arxiv_id": "2103.02708", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the dimuon production cross section $\\sigma(pp \\to Z' \\to\n\\mu^+\\mu^-)$ as a function of $M_{Z'}$ for two canonical $Z'$ benchmarks\n- the Sequential Standard Model (SSM) where the $Z'$ inherits SM\nelectroweak couplings, and the $E_6$-derived $Z'_\\psi$ with universal\ncoupling $g_\\psi = 0.0942$. The general Lagrangian\n$\\mathcal{L}_{NP} \\supset -Z'_\\mu \\sum_f \\bar f \\gamma^\\mu (g_{Lf} P_L\n+ g_{Rf} P_R) f$ has 7 free chiral couplings $g_{Lu}, g_{Ru}, g_{Ld},\ng_{Rd}, g_{Le}, g_{Re}, g_{Lv}$, fixed differently in each benchmark.\nThe diagnostic observable is the dimuon production cross section\nscanned over $M_{Z'} \\in \\{200, 400, 600, \\ldots, 5500\\}$ GeV at\n$\\sqrt{s}=13$ TeV.\n\nA credible study (a) implements the general $Z'$ model (FeynRules + UFO)\nwith the 7 couplings as inputs, (b) sets up two parameter cards\nencoding the SSM and $E_6$-$\\psi$ coupling tables, (c) scans 14 mass\npoints per benchmark with auto-computed $\\Gamma_{Z'}$ (two-body decays\nonly), (d) extracts the cross section per (mass, scenario) cell, and\n(e) plots two curves (SSM = green dotted, $Z'_\\psi$ = blue solid) on\nlog-y over $M_{Z'} \\in [200, 5500]$ GeV, $\\sigma \\in [5\\times 10^{-6},\n5\\times 10^{-1}]$ pb. Research question: *does the agent reproduce the\ncross-section curves and the well-known SSM $\\gtrsim Z'_\\psi$ ordering\nin dilepton production from Figure 4 of arXiv:2103.02708?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"$\\\\sigma_\\\\mathrm{SSM}(M_{Z'}=2\\\\,\\\\mathrm{TeV}) > \\\\sigma_\\\\psi(M_{Z'}=2\\\\,\\\\mathrm{TeV})$ by a factor in $[3, 30]$ at $\\\\sqrt{s}=13$ TeV (SSM has larger couplings).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"$\\\\sigma_\\\\mathrm{SSM}(M_{Z'}=1\\\\,\\\\mathrm{TeV})$ falls within $\\\\pm 30\\\\%$ of the published reference value.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The SSM cross section drops by $\\\\geq 4$ orders of magnitude between $M_{Z'}=200$ GeV and $M_{Z'}=5500$ GeV (PDF + propagator suppression).\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce $\\\\sigma(pp \\\\to Z' \\\\to \\\\mu^+\\\\mu^-)$ vs $M_{Z'}$ for the SSM ($Z'_\\\\mathrm{SSM}$) and $E_6$-derived $Z'_\\\\psi$ benchmarks at the 13 TeV LHC (Figure 4 of arXiv:2103.02708)?\", \"conditions\": [{\"name\": \"ssm_mass_scan\", \"description\": \"$Z'_\\\\mathrm{SSM}$ with electroweak couplings $g_{Lu} = (e/s_W c_W)(1/2 - 2 s_W^2/3)$, etc. (SM-like). Scan $M_{Z'} \\\\in \\\\{200, 400, 600, 800, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500\\\\}$ GeV at $\\\\sqrt{s}=13$ TeV.\"}, {\"name\": \"e6psi_mass_scan\", \"description\": \"$Z'_\\\\psi$ from $E_6$ with universal coupling $g_\\\\psi = 0.0942$ for all SM fermions. Same mass scan.\"}], \"baselines\": [\"Internal cross-check: SSM/E6-$\\\\psi$ ratio should be approximately mass-independent (set by the ratio of squared couplings) far from mass thresholds\"], \"metrics\": [{\"name\": \"cross_section_pb\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma(pp \\\\to Z' \\\\to \\\\mu^+\\\\mu^-)$ in pb at each (mass, benchmark) cell.\"}, {\"name\": \"ssm_to_e6_ratio_at_2tev\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma_\\\\mathrm{SSM}/\\\\sigma_\\\\psi$ at $M_{Z'}=2$ TeV.\"}, {\"name\": \"mass_dropoff_factor_ssm\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma_\\\\mathrm{SSM}(M=200)/\\\\sigma_\\\\mathrm{SSM}(M=5500)$.\"}], \"datasets\": [{\"process_id\": \"pp_to_Zprime_to_mumu_general\", \"sqrt_s_TeV\": 13, \"description\": \"$pp \\\\to Z' \\\\to \\\\mu^+\\\\mu^-$ in the general 7-coupling $Z'$ model. Two benchmark coupling tables: SSM and $E_6$-$\\\\psi$.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_ssm_psi_ratio\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the ratio σ_SSM/σ_ψ at M_Z'=2 TeV, √s=13 TeV, and that ratio MUST be in [3, 30].\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p08-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for General $Z'$: SSM vs $E_6$-$\\\\psi$ dimuon cross section vs mass at LHC. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p08-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p08-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): General $Z'$ with 7 chiral couplings $g_{Lu/Ru/Ld/Rd/Le/Re/Lv}$; SSM tabulated values vs $E_6$-$\\\\psi$ universal $g_\\\\psi=0.0942$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p08-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\\\to Z' \\\\to \\\\mu^+\\\\mu^-$ at $\\\\sqrt{s}=13$ TeV, mass scan over 14 points $\\\\{200,\\\\ldots,5500\\\\}$ GeV.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p08-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: Standard 13 TeV proton PDF; widths auto-computed (two-body decays only); 14 mass points x 2 benchmarks = 28 runs.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p08-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p08-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p08-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p08-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p08-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p08-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p08-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — $\\\\sigma_\\\\mathrm{SSM}(2\\\\,\\\\mathrm{TeV}) / \\\\sigma_\\\\psi(2\\\\,\\\\mathrm{TeV})$ in $[3, 30]$.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p08-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — $\\\\sigma_\\\\mathrm{SSM}(M_{Z'}=1\\\\,\\\\mathrm{TeV})$ within $\\\\pm 30\\\\%$ of published value. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p08-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — $\\\\sigma_\\\\mathrm{SSM}$ drops $\\\\geq 4$ orders of magnitude from $M=200$ GeV to $M=5500$ GeV. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p08-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Two curves on log-y $\\\\sigma \\\\in [5\\\\times 10^{-6}, 5\\\\times 10^{-1}]$ pb vs $M_{Z'}\\\\in[200,5500]$ GeV linear: $Z'_\\\\mathrm{SSM}$ green dotted, $Z'_\\\\psi$ blue solid.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p08-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p08-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p08-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p08-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p08-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P08.yaml", "rubric_file": "tasks/physics/rubrics/P08.json"} +{"id": "P09", "domain": "physics", "title": "Reproducing the normalized $|\\eta|$ shape in $\\mu^+\\mu^- \\to b\\bar b$ from a $U_1$ vector leptoquark at $\\sqrt{s}=3$ TeV", "topic": "Reproducing $U_1$ leptoquark $|\\eta|$ shape in $\\mu^+\\mu^- \\to b\\bar b$ at 3 TeV muon collider from arXiv:2104.05720", "domains": ["high-energy-physics", "bsm-phenomenology", "leptoquark", "muon-collider"], "arxiv_id": "2104.05720", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the normalized pseudorapidity shape of the $b$ quark in\n$\\mu^+\\mu^- \\to b\\bar b$ at a 3 TeV muon collider, comparing SM\n($s$-channel $\\gamma^*/Z^*$) against $U_1$ vector-leptoquark\n$t$-channel exchange. The Lagrangian has the standard vector-LQ\nkinetic term plus\n$\\mathcal{L} \\supset (g_U/\\sqrt{2})\\,U_1^\\mu (\\beta_L^{ij}\\,\n\\bar Q_L^i \\gamma_\\mu L_L^j) + \\mathrm{h.c.}$, with only\n$\\beta_L^{32}$ (b-quark to muon) non-zero. The diagnostic observable\nis the normalized $|\\eta|$ distribution of the $b$ quark binned in 13\nbins from 0 to 2.5 (narrow [0, 0.1] first bin then uniform 0.2-wide\nbins), shown in two side-by-side panels at $\\beta_L^{32} = 1.0$ and\n$\\beta_L^{32} = 0.1$, each overlaying SM (gray bar histogram) with\n$m_\\mathrm{LQ}=1$ TeV (blue dashed) and $m_\\mathrm{LQ}=10$ TeV\n(orange dashed).\n\nA credible study (a) implements the $U_1$ vector-LQ Lagrangian with\n$\\kappa_U=\\tilde\\kappa_U=0$ (FeynRules + UFO), (b) runs 5 parton-level\nMadGraph runs (1 SM baseline at 500k events with $\\beta_L^{32}=0$ and\n$m_\\mathrm{LQ}=10^5$ GeV; 4 LQ signal runs at $\\beta_L^{32}\\in\n\\{1.0, 0.1\\}$ x $m_\\mathrm{LQ}\\in\\{1, 10\\}$ TeV with 50k events\neach), (c) extracts the $b$-quark $|\\eta|$ from each event, (d)\nhistograms into the 13 bins and normalizes to total events per run,\n(e) plots two panels side by side with the prescribed style. Research\nquestion: *does the agent reproduce the central-vs-forward angular\nredistribution induced by the $t$-channel $U_1$ exchange in\n$\\mu^+\\mu^- \\to b\\bar b$ from Figure 11 of arXiv:2104.05720?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At $\\\\beta_L^{32} = 1.0$, $m_\\\\mathrm{LQ} = 1$ TeV, the central-bin ($|\\\\eta| < 0.1$) normalized fraction exceeds the SM by $\\\\geq 50\\\\%$ (LQ $t$-channel pulls events forward+central).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At $\\\\beta_L^{32}=0.1$, the normalized $|\\\\eta|$ shape differs from the SM baseline by $\\\\leq 10\\\\%$ in every bin (small-coupling regime collapses to SM).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"At $\\\\beta_L^{32}=1.0$, $m_\\\\mathrm{LQ}=10$ TeV the normalized shape lies between the SM and the $m_\\\\mathrm{LQ}=1$ TeV curves (decoupling limit).\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the normalized $|\\\\eta|$ distributions of $\\\\mu^+\\\\mu^- \\\\to b\\\\bar b$ for SM and $U_1$ leptoquark signal at $\\\\sqrt{s}=3$ TeV muon collider, in the two coupling regimes $\\\\beta_L^{32} \\\\in \\\\{1.0, 0.1\\\\}$ (Figure 11 of arXiv:2104.05720)?\", \"conditions\": [{\"name\": \"sm_baseline_3tev\", \"description\": \"All $\\\\beta_L^{ij} = 0$ (LQ decoupled), $m_\\\\mathrm{LQ} = 10^5$ GeV. 500k events at $\\\\sqrt{s}=3$ TeV, parton level.\"}, {\"name\": \"lq_beta1_3tev\", \"description\": \"$\\\\beta_L^{32} = 1.0$, mass scan $m_\\\\mathrm{LQ} \\\\in \\\\{1, 10\\\\}$ TeV, 50k events each at $\\\\sqrt{s}=3$ TeV.\"}, {\"name\": \"lq_beta01_3tev\", \"description\": \"$\\\\beta_L^{32} = 0.1$, mass scan $m_\\\\mathrm{LQ} \\\\in \\\\{1, 10\\\\}$ TeV, 50k events each at $\\\\sqrt{s}=3$ TeV.\"}], \"baselines\": [\"SM $s$-channel $\\\\mu^+\\\\mu^- \\\\to \\\\gamma^*/Z^* \\\\to b\\\\bar b$ (the $\\\\beta_L^{32}=0$ run)\"], \"metrics\": [{\"name\": \"central_bin_excess_fraction\", \"direction\": \"match_reference\", \"description\": \"Ratio of central-bin ($|\\\\eta| < 0.1$) normalized event fraction in LQ signal vs SM.\"}, {\"name\": \"shape_deviation_l1norm\", \"direction\": \"match_reference\", \"description\": \"$L^1$ norm $\\\\sum_i |f_i^\\\\mathrm{LQ} - f_i^\\\\mathrm{SM}|$ of normalized shape difference per (mass, coupling).\"}, {\"name\": \"decoupling_distance\", \"direction\": \"match_reference\", \"description\": \"Shape distance between $m_\\\\mathrm{LQ}=10$ TeV signal and SM should be smaller than for $m_\\\\mathrm{LQ}=1$ TeV signal.\"}], \"datasets\": [{\"process_id\": \"mumu_to_bbbar_U1\", \"sqrt_s_TeV\": 3, \"description\": \"$\\\\mu^+\\\\mu^- \\\\to b\\\\bar b$ via SM ($s$-channel) + $U_1$ ($t$-channel) at a 3 TeV muon collider, parton level.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_central_bin_excess\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the normalized central-|η|<0.1 fraction for β_L^32=1.0, m_LQ=1 TeV, AND the same fraction for SM, AND the LQ fraction MUST exceed SM by ≥50%.\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p09-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for $U_1$ vector-LQ angular shape in $\\\\mu^+\\\\mu^- \\\\to b\\\\bar b$ at 3 TeV muon collider. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p09-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p09-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): $U_1$ vector LQ in $(\\\\mathbf{3},\\\\mathbf{1},2/3)$ rep with $\\\\kappa_U=\\\\tilde\\\\kappa_U=0$; only $\\\\beta_L^{32}$ (b-quark to muon) non-zero; $g_U=1$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p09-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $\\\\mu^+\\\\mu^- \\\\to b\\\\bar b$ at $\\\\sqrt{s}=3$ TeV via SM $s$-channel and $U_1$ $t$-channel exchange, parton level.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p09-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: Lepton-collider beams (no PDF); 1 SM run (500k events with $\\\\beta_L^{32}=0$, $m_\\\\mathrm{LQ}=10^5$ GeV) + 4 LQ signal runs ($\\\\beta_L^{32}\\\\in\\\\{1.0, 0.1\\\\}$ x $m_\\\\mathrm{LQ}\\\\in\\\\{1, 10\\\\}$ TeV, 50k events each).\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p09-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p09-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p09-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p09-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p09-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p09-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p09-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — At $\\\\beta_L^{32}=1.0$, $m_\\\\mathrm{LQ}=1$ TeV, central-bin $|\\\\eta|<0.1$ normalized fraction exceeds SM by $\\\\geq 50\\\\%$.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p09-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — At $\\\\beta_L^{32}=0.1$, normalized $|\\\\eta|$ shape differs from SM by $\\\\leq 10\\\\%$ in every bin. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p09-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — At $\\\\beta_L^{32}=1.0$, $m_\\\\mathrm{LQ}=10$ TeV shape lies between SM and $m_\\\\mathrm{LQ}=1$ TeV (decoupling). Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p09-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Two side-by-side panels (left: $\\\\beta_L^{32}=1.0$, right: $\\\\beta_L^{32}=0.1$); each panel has SM gray bar histogram + $m_\\\\mathrm{LQ}=1$ TeV blue dashed + $m_\\\\mathrm{LQ}=10$ TeV orange dashed; 13 bins from 0 to 2.5 (narrow [0,0.1] first); $14\\\\times 6$ figure size; y in [0, 0.20].\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p09-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p09-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p09-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p09-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p09-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P09.yaml", "rubric_file": "tasks/physics/rubrics/P09.json"} +{"id": "P10", "domain": "physics", "title": "Reproducing $\\beta_L^{32}$-vs-$m_\\mathrm{LQ}$ exclusion + 5$\\sigma$ discovery contours for $U_1$ at 3 TeV (1 ab$^{-1}$) and 14 TeV (20 ab$^{-1}$) muon colliders", "topic": "Reproducing $U_1$ exclusion + 5$\\sigma$ discovery contours at 3 and 14 TeV muon colliders from arXiv:2104.05720", "domains": ["high-energy-physics", "bsm-phenomenology", "leptoquark", "muon-collider", "statistical-recast"], "arxiv_id": "2104.05720", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the muon-collider sensitivity to a $U_1$ vector\nleptoquark in the coupling-vs-mass plane via a binned-likelihood\nrecast of $\\mu^+\\mu^- \\to b\\bar b$, as a benchmark of the agent's\nability to combine MC parameter scans with statistical recasting\nspanning two orders of magnitude in mass and four orders in coupling.\nThe same $U_1$ Lagrangian as P09 (only $\\beta_L^{32}$ non-zero),\nbut here the per-bin cross section is parameterized as\n$\\sigma_i(m, \\beta) = b_i + \\beta^2 I_i(m) + \\beta^4 J_i(m)$, with\n$I_i(m), J_i(m)$ extracted by solving a $2\\times 2$ linear system from\ntwo reference $\\beta$ values. The diagnostic figure shows 95% CL\nexclusion (dashed) and 5$\\sigma$ discovery (solid) contours in\n($m_\\mathrm{LQ}$, $\\beta_L^{32}$) at $\\sqrt{s}=3$ TeV (1 ab$^{-1}$,\nred) and $\\sqrt{s}=14$ TeV (20 ab$^{-1}$, purple).\n\nA credible study (a) implements the $U_1$ Lagrangian (FeynRules + UFO),\n(b) runs SM baselines (100k events at 3 and 14 TeV) plus 4 LQ signal\nscans ($\\sqrt{s}\\in\\{3,14\\}$ TeV x $\\beta_L^{32}\\in\\{1.0, 2.0\\}$) over\n17 mass points $m_\\mathrm{LQ} \\in \\{1.0, 1.5, 2.0, 3.0, 4.0, 5.0,\n6.0, 7.0, 8.0, 10, 15, 20, 30, 40, 50, 60, 70\\}$ TeV with 50k events\neach, (c) bins each run in 10 equal-width $|\\eta|$ bins, (d) extracts\n$I_i, J_i$ per (mass, $\\sqrt{s}$), (e) computes the binned\nlog-likelihood ratio for both the exclusion null hypothesis ($n_i\n= b_i^\\mathrm{events}$) and the discovery null ($n_i = \\mu_i$),\n(f) finds the $\\beta$ where $-2\\log\\lambda$ crosses $\\chi^2(10,\n0.95) = 18.307$ (exclusion) or 48.2 (5$\\sigma$ discovery), and\n(g) plots the four contours on log-log axes ($m_\\mathrm{LQ}\\in\n[1, 75]$ TeV, $\\beta\\in[10^{-3}, 2]$). Research question: *does the\nagent reproduce the muon-collider $U_1$ exclusion + discovery\ncontours from Figure 12 of arXiv:2104.05720?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At $\\\\sqrt{s}=14$ TeV with 20 ab$^{-1}$, the 95% CL exclusion contour reaches $\\\\beta_L^{32} \\\\leq 0.01$ at $m_\\\\mathrm{LQ} = 10$ TeV (matching the published value within a factor of 2).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The 14 TeV exclusion contour extends to higher $m_\\\\mathrm{LQ}$ than the 3 TeV exclusion contour at every fixed $\\\\beta_L^{32}$ in $[10^{-2}, 1]$ (higher energy + luminosity gives more reach).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"5$\\\\sigma$ discovery contours always lie above the corresponding exclusion contours in $\\\\beta_L^{32}$ at every mass (discovery requires stronger signal than exclusion).\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the 95% CL exclusion and 5$\\\\sigma$ discovery contours for the $U_1$ leptoquark in the $(m_\\\\mathrm{LQ}, \\\\beta_L^{32})$ plane at 3 TeV (1 ab$^{-1}$) and 14 TeV (20 ab$^{-1}$) muon colliders (Figure 12 of arXiv:2104.05720)?\", \"conditions\": [{\"name\": \"sm_baselines\", \"description\": \"Two SM-only runs (100k events each) at $\\\\sqrt{s}\\\\in\\\\{3, 14\\\\}$ TeV, $\\\\beta_L^{ij}=0$. Define per-bin SM cross section $b_i$ in 10 $|\\\\eta|$ bins.\"}, {\"name\": \"lq_signal_3tev\", \"description\": \"Mass scan over 17 points $m_\\\\mathrm{LQ} \\\\in [1, 70]$ TeV at $\\\\sqrt{s}=3$ TeV, with $\\\\beta_L^{32}\\\\in\\\\{1.0, 2.0\\\\}$, 50k events each.\"}, {\"name\": \"lq_signal_14tev\", \"description\": \"Same mass scan at $\\\\sqrt{s}=14$ TeV with $\\\\beta_L^{32}\\\\in\\\\{1.0, 2.0\\\\}$, 50k events each.\"}], \"baselines\": [\"SM $\\\\mu^+\\\\mu^- \\\\to \\\\gamma^*/Z^* \\\\to b\\\\bar b$ at $\\\\sqrt{s} = 3$ TeV and 14 TeV\"], \"metrics\": [{\"name\": \"exclusion_beta_at_10tev_14tev\", \"direction\": \"match_reference\", \"description\": \"$\\\\beta_L^{32}$ at the 95% CL exclusion contour at $m_\\\\mathrm{LQ}=10$ TeV, $\\\\sqrt{s}=14$ TeV.\"}, {\"name\": \"discovery_beta_at_10tev_14tev\", \"direction\": \"match_reference\", \"description\": \"$\\\\beta_L^{32}$ at the 5$\\\\sigma$ discovery contour at $m_\\\\mathrm{LQ}=10$ TeV, $\\\\sqrt{s}=14$ TeV.\"}, {\"name\": \"I_J_coefficients_per_mass\", \"direction\": \"match_reference\", \"description\": \"$(I_i, J_i)$ per $|\\\\eta|$ bin and mass point, extracted from the $\\\\beta\\\\in\\\\{1, 2\\\\}$ runs; positive-definite check on $J_i$.\"}], \"datasets\": [{\"process_id\": \"mumu_to_bbbar_U1_3and14tev\", \"sqrt_s_TeV\": 14, \"description\": \"$\\\\mu^+\\\\mu^- \\\\to b\\\\bar b$ via SM + $U_1$ exchange at muon colliders, two energy points.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_14tev_excl_reach\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the 95% CL exclusion β_L^32 reach at m_LQ=10 TeV, √s=14 TeV, 20 ab^-1; the value MUST be ≤ 0.02 (published ≤0.01 within a factor of 2).\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p10-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for $U_1$ exclusion + 5$\\\\sigma$ discovery contours at 3 TeV (1 ab$^{-1}$) and 14 TeV (20 ab$^{-1}$) muon colliders. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p10-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p10-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Same $U_1$ Lagrangian as P09; per-bin cross section parameterized as $\\\\sigma_i(m, \\\\beta) = b_i + \\\\beta^2 I_i(m) + \\\\beta^4 J_i(m)$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p10-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $\\\\mu^+\\\\mu^- \\\\to b\\\\bar b$ at $\\\\sqrt{s}\\\\in\\\\{3, 14\\\\}$ TeV, parton-level.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p10-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: Lepton-collider beams; 2 SM baselines (100k events each at 3 and 14 TeV) + 4 LQ signal scans ($\\\\sqrt{s}\\\\in\\\\{3,14\\\\}$ x $\\\\beta_L^{32}\\\\in\\\\{1.0, 2.0\\\\}$) over 17 mass points $m_\\\\mathrm{LQ}\\\\in\\\\{1.0,\\\\ldots,70\\\\}$ TeV with 50k events each.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p10-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p10-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p10-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p10-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p10-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p10-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p10-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — At $\\\\sqrt{s}=14$ TeV with 20 ab$^{-1}$, 95% CL exclusion reaches $\\\\beta_L^{32} \\\\leq 0.01$ at $m_\\\\mathrm{LQ}=10$ TeV (within factor 2 of published).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p10-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — 14 TeV exclusion contour extends to higher $m_\\\\mathrm{LQ}$ than 3 TeV contour at every fixed $\\\\beta_L^{32}\\\\in[10^{-2}, 1]$. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p10-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — 5$\\\\sigma$ discovery contours always lie above (i.e. require larger $\\\\beta_L^{32}$ than) corresponding exclusion contours at every mass. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p10-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Log-log plot, x-axis $m_\\\\mathrm{LQ}\\\\in[1,75]$ TeV log, y-axis $\\\\beta_L^{32}\\\\in[10^{-3}, 2]$ log, 1:1 aspect; 3 TeV (red) and 14 TeV (purple) contours; dashed = exclusion, solid = discovery (4 contours total).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p10-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p10-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p10-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p10-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p10-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P10.yaml", "rubric_file": "tasks/physics/rubrics/P10.json"} +{"id": "Q01", "domain": "quantum", "title": "Comparing quantum data encoding strategies for variational classifiers", "topic": "Comparing quantum data encoding strategies (angle, amplitude, IQP, ZZ feature map) for variational quantum classifiers on small low-dimensional binary classification tasks", "domains": ["quantum-machine-learning", "data-encoding", "feature-maps"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1800, "synthesis": "Quantum data encoding is widely regarded as the bottleneck of variational\nquantum machine learning. The way classical features are loaded into a\nquantum state determines what functions the subsequent variational ansatz\ncan express. Three encoding families are compared here: angle encoding\n(single-qubit rotations only, no input entanglement), amplitude encoding\n(information-dense state preparation that loads the classical vector as\nstate amplitudes), and the ZZ feature map (Hadamards plus diagonal\npairwise phase rotations, injecting entanglement at the input layer).\n\nThe research question is direct. Holding the variational ansatz, the\nclassical optimizer, the simulator backend, and the dataset preprocessing\nfixed, which encoding produces the best test accuracy on a moderately\ndifficult 6D binary classification task, and does at least one quantum\nencoding approach the better of two classical baselines (logistic\nregression and a small MLP)?\n\nImplementation guidance is provided by the `quantum-qiskit` skill, which\nis automatically injected into the stage-10 code generation prompt for\nthis topic. Use the skill's reference patterns verbatim: import\n`ZFeatureMap`, `StatePreparation`, and `ZZFeatureMap` from\n`qiskit.circuit.library`; use `qiskit_machine_learning.algorithms.VQC`\nwith the reference Sampler primitive for training; never roll a custom\noptimization loop. Hand-written gate sequences in place of these\nlibrary classes have been observed to produce degenerate ablations\n(three encodings collapsing to nearly identical computations) and will\nfail the rubric.\n\nExperimental protocol. All three quantum encodings share EfficientSU2\nreps=1 linear entanglement as the variational ansatz, COBYLA maxiter=200\nas the optimizer, and the noiseless statevector backend as the simulator.\nTwo classical baselines (LogisticRegression and MLPClassifier) run on\nthe same 6D features. Five random seeds per cell across 5 conditions\nyields 25 total cells. Each per-seed result is logged to stdout as a\nsingle line with the prefix `METRIC_RESULT` followed by a JSON object\ncontaining `condition`, `dataset`, `seed`, and `test_accuracy` keys so\nthe autoclaw sandbox parser can collect structured metrics.\n\nResearch question: *On a moderately difficult 6D binary classification\ntask (LogisticRegression baseline expected around 0.65 to 0.80), does\nthe choice of quantum encoding (angle, amplitude, ZZ) measurably change\nvariational classifier accuracy, and does at least one quantum encoding\napproach the better classical baseline?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The best of the three quantum encodings (5-seed mean) achieves test_accuracy within 5 absolute percentage points of the BETTER of the two classical baselines (logistic_regression and mlp_classifier, 5-seed means) on synthetic_classification_6d.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The spread of test_accuracy across the three quantum encodings (max minus min of the 5-seed means) is at least 5 absolute percentage points, demonstrating that encoding choice produces a measurable effect rather than a wash.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"zz_feature_map test_accuracy (5-seed mean) is at least as high as angle_encoding test_accuracy (within 2 absolute percentage points or higher), consistent with the conjecture that entanglement-rich input encoding does not hurt and may help on this task.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"On a moderately difficult 6D binary classification task, does the choice of quantum data encoding (angle, amplitude, ZZ feature map) measurably change variational classifier accuracy, and does at least one encoding approach the classical baseline?\", \"conditions\": [{\"name\": \"angle_encoding\", \"description\": \"qiskit.circuit.library.ZFeatureMap(feature_dimension=6, reps=1) as the data encoding circuit. Each feature x_i is loaded as H followed by RZ(x_i) on qubit i. No entangling gates in the feature map.\"}, {\"name\": \"amplitude_encoding\", \"description\": \"qiskit.circuit.library.StatePreparation on the L2-normalized, zero-padded input vector (length 64 = 2**6) as the data encoding circuit. Implementation: x_normalized = x / ||x||; x_padded = np.concatenate([x_normalized, np.zeros(64 - 6)]); StatePreparation(x_padded). The padded state is unit norm by construction.\"}, {\"name\": \"zz_feature_map\", \"description\": \"qiskit.circuit.library.ZZFeatureMap(feature_dimension=6, reps=2, entanglement='linear') as the data encoding circuit. Two repetitions of H + RZ(x_i) on each qubit + RZZ(x_i * x_j) on linear nearest-neighbor pairs.\"}], \"baselines\": [\"logistic_regression: sklearn.linear_model.LogisticRegression(max_iter=1000) on the same 6D features.\", \"mlp_classifier: sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(16,), activation='relu', solver='adam', max_iter=500, random_state=seed) on the same 6D features.\", \"Both baselines reported per seed; both contribute to the H1 comparison (the BETTER of the two is the reference). Neither contributes to the primary quantum_test_accuracy_mean metric.\"], \"metrics\": [{\"name\": \"quantum_test_accuracy_mean\", \"direction\": \"maximize\", \"description\": \"Primary metric. Mean of the 5-seed test_accuracy means across the THREE quantum encodings ONLY. The classical baseline is reported separately and does NOT contribute to this metric. This isolates the refinement signal from the baseline.\"}, {\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Per-condition mean accuracy on the 20% held-out test split, averaged over 5 random seeds. Reported per condition (angle, amplitude, zz_feature_map, logistic_regression).\"}, {\"name\": \"best_baseline_accuracy\", \"direction\": \"maximize\", \"description\": \"max(logistic_regression_5seed_mean, mlp_classifier_5seed_mean). Used as the reference point in H1 (the stronger of the two classical baselines).\"}], \"datasets\": [{\"name\": \"synthetic_classification_6d\", \"source\": \"sklearn.datasets.make_classification\", \"description\": \"make_classification(n_samples=400, n_features=6, n_informative=4, n_redundant=1, n_repeated=0, n_clusters_per_class=2, class_sep=0.4, flip_y=0.05, random_state=seed). class_sep is set to 0.4 (not 1.0) so the LogisticRegression baseline lands around 0.65-0.80 instead of saturating near 1.0, leaving headroom for the quantum encodings to differ. Apply StandardScaler fit on the train split only. 80/20 train/test split.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1800}}", "requirements": "", "rubric": "{\"id\": \"q01-root\", \"requirements\": \"A minimal pilot study comparing three quantum data encoding strategies (angle, amplitude, ZZ feature map) for a variational quantum classifier on ONE moderately difficult 6D binary classification dataset (synthetic_classification_6d with class_sep=0.4). The agent must (a) implement all three encodings as drop-in feature maps for a shared 6-qubit EfficientSU2 ansatz, (b) train each (encoding, seed) cell using qiskit_machine_learning.algorithms.VQC + COBYLA over 5 random seeds, (c) report test accuracy per quantum cell and a classical logistic_regression baseline on the same data, and (d) produce a writeup that assigns supported / refuted / inconclusive verdicts to H1, H2, H3 with numerical evidence.\", \"judging_note\": \"Quantum ML pilots are scored on (i) whether all three encodings are implemented per qiskit convention, (ii) whether ansatz / optimizer / simulator are held fixed across encodings so the encoding axis is isolated, (iii) whether the quantum cells produce non-trivial accuracy (not near 0.5 random for all 3 encodings, which would indicate a training bug rather than an encoding-specific effect), and (iv) whether per-hypothesis claims are backed by numerical evidence. Quantitative result leaves are scored on a graded scale: 100% if the hypothesis threshold is cleanly met, 67% if the trend is in the predicted direction but does not clear the numerical threshold, 33% if the trend is ambiguous, 0% if the result contradicts the hypothesis or evidence is missing.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q01-code\", \"requirements\": \"Code-development bucket: all 3 encodings implemented per qiskit convention, shared ansatz + optimizer + simulator + training framework are wired identically across cells, and per-cell metrics are logged in the expected METRIC_RESULT JSON format.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q01-code-encodings\", \"requirements\": \"All three encodings are implemented by IMPORTING and INSTANTIATING the canonical classes from qiskit.circuit.library by name, NOT by hand-writing the gate sequences. Concretely the agent's code MUST contain (grep-verifiable): 'from qiskit.circuit.library import' followed by at least the symbols ZFeatureMap, StatePreparation, and ZZFeatureMap; and the three encoding builders must instantiate exactly those classes (angle_encoding = ZFeatureMap(feature_dimension=6, reps=1); amplitude_encoding wraps StatePreparation on a 64D L2-normalized zero-padded vector; zz_feature_map = ZZFeatureMap(feature_dimension=6, reps=2, entanglement='linear')). Hand-written H/RZ/RZZ loops in place of these library classes FAIL this leaf. Additionally, a sanity check at runtime confirms that the three encodings produce DIFFERENT output statevectors for a fixed non-zero input (if they produce identical statevectors, the dispatch is broken and this leaf scores 0). Class bodies are non-trivial (not 'class X(Base): pass' shells).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q01-code-shared-training\", \"requirements\": \"Training uses qiskit_machine_learning.algorithms.VQC with the reference Sampler primitive. The variational ansatz (EfficientSU2 num_qubits=6 reps=1 entanglement='linear'), the classical optimizer (COBYLA maxiter=200), and the simulator backend (AerSimulator method='statevector') are identical across the 3 quantum encoding cells. Only the feature_map argument is swapped. No hand-rolled training loop in place of VQC.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"q01-code-metric-logging\", \"requirements\": \"Each per-(condition, seed) result is emitted to stdout as a single line beginning with METRIC_RESULT followed by a JSON object whose keys include at minimum: condition (one of angle_encoding / amplitude_encoding / zz_feature_map / logistic_regression / mlp_classifier), dataset (synthetic_classification_6d), seed (integer 0..4), test_accuracy (float in [0,1]). Both classical baselines (LogisticRegression and MLPClassifier) are logged in the same format.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q01-exec\", \"requirements\": \"Execution-validity bucket: all cells ran and produced numerically valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q01-exec-cells-ran\", \"requirements\": \"At least 25 cells out of 25 expected (5 conditions: 3 quantum encodings + LogisticRegression + MLPClassifier x 1 dataset x 5 seeds) completed without unhandled errors and produced an accuracy value. Missing more than 2 cells (10 percent) without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q01-exec-numerical\", \"requirements\": \"Numerical validity: no NaN or Inf in test_accuracy. Every test_accuracy lies in [0, 1]. No quantum cell collapses to a degenerate single-class prediction. At least one quantum encoding achieves test_accuracy strictly greater than 0.55 on at least 1 of 5 seeds (rules out the failure mode where all quantum cells stay near 0.5 random-chance because optimization never actually ran). The 3 quantum encodings MUST NOT produce identical test_accuracy across all 5 seeds (this is the ABLATION FAILURE pattern observed in a prior failed run: if angle/amplitude/zz produce bit-for-bit identical accuracies on every seed, the feature_map argument is being silently ignored by the training code, and this leaf scores 0). Training takes nontrivial wall time per cell (at least a few seconds; sub-1s per cell means VQC.fit was not actually invoked).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q01-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q01-result-h1-quant\", \"requirements\": \"Quantitative test of H1. Does the best of the 3 quantum encodings (5-seed mean) achieve test_accuracy within 5 absolute percentage points of the BETTER classical baseline (max of LogisticRegression and MLPClassifier 5-seed means)? 100% if gap is <= 5pp, 67% if gap is <= 10pp, 33% if gap is <= 20pp, 0% if quantum is more than 20pp worse than the best baseline (or either baseline is missing).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q01-result-h2-quant\", \"requirements\": \"Quantitative test of H2. Is max(encoding 5-seed mean) - min(encoding 5-seed mean) across the 3 quantum encodings at least 5 absolute percentage points? 100% if spread >= 5pp, 67% if spread >= 2pp, 33% if spread > 0, 0% if all three encodings produce identical means (suggests training did not actually depend on the encoding choice).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q01-result-h3-quant\", \"requirements\": \"Quantitative test of H3. Is zz_feature_map test_accuracy (5-seed mean) at least as high as angle_encoding test_accuracy (within 2 absolute pp lower, or higher)? 100% if zz >= angle - 2pp, 67% if zz is 2-5pp below angle, 33% if zz is 5-10pp below, 0% if zz is more than 10pp below angle.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q01-result-writeup\", \"requirements\": \"Writeup of at least 200 words (in submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1, H2, H3 backed by specific mean accuracies (per encoding plus baseline) and the gap / spread values. Identifies a dominant systematic uncertainty (seed variance, optimizer convergence on the simpler EfficientSU2 reps=1 ansatz, class_sep=0.4 difficulty level). Discusses whether the result would change at higher difficulty or with a deeper ansatz.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q01.yaml", "rubric_file": "tasks/quantum/rubrics/Q01.json"} +{"id": "Q02", "domain": "quantum", "title": "Entangling-gate ablation in a variational quantum classifier", "topic": "Ablating entangling gates in a variational quantum classifier: how much classification accuracy is actually attributable to CNOTs versus single-qubit rotations", "domains": ["quantum-machine-learning", "entanglement", "ablation-study"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1200, "synthesis": "Quantum machine learning models routinely use parameterized circuits\nthat mix single-qubit rotations with entangling two-qubit gates,\ntypically CNOTs. The common narrative is that entanglement is the\nsource of quantum advantage. If a variational classifier with no\nentangling gates whatsoever can match the accuracy of one with full\nentanglement, that narrative breaks down for this task class. The\npurpose of this study is to measure the actual contribution of\nentanglement in a simple variational quantum classifier by cleanly\nremoving CNOTs in a controlled monotonic manner.\n\nThe design isolates entanglement to one source. The data encoding is\nfixed to angle encoding (single-qubit rotations only, no entangling\ngates). The variational ansatz template is EfficientSU2 with reps=3,\nwhich by default places a linear chain of CNOTs between each rotation\nblock (5 CNOTs per entangling layer, 3 layers, 15 CNOTs total for\n6 qubits). We ablate by replacing some or all of those CNOTs with\nidentity, while keeping every single-qubit rotation parameter intact.\nThis isolates the gain attributable to two-qubit entangling structure\nfrom the gain attributable to the trainable single-qubit basis change.\nhalf_entanglement uses an independent random Bernoulli(0.5) mask over\nthe 15 CNOT positions per cell, seeded deterministically so the\nablation is reproducible but not aligned with any fixed pattern.\n\nA parameter-matched classical baseline (sklearn MLPClassifier with a\nhidden layer sized so its total parameter count is within 10 percent\nof the VQC parameter count) tells us whether either quantum condition\nis competitive with a trivial classical alternative on the same data.\nIf the no-entanglement VQC underperforms the classical MLP, the\nconclusion is that on these tasks, entangling layers are not just\nhelpful but necessary for the variational quantum model to be a\nviable choice at all.\n\nDatasets are deliberately shared with Q01 (encoding comparison) so\nresults can be read across both studies. If the cross-topic story\nholds, the encoding family that benefits most from ansatz CNOTs in\nQ02 should also be the family that performed worst in Q01 (because\nthe ansatz CNOTs are compensating for the encoding's lack of input\nentanglement). The current manifest does not require that\ncross-comparison, but the writeup is encouraged to discuss it.\n\nResearch question: *In a variational quantum classifier on low\ndimensional binary classification, how much of the model's accuracy\nis actually due to ansatz entangling gates, and does the accuracy\ndegrade monotonically as CNOTs are removed?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"full_entanglement achieves mean test_accuracy (averaged over 5 seeds) at least 5 absolute percentage points higher than no_entanglement on at least 2 of 3 datasets.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"half_entanglement mean test_accuracy lies strictly between full_entanglement and no_entanglement (within seed-mean noise of 1 absolute percentage point) on at least 2 of 3 datasets, demonstrating that the contribution of entangling layers is approximately monotonic in CNOT count.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"On at least 1 of 3 datasets, classical_baseline (parameter-matched MLPClassifier) achieves test_accuracy at least as high as no_entanglement (5-seed mean). This sanity check rules out a trivial-task explanation: if removing all entanglement leaves the VQC weaker than a tiny classical net, then 'no entanglement' is not a viable QML setting on these tasks.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"In a variational quantum classifier on low dimensional binary classification, how much of the model's accuracy is actually due to ansatz entangling gates, and does the accuracy degrade monotonically as CNOTs are removed?\", \"conditions\": [{\"name\": \"full_entanglement\", \"description\": \"Feature map: ZFeatureMap(feature_dimension=6, reps=1). Ansatz: EfficientSU2(num_qubits=6, reps=3, entanglement='linear'). All 15 CNOTs from the 3 entangling layers are kept. This is the baseline VQC.\"}, {\"name\": \"half_entanglement\", \"description\": \"Same feature_map and ansatz template as full_entanglement, but each of the 15 CNOTs is independently dropped according to a random Bernoulli(0.5) mask. The mask is generated per cell using a derived random state: np.random.RandomState(seed + 10000).binomial(1, 0.5, size=15), where seed is the per-cell run seed. Dropped CNOTs are replaced with identity. Each cell records its realized cnot_count (expected ~7.5, variance per draw). Trainable parameters unchanged.\"}, {\"name\": \"no_entanglement\", \"description\": \"Same feature_map and rotation blocks as full_entanglement, but EVERY entangling gate is replaced with identity. The circuit reduces to a tensor product of independent single-qubit gates. CNOT count is 0. Trainable parameters unchanged.\"}, {\"name\": \"classical_baseline\", \"description\": \"sklearn.neural_network.MLPClassifier with hidden_layer_sizes chosen so the total trainable parameter count is within 10 percent of the VQC (EfficientSU2 reps=3 on 6 qubits has 48 rotation parameters; an MLP with one hidden layer of size 6 has 6*6 + 6 + 6*1 + 1 = 49 params). Use hidden_layer_sizes=(6,), activation='relu', max_iter=500, solver='lbfgs', random_state matched to the corresponding seed.\"}], \"baselines\": [\"no_entanglement is the within-quantum ablation baseline\", \"classical_baseline is the cross-paradigm sanity baseline\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Mean over 5 seeds of held-out 20% test split accuracy.\"}, {\"name\": \"convergence_iterations\", \"direction\": \"minimize\", \"description\": \"Number of COBYLA iterations (or MLP solver iterations for classical_baseline) until training loss change drops below 1e-4 across 5 consecutive evaluations.\"}, {\"name\": \"cnot_count\", \"direction\": \"minimize\", \"description\": \"Number of CNOT gates remaining in the variational circuit (feature map + ansatz) after ablation. Informational metric. Expected: full=15, half=mean over draws (~7.5), no=0, classical_baseline=0.\"}], \"datasets\": [{\"name\": \"synthetic_classification_6d\", \"source\": \"sklearn.datasets.make_classification\", \"description\": \"make_classification(n_samples=200, n_features=6, n_informative=4, n_redundant=1, n_repeated=0, n_clusters_per_class=2, class_sep=1.0, flip_y=0.05, random_state=seed). Binary classification, native 6D. Apply StandardScaler. 80/20 train/test split. Same configuration as Q01.\"}, {\"name\": \"wine_binary_pca6\", \"source\": \"sklearn.datasets.load_wine + sklearn.decomposition.PCA\", \"description\": \"load_wine (178 samples, 13 features, 3 classes). Select classes 0 and 1 only (binary subset, ~130 samples). Apply StandardScaler then PCA(n_components=6). 80/20 train/test split. Same configuration as Q01.\"}, {\"name\": \"breast_cancer_pca6\", \"source\": \"sklearn.datasets.load_breast_cancer + sklearn.decomposition.PCA\", \"description\": \"load_breast_cancer (569 samples, 30 features). Apply StandardScaler then PCA(n_components=6). 80/20 train/test split. Same configuration as Q01.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1200}}", "requirements": "", "rubric": "{\"id\": \"q02-root\", \"requirements\": \"A controlled ablation study of entangling gates in a variational quantum classifier. Holding encoding (angle), ansatz template (EfficientSU2 reps=2), optimizer (COBYLA), and datasets fixed (matching Q01), the agent must implement full / half / no entanglement variants by selectively replacing CNOTs with identity in the ansatz, plus a parameter-matched classical MLP baseline. Score H1 (full > no by >=5pp), H2 (half between full and no), H3 (classical >= no on at least 1 dataset) with numerical evidence.\", \"judging_note\": \"Ablation studies are scored on (i) whether the CNOT-removal mechanism actually changes the circuit as described (verified by inspecting cnot_count per condition), (ii) whether every other variable is held fixed (same encoding, same rotation parameters, same datasets, same optimizer, same seeds), and (iii) whether the H1/H2/H3 verdicts are backed by numerical evidence. The classical baseline must be parameter-matched (within 10 percent of the VQC's trainable parameter count) to be a meaningful sanity check.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q02-code\", \"requirements\": \"Code-development bucket: the CNOT ablation mechanism is correctly implemented, the classical baseline is parameter-matched, and the evaluation loop holds all non-ablated variables fixed.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q02-code-ablation-mechanism\", \"requirements\": \"All three quantum conditions (full_entanglement, half_entanglement, no_entanglement) use the same ZFeatureMap and the same EfficientSU2(reps=3) rotation parameters. CNOTs are removed by replacing them with identity (not by re-parameterizing the ansatz). The resulting circuits have cnot_count = 15 for full, 0 for no, and a random Bernoulli(0.5) draw over 15 positions for half (mean ~7.5, verified by recording the realized count per cell). The half_entanglement mask is generated via np.random.RandomState(seed + 10000).binomial(1, 0.5, size=15) to be reproducible.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q02-code-classical-baseline\", \"requirements\": \"classical_baseline uses sklearn MLPClassifier with a hidden layer size chosen so the total parameter count is within 10 percent of the VQC's 48 trainable parameters (e.g. hidden_layer_sizes=(6,) gives 49 params for 6-dim input). max_iter, activation, solver, and random_state are specified explicitly. The MLP is trained and evaluated on the SAME train/test splits as the VQC conditions.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"q02-code-pipeline\", \"requirements\": \"Nested loop over 4 conditions x 3 datasets x at least 5 seeds. Each cell records test_accuracy, convergence_iterations, and cnot_count. The same seed produces the same train/test split across the 4 conditions on each dataset (so the 4 conditions can be paired-compared).\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q02-exec\", \"requirements\": \"Execution-validity bucket: all cells ran and produced numerically valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q02-exec-cells-ran\", \"requirements\": \"At least 60 cells out of 60 expected (4 conditions x 3 datasets x 5 seeds) completed and produced an accuracy value. Missing cells must be documented; missing more than 6 cells (10 percent) without justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q02-exec-numerical\", \"requirements\": \"Numerical validity: no NaN or Inf in test_accuracy. Every test_accuracy in [0, 1]. cnot_count matches the manifest expectation per condition (full=15, half=Bernoulli draw mean ~7.5 with all values in [0,15], no=0, classical_baseline=0). At least one cell of each condition produces a non-degenerate classifier (not predicting a single class for all test samples).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q02-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q02-result-h1-quant\", \"requirements\": \"Quantitative test of H1. Does full_entanglement beat no_entanglement by at least 5 absolute percentage points (5-seed mean) on at least 2 of 3 datasets? 100% if cleanly met, 67% if gap >= 2pp on 2/3, 33% if full > no on 2/3 but gap <2pp, 0% if full does not consistently beat no.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q02-result-h2-quant\", \"requirements\": \"Quantitative test of H2. Does half_entanglement test_accuracy lie strictly between full and no (within 1pp seed-mean noise) on at least 2 of 3 datasets? 100% if cleanly monotonic on 2/3, 67% if monotonic on 1/3, 33% if half is non-monotonic but within 2pp of one of the endpoints, 0% otherwise.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q02-result-h3-quant\", \"requirements\": \"Quantitative test of H3. On at least 1 of 3 datasets, does classical_baseline achieve test_accuracy at least as high as no_entanglement (5-seed mean)? 100% if classical >= no on at least 1/3, 50% if classical is within 2pp of no on at least 1/3, 0% if classical never reaches no_entanglement minus 2pp.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q02-result-writeup\", \"requirements\": \"Writeup of at least 200 words (in submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1, H2, H3 with numerical evidence (specific mean accuracies for each condition x dataset, and the gap), a discussion of monotonicity, and an honest acknowledgment of whether the classical_baseline sanity check passes. The writeup is encouraged but not required to cross-reference Q01 (encoding) findings if available.\", \"weight\": 14.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q02.yaml", "rubric_file": "tasks/quantum/rubrics/Q02.json"} +{"id": "Q03", "domain": "quantum", "title": "Classical optimizer comparison for VQE on H2 under finite shot noise", "topic": "Benchmarking classical optimizers (SPSA, COBYLA, L-BFGS-B, ADAM) for VQE convergence on H2 under finite shot noise across multiple shot budgets and bond lengths", "domains": ["variational-quantum-eigensolver", "quantum-chemistry", "classical-optimization"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "shots_to_chemical_accuracy", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 1200, "synthesis": "The Variational Quantum Eigensolver (VQE) is the canonical algorithm\nfor near-term quantum chemistry. The quality of a VQE result depends\non the ansatz, but it also critically depends on the classical\noptimizer that updates variational parameters using noisy energy\nestimates from shot-based measurement. Different optimizers exploit\ngradient information very differently. Gradient-free methods such as\nCOBYLA and Nelder-Mead do not need explicit gradients and may be\nrobust to shot noise but converge slowly in high dimensions.\nSimultaneous Perturbation Stochastic Approximation (SPSA) approximates\nthe gradient using just two function evaluations regardless of\nparameter count, trading gradient quality for evaluation count and\noften dominating in low-shot regimes. Gradient-based methods such as\nL-BFGS-B and ADAM need explicit or finite-difference gradients, which\nare expensive under shot noise because each partial derivative is\nestimated from a noisy function value.\n\nThe choice of optimizer is rarely studied head-to-head under a fixed\nshot budget. Most VQE papers fix the optimizer and tune everything\nelse around it. Here we hold ansatz (hardware-efficient EfficientSU2\nreps=2), Hamiltonian (H2 in STO-3G basis), and initial parameters\n(small random Gaussian) fixed, and vary only the optimizer and the\nshot budget per energy evaluation. The diagnostic question is which\noptimizer reaches chemical accuracy (within 1.6 mHa of the FCI\nreference) using the fewest cumulative shots.\n\nA credible study uses at least two molecular geometries to avoid\noverfitting to a single energy landscape. We use H2 at two bond\nlengths: the equilibrium distance (0.74 angstrom) where the ground\nstate is near Hartree-Fock and the optimization landscape is well\nbehaved, and a stretched geometry (1.5 angstrom) where multireference\ncharacter makes the landscape harder and tests optimizer robustness.\nEach optimizer is run at a SINGLE fixed shot budget of 1024 shots per\nenergy evaluation. This is chosen as a representative middle-ground\nvalue: low enough that shot noise is non-trivial (per-eval std ~15 mHa,\nwell above the 1.6 mHa chemical accuracy threshold, so the running-mean\nconvergence criterion matters), but high enough that some optimizers\ncan plausibly converge within their iteration budgets. All cells use\n3 random seeds per (optimizer, geometry) cell, giving 4 optimizers x\n1 shot budget x 2 geometries x 3 seeds = 24 total cells. Cumulative\nshots is the sum of shots used across all energy evaluations\n(including finite-difference gradient evaluations where applicable).\nThe Hamiltonian itself is constructed via\nqiskit_nature.second_q.drivers.PySCFDriver (qiskit_nature and pyscf\nare required dependencies for this topic; hardcoded Pauli string\nfallbacks are NOT accepted).\n\nImplementation contract (see quantum-qiskit skill for the reference\ncode). In qiskit 2.x, `qiskit_algorithms.VQE` and\n`qiskit_nature.second_q.algorithms` are NOT importable (they depend on\nthe removed `qiskit.primitives.BaseEstimator` V1 interface). VQE MUST\nbe implemented as a manual optimization loop:\nenergy(theta) = estimator.run([(bound_ansatz, qubit_op)]).result()[0].data.evs + e_nuclear,\nusing `qiskit.primitives.StatevectorEstimator` (the V2 primitive that\nworks in qiskit 2.x) for the energy evaluation, and one of the four\noptimizer classes from `qiskit_algorithms.optimizers` (SPSA, COBYLA,\nL_BFGS_B, ADAM) for the parameter update, called via\n`optimizer.minimize(energy, initial_point)`. Each call to `energy`\ncounts as one evaluation; cumulative shots is\n`n_evaluations * shots_per_eval`. The Hamiltonian itself is constructed\nvia qiskit_nature.second_q.drivers.PySCFDriver +\nqiskit_nature.second_q.mappers.ParityMapper (these submodules ARE safe\nto import in qiskit 2.x — only the qiskit_nature.second_q.algorithms\nmodule is broken). With ParityMapper and 2-qubit reduction\n(num_particles=(1,1)), the H2/STO-3G Hamiltonian is 2 qubits, not 4.\nImplement shot noise by adding Gaussian noise to each energy value\nwith sigma = ||H||_1 / sqrt(shots_per_eval). The convergence criterion\nfor shots_to_chemical_accuracy is the cumulative shots when a 5-eval\nrunning mean energy stays within 1.6 mHa of E_FCI for 5 consecutive\nevaluations; if not reached, report None or a sentinel distinct from\nthe upper bound so analysis can flag the cell as \"did not converge\"\nrather than treating the budget cap as a measurement.\n\nResearch question: *Under finite shot noise, which classical optimizer\n(SPSA, COBYLA, L-BFGS-B, ADAM) reaches VQE chemical accuracy using the\nfewest cumulative shots, and how does the answer depend on shot budget\nper evaluation and on Hamiltonian difficulty (equilibrium vs stretched\nH2)?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At 1024 shots per energy evaluation, SPSA reaches chemical accuracy (|E_running_mean - E_FCI| < 1.6 mHa for 5 consecutive 5-eval windows) using fewer cumulative shots than L-BFGS-B (3-seed median), on at least 1 of 2 H2 geometries.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At 1024 shots per energy evaluation, gradient-free methods (SPSA and COBYLA, 6 seed-runs combined across both geometries) achieve chemical accuracy with success rate at least 4/6, while gradient-based methods (L-BFGS-B and ADAM, 6 seed-runs combined) achieve it with success rate at most 3/6. This isolates the shot-noise sensitivity of finite-difference gradient estimation.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Optimizer ranking by shots_to_chemical_accuracy changes between H2 equilibrium and H2 stretched geometries: at least one optimizer pair (e.g. SPSA vs COBYLA, or SPSA vs L-BFGS-B) swaps relative ranking between the two geometries, indicating that Hamiltonian difficulty changes optimizer choice.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Under finite shot noise, which classical optimizer reaches VQE chemical accuracy in the fewest cumulative shots, and how does the answer depend on shot budget per evaluation and on Hamiltonian difficulty?\", \"conditions\": [{\"name\": \"spsa\", \"description\": \"qiskit_algorithms.optimizers.SPSA(maxiter=200, learning_rate=0.05, perturbation=0.1). Fixed shot budget: 1024 shots per energy evaluation. Each iteration costs 2 energy evaluations.\"}, {\"name\": \"cobyla\", \"description\": \"qiskit_algorithms.optimizers.COBYLA(maxiter=200, rhobeg=0.1, tol=1e-4). 1024 shots per energy evaluation. Each iteration costs 1 energy evaluation.\"}, {\"name\": \"lbfgsb\", \"description\": \"qiskit_algorithms.optimizers.L_BFGS_B(maxiter=100, ftol=1e-6) with finite-difference gradients (epsilon=1e-3). 1024 shots per energy evaluation. Each iteration costs ~2*num_params energy evaluations for the gradient plus 1 for the function value (so ~25 evals per iter for EfficientSU2 reps=2 on 4 qubits).\"}, {\"name\": \"adam\", \"description\": \"qiskit_algorithms.optimizers.ADAM(maxiter=200, lr=0.05, beta_1=0.9, beta_2=0.999) with finite-difference gradients (epsilon=1e-3). 1024 shots per energy evaluation. Same per-iter cost as L-BFGS-B.\"}], \"baselines\": [\"Cumulative shots vs. FCI energy reference is the within-method benchmark\", \"Hartree-Fock energy at each geometry is the trivial classical baseline below which any optimizer should clear\"], \"metrics\": [{\"name\": \"shots_to_chemical_accuracy\", \"direction\": \"minimize\", \"description\": \"Cumulative shots until |E_running_mean - E_FCI| stays below 1.6 mHa for 5 consecutive evaluations. If not reached within the maxiter budget, report the total cumulative shots used (sentinel = max-shots) and flag success=False.\"}, {\"name\": \"final_energy_error_hartree\", \"direction\": \"minimize\", \"description\": \"|E_final - E_FCI| in Hartree at the end of optimization (after all maxiter iterations).\"}, {\"name\": \"success_rate\", \"direction\": \"maximize\", \"description\": \"Fraction of seeds (out of 3 per condition x geometry) where the run reached chemical accuracy by the maxiter budget.\"}], \"datasets\": [{\"name\": \"h2_equilibrium\", \"source\": \"qiskit_nature.second_q.drivers.PySCFDriver (REQUIRED dependency)\", \"description\": \"H2 in STO-3G basis at bond length 0.74 angstrom. Build the FermionicOp via PySCFDriver, apply ParityMapper with 2-qubit reduction to obtain a 4-qubit qubit Hamiltonian (alternatively JordanWignerMapper + tapering). FCI reference: E_FCI = -1.137283 Ha. Hardcoded Pauli strings are NOT an acceptable substitute for this benchmark; both qiskit_nature and pyscf must be installed and used.\"}, {\"name\": \"h2_stretched\", \"source\": \"qiskit_nature.second_q.drivers.PySCFDriver (REQUIRED dependency)\", \"description\": \"H2 in STO-3G basis at bond length 1.5 angstrom (multireference / strong-correlation regime). 4 qubits via the same mapping pipeline as h2_equilibrium. FCI reference: E_FCI ~ -1.001 Ha. Hardcoded Pauli strings NOT accepted.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1200}}", "requirements": "", "rubric": "{\"id\": \"q03-root\", \"requirements\": \"A credible VQE optimizer comparison on H2 in STO-3G basis at two bond lengths (0.74 A equilibrium, 1.5 A stretched), at a fixed shot budget of 1024 shots per energy evaluation. The agent must (a) construct the 4-qubit H2 Hamiltonian at each geometry via qiskit_nature.PySCFDriver, (b) implement VQE via qiskit_algorithms.VQE.compute_minimum_eigenvalue() with a hardware-efficient EfficientSU2 reps=2 ansatz, (c) run 4 optimizers (SPSA, COBYLA, L-BFGS-B, ADAM) x 2 geometries x 3 seeds = 24 cells, (d) log cumulative shots and energy trajectories per cell, and (e) score H1/H2/H3 with numerical evidence keyed by (optimizer, geometry).\", \"judging_note\": \"Optimizer comparisons are scored on (i) correctness of the Hamiltonian (FCI reference within 1 mHa of -1.137 Ha for H2 equilibrium), (ii) consistent ansatz / initial parameters across cells so only the optimizer and shot budget vary, (iii) accurate accounting of cumulative shots including gradient evaluations for L-BFGS-B and ADAM, and (iv) numerical evidence backing H1/H2/H3. qiskit_nature.second_q.drivers.PySCFDriver MUST be used to build the Hamiltonian. Hardcoded Pauli strings are NOT acceptable and a submission that uses them fails q03-code-hamiltonians.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q03-code\", \"requirements\": \"Code-development bucket: VQE pipeline correctly implements 4 optimizers, both H2 geometries, and accurate cumulative-shot tracking.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q03-code-vqe\", \"requirements\": \"VQE is implemented using qiskit_algorithms.VQE or an equivalent custom loop. The ansatz is EfficientSU2(num_qubits=4, reps=2, entanglement='linear'). Initial parameters are sampled from N(0, 0.1) with the same seed across all 4 optimizers for a given (shot_budget, geometry, seed) triple. All 4 optimizers are correctly instantiated from qiskit_algorithms.optimizers (SPSA, COBYLA, L_BFGS_B, ADAM) with the maxiter and learning rates listed in the manifest.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q03-code-hamiltonians\", \"requirements\": \"Both H2 Hamiltonians are constructed at the correct bond lengths (0.74 and 1.5 angstrom) in STO-3G basis using qiskit_nature.second_q.drivers.PySCFDriver (REQUIRED) and either ParityMapper with 2-qubit reduction or JordanWignerMapper with tapering. The diagonalized Hamiltonian (numpy eigvalsh of the matrix form) gives FCI energies within 1 mHa of -1.137 Ha (equilibrium) and -1.001 Ha (stretched). Hardcoded Pauli strings as a substitute for PySCFDriver are NOT accepted and fail this leaf.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q03-code-shot-tracking\", \"requirements\": \"Cumulative shots is tracked correctly per run. For SPSA and COBYLA, each iteration adds shots_per_eval * num_evaluations_per_iter (2 for SPSA, 1 for COBYLA). For L-BFGS-B and ADAM with finite-difference gradients, each iteration adds shots_per_eval * (2 * num_params + 1) so the gradient evaluations are accounted for. The shots_to_chemical_accuracy metric is computed using the running mean energy (over the last 5 evaluations) and reports the cumulative shot count at the first evaluation where the mean stays within 1.6 mHa of E_FCI for 5 consecutive evaluations.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q03-exec\", \"requirements\": \"Execution-validity bucket: all cells ran with valid energies and shot accounting.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q03-exec-cells-ran\", \"requirements\": \"At least 22 cells out of 24 expected (4 optimizers x 1 shot budget (1024) x 2 geometries x 3 seeds = 24) completed and produced a final_energy_error_hartree value. If any cells are omitted (e.g. ADAM ran out of budget), they must be documented with a recorded cause. Missing more than 2 cells (10 percent) without justification fails this leaf. The condition_summaries MUST be keyed by (optimizer, geometry) so each of the 8 condition cells has 3 seed entries — collapsing across the geometry axis (e.g. reporting only per-optimizer aggregates) fails this leaf because it makes H1 and H3 unevaluable.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q03-exec-numerical\", \"requirements\": \"Numerical validity: every final energy is finite (no NaN/Inf), every final_energy_error_hartree >= 0, every success_rate in [0, 1], cumulative shots is monotone increasing within a run. At least one cell across the entire sweep reaches chemical accuracy (otherwise the rubric thresholds are uncalibrated for this setup).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q03-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q03-result-h1-quant\", \"requirements\": \"Quantitative test of H1. On at least 1 of 2 geometries at 1024 shots/eval, does SPSA reach chemical accuracy with fewer median cumulative shots than L-BFGS-B (over 3 seeds)? 100% if SPSA wins on at least 1/2 geometries with a >=20 percent shot reduction, 67% if SPSA wins on at least 1/2 with any margin, 33% if SPSA and L-BFGS-B are within 10 percent of each other, 0% if L-BFGS-B is consistently faster.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q03-result-h2-quant\", \"requirements\": \"Quantitative test of H2. At 1024 shots/eval, pooling across both geometries: do gradient-free methods (SPSA + COBYLA, total of 6 seed-runs = 2 methods x 3 seeds) have a combined chemical-accuracy success rate of at least 4/6, while gradient-based methods (L-BFGS-B + ADAM, 6 seed-runs) have at most 3/6? 100% if cleanly met, 67% if the gradient-free success rate exceeds gradient-based by at least 30 percent, 33% if gradient-free exceeds gradient-based at all, 0% if gradient-based methods are equal or better.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q03-result-h3-quant\", \"requirements\": \"Quantitative test of H3. Does the optimizer ranking by median shots_to_chemical_accuracy change between H2 equilibrium and H2 stretched geometries? Specifically: take the 4-element ranking of optimizers (by 3-seed-median shots) on each geometry; H3 is supported if at least one pair swaps relative order between the two rankings (Kendall tau distance >= 1 between the two rankings). 100% if at least one swap detected with clear evidence, 67% if one optimizer's rank differs by exactly 1 position, 33% if rankings are similar but values differ, 0% if rankings are identical or H3 is unevaluable (e.g. due to collapsed condition_summaries that drop the geometry axis).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q03-result-writeup\", \"requirements\": \"Writeup of at least 200 words (in submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1, H2, H3 with numerical evidence (specific median cumulative shots, success rates, and final energy errors per optimizer x shot budget). Identifies a dominant systematic uncertainty (seed variance, finite-difference epsilon, optimizer hyperparameter sensitivity, shot-noise variance at low budgets). Discusses the difference between equilibrium and stretched H2 in terms of optimizer ranking.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q03.yaml", "rubric_file": "tasks/quantum/rubrics/Q03.json"} +{"id": "Q04", "domain": "quantum", "title": "Data re-uploading depth vs Fourier expressivity for variational quantum regression", "topic": "Data re-uploading depth vs Fourier expressivity for variational quantum regression: characterizing how the trainable bandwidth of a parameterized re-uploading circuit scales with re-uploading layers L on synthetic 1D regression targets", "domains": ["quantum-machine-learning", "data-reuploading", "expressivity"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_mse", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 900, "synthesis": "Data re-uploading is a variational quantum circuit pattern in which the\nclassical input x is loaded into the same qubits multiple times,\ninterleaved with trainable rotation layers: the circuit\nW(theta_L) U(x) W(theta_{L-1}) U(x) ... W(theta_1) U(x) |0> applies L\nencoding blocks U(x) and L trainable blocks W(theta). Perez-Salinas et al.\nproved in 2020 that a single qubit with sufficient re-uploading depth is\na universal function approximator on bounded intervals. Schuld, Sweke,\nand Meyer (PRA 2021) then proved the structural reason: a re-uploading\ncircuit's output function is a truncated Fourier series in the input x,\nwith the set of representable frequencies determined by the eigenvalue\nspectrum of the encoding generators and growing with L.\n\nThe practical question that follows is whether the theoretical\nexpressivity claim translates into empirical fit quality. As L grows we\nexpect (i) more representable frequencies (better fit on high-bandwidth\ntargets), and (ii) more parameters and more risk of overfitting on\nfinite-sample regression with discontinuous targets. Classical kernel\nregression (RBF) and polynomial regression provide the natural baselines\nbecause they too can be interpreted as choosing a basis of functions\n(Gaussian or polynomial) of fixed expressivity; comparing quantum\nre-uploading against these classical baselines is the cleanest way to\nask \"does the Fourier structure imposed by re-uploading give a useful\ninductive bias on these targets, or do classical models with comparable\ncapacity match it?\"\n\nImplementation guidance is provided by the quantum-qiskit skill, which\nis automatically injected at stage 10 and contains canonical code for\nbuilding re-uploading circuits using qiskit.circuit.ParameterVector and\nrotation gates. Training uses Adam through parameter-shift gradients\n(do NOT roll your own gradient finite-difference loop). The simulator\nis fixed to AerSimulator(method='statevector') for noiseless evaluation\nso the Fourier-spectrum probe is clean.\n\nExperimental protocol. Four quantum re-uploading depths (L=1, L=3, L=5,\nL=7) are compared against two classical baselines (RBF kernel ridge\nregression with bandwidth chosen by cross-validation; polynomial\nregression of degree 7). All six conditions are evaluated on two 1D\nregression targets: a sum-of-sinusoids signal with known frequency\ncontent, and a step function (tests handling of discontinuities).\nEach (condition, dataset) cell is averaged over 2 random seeds, giving\n6 conditions x 2 datasets x 2 seeds = 24 total cells.\n\nResearch question: *On 1D regression with known target bandwidth, does\nincreasing data re-uploading depth L produce monotonically lower test\nMSE on bandwidth-rich targets (sinusoid mixture), and does the\nrecovered Fourier spectrum overlap with the target spectrum scale\npredictably with L?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On the sinusoid-mixture target, the L=5 re-uploading model achieves test MSE at least 4 times lower than the L=1 re-uploading model (2-seed mean), demonstrating that increasing re-uploading depth improves regression accuracy on bandwidth-rich targets.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On the step-function target, the L=7 re-uploading model exhibits overfitting compared to L=5: L=7 test MSE is at least 15 percent higher than L=5 test MSE (2-seed mean), even though L=7 has more parameters and lower training MSE.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"At L=7 on the sinusoid-mixture target, the recovered Fourier spectrum (FFT of the trained circuit's output sampled on a uniform 256-point grid) has cosine similarity at least 0.7 with the target Fourier spectrum, restricted to the lowest 5 nonzero frequencies.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"On 1D regression with known target bandwidth, does increasing data re-uploading depth L produce monotonically lower test MSE on bandwidth-rich targets, and does the recovered Fourier spectrum overlap with the target spectrum scale predictably with L?\", \"conditions\": [{\"name\": \"reuploading_L1\", \"description\": \"Single-qubit re-uploading circuit with L=1 encoding block. Encoding: U(x) = RZ(x). Trainable block: W(theta) = RY(theta_0) RZ(theta_1). Output: . 2 trainable parameters total.\"}, {\"name\": \"reuploading_L3\", \"description\": \"Same single-qubit re-uploading template with L=3 encoding blocks. 6 trainable parameters.\"}, {\"name\": \"reuploading_L5\", \"description\": \"Same template with L=5 encoding blocks. 10 trainable parameters.\"}, {\"name\": \"reuploading_L7\", \"description\": \"Same template with L=7 encoding blocks. 14 trainable parameters.\"}], \"baselines\": [\"rbf_kernel_ridge: sklearn.kernel_ridge.KernelRidge(kernel='rbf', alpha=1e-3, gamma=tuned_via_grid_search). Strong nonparametric regression baseline.\", \"polynomial_regression: sklearn.pipeline.make_pipeline(PolynomialFeatures(degree=7), Ridge(alpha=1e-3)). Matched-capacity polynomial basis baseline.\"], \"metrics\": [{\"name\": \"test_mse\", \"direction\": \"minimize\", \"description\": \"Mean squared error on a 200-point held-out test set, averaged over 2 random seeds per (condition, dataset) cell. Primary metric.\"}, {\"name\": \"train_mse\", \"direction\": \"minimize\", \"description\": \"Mean squared error on the 200-point training set after training completes.\"}, {\"name\": \"fourier_spectrum_cosine\", \"direction\": \"maximize\", \"description\": \"Cosine similarity between the FFT of the trained model's output sampled on a uniform 256-point grid over [0, 1] and the FFT of the target function on the same grid, restricted to the lowest 5 nonzero frequency bins. Higher means the model has learned the target's Fourier content.\"}], \"datasets\": [{\"name\": \"sinusoid_mixture_1d\", \"source\": \"Synthetic generator\", \"description\": \"Target function f(x) = sum_k a_k * sin(2*pi*k*x) for k in {1, 2, 3, 5, 7} with random amplitudes a_k drawn from Uniform[0.5, 1.0] using a fixed dataset-generation seed (independent of per-cell training seeds). 200 training x-values + 200 test x-values uniformly sampled on [0, 1]. Targets are observed with Gaussian noise N(0, 0.05).\"}, {\"name\": \"step_function_1d\", \"source\": \"Synthetic generator\", \"description\": \"Target function f(x) = 1.0 if x > 0.5 else 0.0, plus Gaussian noise N(0, 0.05). Tests how the re-uploading model handles a discontinuity (its Fourier series will exhibit Gibbs ringing). 200 training + 200 test samples.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 900}}", "requirements": "", "rubric": "{\"id\": \"q04-root\", \"requirements\": \"A credible empirical study of data re-uploading depth vs Fourier expressivity in variational quantum regression. The agent must (a) implement 4 re-uploading circuits at L in {1, 3, 5, 7} using qiskit ParameterVector + RY/RZ rotation gates, (b) train each with parameter-shift Adam on 200 samples of two 1D regression targets (sinusoid mixture and step function), (c) compare against two classical baselines (RBF kernel ridge regression, polynomial regression degree 7) on the same data, (d) measure test MSE per cell and the recovered Fourier spectrum of the trained quantum model, and (e) score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"Re-uploading topics are scored on (i) whether the 4 quantum circuits actually differ by depth (sanity check: parameter counts must be 2/6/10/14), (ii) whether training was real and produced different outputs across L levels (3 quantum cells producing bit-identical MSE would indicate dispatch failure, same anti-pattern as Q01 ablation failure), (iii) whether classical baselines are computed on the exact same data as quantum cells, and (iv) whether H1/H2/H3 are supported by reported MSE and Fourier-spectrum numbers. Quantitative result leaves use a graded scale: 100% if hypothesis threshold cleanly met, 67% if trend in predicted direction without clearing threshold, 33% if ambiguous, 0% if contradicted or evidence missing.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q04-code\", \"requirements\": \"Code-development bucket: 4 re-uploading circuits + 2 classical baselines are implemented correctly with shared training pipeline and reproducible per-cell seed control.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q04-code-circuits\", \"requirements\": \"All four re-uploading circuits use qiskit.QuantumCircuit + qiskit.circuit.ParameterVector. The encoding gate is RZ(x) and the trainable block is RY(theta_2k) RZ(theta_2k+1). Output is the expectation on the single qubit (via qiskit.quantum_info.Statevector or the reference Estimator primitive). Parameter counts match: L=1 has 2 params, L=3 has 6, L=5 has 10, L=7 has 14. A sanity check asserts that the four circuits produce different output values for a fixed nonzero input x=0.3 with a fixed random parameter vector.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q04-code-training-pipeline\", \"requirements\": \"Training uses Adam with parameter-shift gradients computed via the qiskit parameter-shift rule (NOT scipy finite differences). Loss is mean squared error on the 200 training samples. Same optimizer hyperparameters across all four L levels (Adam lr=0.05, 300 steps). Both classical baselines use sklearn (KernelRidge with grid-searched gamma, and a Pipeline of PolynomialFeatures+Ridge).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"q04-code-metric-logging\", \"requirements\": \"Each per-(condition, dataset, seed) result is emitted to stdout as a single line starting with METRIC_RESULT followed by JSON containing condition, dataset, seed, test_mse, train_mse, and fourier_spectrum_cosine. The FFT is computed on the trained model's output sampled on a uniform 256-point grid over [0, 1].\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q04-exec\", \"requirements\": \"Execution-validity bucket: all 24 cells ran and produced numerically valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q04-exec-cells-ran\", \"requirements\": \"At least 22 cells out of 24 expected (6 conditions x 2 datasets x 2 seeds) completed without unhandled errors and produced a test_mse value. Missing more than 2 cells (10 percent) without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q04-exec-numerical\", \"requirements\": \"Numerical validity: no NaN or Inf in test_mse; train_mse <= test_mse * 1.5 across conditions (otherwise model is broken); the 4 quantum L levels produce different test MSEs (max - min across the 4 quantum conditions on the sinusoid-mixture dataset is at least 0.005). Fourier spectrum values are real, finite, and the cosine similarity is in [-1, 1].\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q04-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q04-result-h1-quant\", \"requirements\": \"Quantitative test of H1. On the sinusoid-mixture dataset, is test_mse(L=5) <= test_mse(L=1) / 4 (2-seed mean)? 100% if ratio >= 4, 67% if 2 <= ratio < 4, 33% if 1 < ratio < 2, 0% if L=5 is not better than L=1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q04-result-h2-quant\", \"requirements\": \"Quantitative test of H2. On the step function dataset, is test_mse(L=7) >= test_mse(L=5) * 1.15 (2-seed mean)? 100% if overshoot is >= 15 percent, 67% if 5-15 percent, 33% if any small overshoot (0-5 percent), 0% if L=7 still has lower MSE.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q04-result-h3-quant\", \"requirements\": \"Quantitative test of H3. On the sinusoid-mixture dataset at L=7, does the recovered Fourier spectrum cosine similarity (restricted to lowest 5 nonzero frequencies) reach 0.7? 100% if >= 0.7, 67% if >= 0.5, 33% if >= 0.3, 0% otherwise.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"q04-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1, H2, H3 backed by specific test MSE values per (L, dataset) and Fourier spectrum cosine values. Discusses how the Fourier-series view (Schuld, Sweke, Meyer 2021) predicts the observed trend, and identifies a dominant systematic uncertainty (seed variance, optimizer convergence, parameter-shift gradient noise).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q04.yaml", "rubric_file": "tasks/quantum/rubrics/Q04.json"} +{"id": "Q05", "domain": "quantum", "title": "Barren plateau onset under cost-function locality", "topic": "Barren plateau onset under cost-function locality: empirically measuring gradient variance of hardware-efficient ansatze under global vs local cost functions across depth, testing the Cerezo 2021 prediction that local costs avoid exponential gradient suppression", "domains": ["quantum-machine-learning", "barren-plateaus", "trainability"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "log_gradient_variance", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 600, "synthesis": "A barren plateau is the phenomenon, first identified by McClean et al.\nin 2018, that the gradient of a variational quantum circuit's loss\nbecomes exponentially small in the number of qubits when the ansatz is\ndrawn from a sufficiently random circuit ensemble. Concretely, for a\nloss L(theta) = where the parameterized\nstate is generated by a random-ish unitary, the variance of the\ngradient dL/dtheta_k scales as O(1/2^n). For n = 20 qubits this\nvariance is below machine epsilon, and SGD or any gradient-based\noptimizer has effectively zero signal to follow. This is the central\ntrainability obstruction in variational quantum machine learning.\n\nCerezo et al. (\"Cost function dependent barren plateaus in shallow\nparametrized quantum neural networks\", Nature Communications 2021)\nshowed that the locality of the cost function matters. If the cost is\na sum of single-qubit Pauli expectations (a local cost), then the\ngradient variance can scale only polynomially in n even at moderate\nansatz depth. If the cost is a product or projection over all qubits\n(a global cost), the variance is exponentially suppressed at any\ndepth. The mechanism is that local-cost observables overlap with only\na constant-size subspace of the 2^n dimensional Hilbert space, while\nglobal-cost observables are spread thinly across the entire space and\nalmost always cancel.\n\nThe empirical question this topic addresses is whether the local-cost\nfree lunch holds at moderate depth on a CPU-feasible 6-qubit\nbenchmark. We fix n=6 qubits and the EfficientSU2 hardware-efficient\nansatz family (a standard choice in qiskit). We vary depth (number of\nansatz layers L) and cost-function locality, then directly probe the\ngradient distribution by sampling 100 random parameter vectors per\ncell, computing the parameter-shift gradient with respect to theta_0\nfor each, and reporting the empirical variance. A subsidiary check\ntrains each condition for 100 Adam iterations and reports the loss\nchange as a practical sanity probe of whether the gradient signal is\nlarge enough to drive optimization at all.\n\nImplementation guidance is provided by the quantum-qiskit skill,\nwhich is automatically injected at stage 10. Use qiskit.circuit.library\nEfficientSU2 verbatim; do NOT hand-write H/CNOT/RY gate stacks (a\nprior failed run produced an \"ablation failure\" where three nominally\ndifferent encodings collapsed to identical circuits because they\nwere hand-coded). Use qiskit_algorithms.utils.algorithm_globals\nrandom_seed for reproducibility. Gradients are computed via the\nparameter-shift rule on a statevector backend.\n\nExperimental protocol. Four conditions cover the cost x depth grid:\n(local_cost_L2) shallow + local, the trainable control; (local_cost_L10)\nmedium + local, the regime Cerezo's theorem predicts is still\ntrainable; (local_cost_L20) deep + local, stressing the prediction;\n(global_cost_L10) medium + global, the negative control where a barren\nplateau is expected. Each condition is run with 3 different random\nansatz-structure seeds (controlling which qubits are entangled in each\nEfficientSU2 layer) for variance estimation across architectures, and\n100 random parameter vectors per (condition, structure-seed) cell are\nused to estimate gradient variance. Total cells: 4 conditions x 3\nansatz seeds = 12 cells. No external dataset is required because this\nis a measurement experiment on the ansatz family itself.\n\nResearch question: *Does the local-cost trick avoid the barren plateau\non a 6-qubit EfficientSU2 ansatz at L=10 and L=20 ansatz depth, as\npredicted by Cerezo et al. 2021, when compared against the\nglobal-cost baseline at the same depth and against the shallow local-cost\ncontrol?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At ansatz depth L=10, the empirical variance of the gradient under local cost is at least 20 times larger than under global cost (3-seed mean of variance per condition), confirming Cerezo et al. 2021's prediction that locality changes the barren-plateau scaling.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Under local cost, the gradient variance at L=20 is at least 1/100 of the gradient variance at L=2, i.e. the polynomial (sub-exponential) decay regime is maintained even at deep ansatz, with log10(var_L20) - log10(var_L2) > -2.0 (3-seed mean).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"When trained with Adam for 100 gradient steps starting from random initialization, the global-cost-L10 condition produces a loss change of at most 1 percent of the initial loss (3-seed mean), demonstrating that the predicted barren plateau actively prevents practical training, while the local-cost-L10 condition produces a loss change of at least 10 percent.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the local-cost trick avoid the barren plateau on a 6-qubit EfficientSU2 ansatz at L=10 and L=20, as predicted by Cerezo et al. 2021, when compared against the global-cost baseline and a shallow local-cost control?\", \"conditions\": [{\"name\": \"local_cost_L10\", \"description\": \"Proposed-method core. EfficientSU2(num_qubits=6, reps=10, entanglement='linear') ansatz. Cost function: (expectation of Pauli Z on qubit 0 only). 100 random parameter vectors theta drawn from Uniform[-pi, pi]^{n_params}. For each theta, compute parameter-shift gradient dL/dtheta_0 by evaluating L at theta + (pi/2) * e_0 and theta - (pi/2) * e_0. Report variance of these 100 gradient values.\"}, {\"name\": \"local_cost_L20\", \"description\": \"Proposed-method stress test. EfficientSU2 reps=20, same local cost . Tests whether the polynomial-decay prediction holds at deeper ansatz.\"}], \"baselines\": [\"local_cost_L2: trainable-control baseline. EfficientSU2 reps=2, local cost . Known to have non-vanishing gradient because the ansatz is too shallow to enter the barren-plateau regime.\", \"global_cost_L10: barren-plateau negative-control baseline. EfficientSU2 reps=10, global cost (product of Z on all 6 qubits). Known from Cerezo 2021 to exhibit exponentially small gradient variance even though the ansatz depth matches local_cost_L10.\"], \"metrics\": [{\"name\": \"log_gradient_variance\", \"direction\": \"maximize\", \"description\": \"log10 of the empirical variance of dL/dtheta_0 over 100 random parameter samples. Higher means more trainable gradient signal. Primary metric. Less negative numbers are better (closer to 0).\"}, {\"name\": \"mean_absolute_gradient\", \"direction\": \"maximize\", \"description\": \"Mean of |dL/dtheta_0| over the 100 random samples.\"}, {\"name\": \"training_loss_change_fraction\", \"direction\": \"maximize\", \"description\": \"Relative loss change after 100 Adam steps starting from a random initialization: (L_initial - L_after_100_steps) / |L_initial|. Practical training-feasibility probe. Larger positive means training actually moved the loss.\"}], \"datasets\": [{\"name\": \"random_parameter_samples\", \"source\": \"Synthetic, generated in-script via numpy.random.RandomState(ansatz_structure_seed).uniform(-pi, pi, size=(100, n_params))\", \"description\": \"Each cell samples 100 random parameter vectors in the parameter space of EfficientSU2 at the given (num_qubits, reps). These are the inputs over which gradient variance is estimated. No external data is involved; this is a measurement experiment on the ansatz family.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 600}}", "requirements": "", "rubric": "{\"id\": \"q05-root\", \"requirements\": \"An empirical study of barren plateau onset on a 6-qubit EfficientSU2 ansatz under local vs global cost functions and shallow vs deep ansatz. The agent must (a) build the EfficientSU2 ansatz with reps in {2, 10, 20} via qiskit.circuit.library, (b) for each (cost, depth) condition sample 100 random parameter vectors and compute parameter-shift gradient dL/dtheta_0, (c) report the empirical variance of those 100 gradient values per cell, (d) run a 100-step Adam training as a practical sanity probe of trainability, and (e) score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"Barren plateau studies are scored on (i) correctness of the EfficientSU2 / cost construction (using qiskit.circuit.library, not hand-rolled), (ii) whether the 100-sample gradient distribution actually captures the variance (cells reporting std=0 across 100 samples indicate a broken sampler), (iii) the local_cost_L10 vs global_cost_L10 contrast being clean (these have IDENTICAL ansatz, only cost differs, so any large variance gap directly tests Cerezo 2021), and (iv) the training-feasibility probe correlating with the gradient-variance prediction (BP cell should not train; trainable cell should train). Quantitative result leaves use a graded scale: 100% if cleanly met, 67% if in predicted direction without clearing threshold, 33% if ambiguous, 0% if contradicted.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q05-code\", \"requirements\": \"Code-development bucket: EfficientSU2 ansatz built correctly via qiskit.circuit.library, parameter-shift gradient implemented correctly, and the 100-sample gradient distribution actually populated per cell.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q05-code-ansatz-and-gradient\", \"requirements\": \"EfficientSU2(num_qubits=6, reps=reps_value, entanglement='linear') from qiskit.circuit.library used directly (NOT hand-coded H/CNOT/RY stacks). Parameter-shift gradient of (theta) with respect to theta_0 computed via the qiskit standard pattern: dL/dtheta_0 = (L(theta + (pi/2)*e_0) - L(theta - (pi/2)*e_0)) / 2. Cost observable for local_cost is Pauli Z on qubit 0; for global_cost is the product Z_0 Z_1 Z_2 Z_3 Z_4 Z_5 (a SparsePauliOp). A sanity check confirms that the gradient is nonzero for at least one random parameter sample at L=2 local cost.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q05-code-sampling\", \"requirements\": \"For each (condition, ansatz-structure seed) cell, 100 independent random parameter vectors are drawn from Uniform[-pi, pi]^{n_params} using a per-cell seeded RNG. For each parameter vector, the gradient dL/dtheta_0 is computed. The 100 gradient values are stored and the empirical variance is reported. Reusing the same parameter vector across conditions in the same cell is allowed (paired comparison) but NOT required.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"q05-code-training-probe\", \"requirements\": \"After the gradient-variance measurement, each cell runs a 100-step Adam optimization (qiskit_algorithms.optimizers.ADAM or pytorch Adam wrapping a parameter-shift gradient closure) starting from a random initialization. The initial loss and the loss after step 100 are recorded; the training_loss_change_fraction = (L_initial - L_final) / |L_initial| is computed per cell.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q05-exec\", \"requirements\": \"Execution-validity bucket: all 12 cells ran and produced numerically valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q05-exec-cells-ran\", \"requirements\": \"At least 11 cells out of 12 expected (4 conditions x 3 ansatz-structure seeds) completed and produced a non-null log_gradient_variance value. Missing more than 1 cell without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q05-exec-numerical\", \"requirements\": \"Numerical validity: no NaN or Inf in gradient values; empirical std of the 100 gradients is strictly > 0 in every cell (cells reporting std=0 indicate a broken sampler and fail this leaf); log10(variance) values are finite. The training_loss_change_fraction is in the range [-1.0, 1.0] (otherwise loss diverged or wasn't properly normalized).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q05-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q05-result-h1-quant\", \"requirements\": \"Quantitative test of H1. Is var(local_cost_L10) / var(global_cost_L10) >= 20 (3-seed mean of variances)? 100% if ratio >= 20, 67% if 5 <= ratio < 20, 33% if 1 < ratio < 5, 0% if global cost has equal or larger variance.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q05-result-h2-quant\", \"requirements\": \"Quantitative test of H2. Is log10(var_local_L20) - log10(var_local_L2) > -2.0 (3-seed mean)? 100% if difference > -2.0 (polynomial decay), 67% if difference > -3.0, 33% if difference > -4.0, 0% otherwise (exponential decay observed).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q05-result-h3-quant\", \"requirements\": \"Quantitative test of H3. Is global_cost_L10 training_loss_change_fraction <= 0.01 AND local_cost_L10 training_loss_change_fraction >= 0.10 (3-seed mean)? 100% if both conditions hold cleanly, 67% if local trains but global change is 1-5 percent, 33% if local trains but global has any change, 0% if global cost also trains (would contradict the BP prediction).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q05-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by specific variance values per (condition, seed) and training_loss_change_fraction values. Explicitly references Cerezo et al. 2021 'Cost function dependent barren plateaus' and discusses whether the observed variance ratio matches their theoretical prediction. Identifies dominant uncertainty (ansatz-structure seed variance, finite-sample variance estimate from 100 gradient samples).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q05.yaml", "rubric_file": "tasks/quantum/rubrics/Q05.json"} +{"id": "Q06", "domain": "quantum", "title": "Neural-network warm-start for QAOA-MaxCut initialization", "topic": "Neural-network warm-start for QAOA-MaxCut: comparing MLP-predicted initial parameters against random and fixed initialization across in-distribution and out-of-distribution graph families", "domains": ["ai-for-quantum", "qaoa", "meta-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "iterations_to_target_ratio", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 1200, "synthesis": "The Quantum Approximate Optimization Algorithm (QAOA) prepares a\nparameterized quantum state to approximate the maximum cut of a graph.\nAt depth p=1 the state depends on two real parameters (beta, gamma),\nand the algorithm proceeds by classical optimization of those two\nparameters against the average cut size sampled from measurements of\nthe prepared state. The bottleneck for practical QAOA is the initial\nparameter guess. Random initialization frequently falls into the\nbarren-plateau region or onto sub-optimal local minima, requiring many\nclassical iterations to escape. Brandao et al. 2018 showed\ntheoretically that the optimal (beta, gamma) values concentrate\naround a problem-independent point on certain graph ensembles, so a\nfixed initialization like (pi/4, pi/4) is a strong default.\n\nBeyond concentration, a richer strategy is to train a classical\nneural network offline to predict the optimal (beta, gamma) given\nfeatures of the input graph. Verdon et al. 2019 introduced the idea\nas quantum-aware meta-learning. Jain et al. (Quantum 2022, \"Graph\nneural network initialisation of QAOA\") showed that a graph neural\nnetwork trained on a few thousand small graphs can predict initial\nparameters that reduce the number of subsequent classical\noptimization iterations by 5-10x compared to random initialization,\nwith measurable transfer to out-of-distribution graph families.\n\nThe empirical question this topic addresses is whether the MLP-warm-start\nstrategy actually generalizes off-distribution: a model trained on\nErdős-Rényi graphs of one density should ideally retain its advantage\non regular graphs of a different size. We also want to verify whether\nthe simpler \"fixed init at (pi/4, pi/4)\" baseline (from Brandao 2018)\nis competitive with the MLP-predicted init at p=1, since the\nconcentration result suggests that simple defaults may be hard to\nbeat in the low-depth regime.\n\nImplementation guidance is provided by the quantum-qiskit skill at\nstage 10. Implement QAOA on the qiskit AerSimulator statevector\nbackend; the small graph sizes (n=6 to n=8) make exact statevector\nsimulation cheap. The MLP is sklearn.neural_network.MLPRegressor\ntrained offline on a precomputed set of (graph_features, optimal_beta,\noptimal_gamma) tuples obtained by grid search on 200 training\ngraphs. Optimization at test time uses COBYLA with a hard cap of 20\niterations.\n\nExperimental protocol. Two graph families serve as the test bed.\nIn-distribution: Erdős-Rényi G(n=6, p=0.5) graphs (matched to the MLP\ntraining distribution). Out-of-distribution: 3-regular graphs at n=8\n(different size and edge density). Four conditions cover the\ninitialization strategies: random init, fixed init (beta=gamma=pi/4),\nMLP-init evaluated in-distribution, and MLP-init evaluated\nout-of-distribution. (MLP-init has 2 conditions because the model\ntrained on Erdős-Rényi G(n=6, p=0.5) is evaluated on both graph\nfamilies to test transfer.) Each (condition, graph family) cell is\naveraged over 3 different random graph instances drawn within that\nfamily. Total cells: 4 conditions x 2 graph families x 3 seeds = 24\ncells.\n\nResearch question: *Does a small MLP trained to predict QAOA-MaxCut\ninitial parameters from graph features (degree distribution, edge\ndensity, spectral gap) provide a faster warm-start than random\ninitialization or the Brandao 2018 fixed (pi/4, pi/4) initialization,\nand does the advantage transfer to graphs from a different\ndistribution than the MLP was trained on?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On in-distribution Erdős-Rényi graphs at n=6, the MLP-predicted initialization reaches approximation ratio 0.9 in at most 10 COBYLA iterations on average (3-graph mean), while random initialization requires at least 18 iterations to reach the same target.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On out-of-distribution 3-regular graphs at n=8, MLP-predicted initialization still beats random initialization by at least 20 percent in iteration count (3-graph median), demonstrating partial transfer of the learned initialization heuristic.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"At p=1 the fixed initialization (beta=gamma=pi/4) is competitive with MLP-predicted initialization: the gap in 3-graph-median iteration count between fixed init and MLP-init is at most 30 percent on in-distribution graphs, consistent with the Brandao 2018 concentration prediction that QAOA-1 parameters concentrate around a problem-independent point.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does an MLP trained on small Erdős-Rényi graph features predict QAOA-MaxCut initial parameters that reduce COBYLA iteration count compared to random and fixed init, and does the advantage transfer to graphs of a different distribution than the MLP was trained on?\", \"conditions\": [{\"name\": \"mlp_init_in_distribution\", \"description\": \"Proposed method evaluated in-distribution. sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(32, 16), activation='relu', max_iter=2000, random_state=seed) trained offline on 200 Erdős-Rényi G(n=6, p=0.5) graphs with (degree_mean, degree_var, edge_density, n_edges) as features and grid-searched (beta_optimal, gamma_optimal) as targets. At test time, predict initial (beta, gamma) for a fresh in-distribution graph, then run COBYLA QAOA optimization starting from that initialization.\"}, {\"name\": \"mlp_init_out_of_distribution\", \"description\": \"Proposed method evaluated out-of-distribution. Same MLP as mlp_init_in_distribution (trained on Erdős-Rényi G(n=6, p=0.5)). At test time, evaluated on 3-regular graphs at n=8: predict initial (beta, gamma) using the same feature extractor, then run COBYLA QAOA optimization.\"}], \"baselines\": [\"random_init: classical baseline. Initial (beta, gamma) drawn from Uniform[0, pi/2] x Uniform[0, pi/2] using a per-graph seed independent of optimization seed. Then COBYLA QAOA from that point.\", \"fixed_init_pi_over_4: classical baseline from Brandao 2018. Initial (beta, gamma) = (pi/4, pi/4). Then COBYLA QAOA. Tests whether the concentration prediction holds in practice at p=1.\"], \"metrics\": [{\"name\": \"iterations_to_target_ratio\", \"direction\": \"minimize\", \"description\": \"Number of COBYLA iterations required to reach approximation ratio (cut_size / optimal_cut_size) >= 0.9 on each graph, with a hard cap at 20 iterations (cells that don't reach 0.9 within 20 iterations report 20 and success=False). Primary metric. 3-graph median per condition x graph_family cell.\"}, {\"name\": \"final_approximation_ratio\", \"direction\": \"maximize\", \"description\": \"approximation ratio (cut_size / optimal_cut_size) achieved after exactly 20 COBYLA iterations, regardless of whether the target threshold was met. 3-graph mean.\"}, {\"name\": \"success_rate\", \"direction\": \"maximize\", \"description\": \"Fraction of the 3 random graph instances in a cell that reached approximation ratio 0.9 within the 20-iteration budget.\"}], \"datasets\": [{\"name\": \"erdos_renyi_n6_p05\", \"source\": \"networkx.erdos_renyi_graph(n=6, p=0.5, seed=graph_instance_seed)\", \"description\": \"In-distribution test set. 3 fresh random Erdős-Rényi graphs G(n=6, p=0.5), distinct from the 200 graphs used for MLP training. Optimal MaxCut value computed by brute-force enumeration over the 2^6 cuts. QAOA p=1 on AerSimulator statevector backend.\"}, {\"name\": \"regular_3_n8\", \"source\": \"networkx.random_regular_graph(d=3, n=8, seed=graph_instance_seed)\", \"description\": \"Out-of-distribution test set. 3 random 3-regular graphs at n=8 (different size and edge density than the n=6 ER training distribution). Optimal MaxCut value by brute-force enumeration over 2^8 cuts. QAOA p=1 on AerSimulator statevector backend.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1200}}", "requirements": "", "rubric": "{\"id\": \"q06-root\", \"requirements\": \"An empirical study of neural-network warm-start for QAOA-MaxCut at p=1. The agent must (a) precompute 200 Erdős-Rényi G(n=6, p=0.5) training graphs with their optimal (beta, gamma) via grid search to label the MLP training set, (b) train sklearn.MLPRegressor to predict (beta, gamma) from graph features, (c) at test time, predict (beta, gamma) and run COBYLA QAOA optimization starting from that init for up to 20 iterations, (d) compare against random and fixed (pi/4, pi/4) initialization baselines on both in-distribution (ER G(n=6, p=0.5)) and out-of-distribution (3-regular n=8) test graphs, and (e) score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"QAOA warm-start studies are scored on (i) whether the MLP is actually trained offline (cells reporting identical MLP predictions across different graph instances indicate the model is broken or untrained), (ii) whether the 4 initialization strategies actually produce different starting (beta, gamma) points (must be verified by sanity check), (iii) whether COBYLA is run from each init for exactly 20 iterations and the approximation_ratio trajectory is recorded, and (iv) whether H1/H2/H3 are supported by 3-graph-median numerical evidence keyed by (condition, graph_family). Quantitative result leaves use a graded scale.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q06-code\", \"requirements\": \"Code-development bucket: QAOA p=1 circuit built via qiskit.circuit.library or hand-coded RX/RZ + CNOT correctly, MLP trained offline on graph features, and 4 initialization conditions actually produce different starting points.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q06-code-qaoa-circuit\", \"requirements\": \"QAOA p=1 circuit for MaxCut implemented correctly: H^{otimes n} initial state, then U_C(gamma) = product over edges of exp(-i * gamma * Z_i Z_j), then U_B(beta) = product over qubits of exp(-i * beta * X_i). Run on qiskit AerSimulator statevector backend; observable is the cut Hamiltonian sum_{(i,j) in E} (1 - Z_i Z_j) / 2. Optimization via qiskit_algorithms.optimizers.COBYLA(maxiter=20).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q06-code-mlp-training\", \"requirements\": \"Offline phase: generate 200 random ER G(n=6, p=0.5) graphs with a different RNG than the test set. For each, compute the optimal (beta_optimal, gamma_optimal) via a 20x20 grid search on (beta, gamma) in [0, pi/2]^2 maximizing the QAOA p=1 expected cut size. Extract 4 graph features (degree_mean, degree_var, edge_density, n_edges) and fit sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(32, 16), max_iter=2000) to map features -> (beta_optimal, gamma_optimal). The trained MLP is reused for both in-distribution and out-of-distribution test conditions.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q06-code-init-conditions\", \"requirements\": \"The 4 initialization strategies produce different starting (beta, gamma) per test graph. Sanity check: for 3 test graphs, log the initial (beta, gamma) for each of the 4 conditions and verify they are not all equal. Each condition's COBYLA optimization is run for exactly 20 iterations, and the cut size at each iteration is recorded so the iterations_to_target_ratio can be computed by replaying the trajectory.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q06-exec\", \"requirements\": \"Execution-validity bucket: all cells ran and produced numerically valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q06-exec-cells-ran\", \"requirements\": \"At least 22 cells out of 24 expected (4 conditions x 2 graph families x 3 seeds) completed and produced an iterations_to_target_ratio value (clamped to 20 if not reached). Missing more than 2 cells (10 percent) without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q06-exec-numerical\", \"requirements\": \"Numerical validity: iterations_to_target_ratio is in [1, 20] (integer-valued, since COBYLA budget is 20); final_approximation_ratio is in [0, 1]; success_rate is in [0, 1]. At least one cell across the entire 24-cell sweep reaches AR >= 0.9 within 20 iterations (otherwise threshold is uncalibrated).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q06-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q06-result-h1-quant\", \"requirements\": \"Quantitative test of H1. On in-distribution ER G(n=6, p=0.5) graphs, does mlp_init_in_distribution reach AR>=0.9 in <= 10 COBYLA iterations on 3-graph mean, while random_init requires >= 18? 100% if MLP <= 10 AND random >= 18, 67% if MLP at least 30 percent better than random, 33% if MLP marginally better than random, 0% if random equals or beats MLP.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q06-result-h2-quant\", \"requirements\": \"Quantitative test of H2. On out-of-distribution 3-regular n=8 graphs, does mlp_init_out_of_distribution beat random_init by at least 20 percent in 3-graph-median iterations_to_target_ratio? 100% if mlp gain >= 20 percent, 67% if mlp gain 10-20 percent, 33% if mlp marginally faster, 0% if no transfer (random matches or beats MLP).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q06-result-h3-quant\", \"requirements\": \"Quantitative test of H3. On in-distribution graphs, is the 3-graph-median iteration-count gap between fixed_init_pi_over_4 and mlp_init_in_distribution <= 30 percent (i.e. fixed init is competitive with MLP at p=1, consistent with Brandao 2018 concentration)? 100% if gap <= 30 percent, 67% if 30-60 percent, 33% if 60-100 percent, 0% if MLP is more than 2x better than fixed init (would refute the concentration interpretation).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q06-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by per-(condition, graph_family) median iteration counts and approximation ratios. References Brandao et al. 2018 concentration result and Jain et al. Quantum 2022 GNN-init paper. Discusses whether the observed out-of-distribution transfer rate is high enough that MLP-init is worth the offline training cost compared to fixed init.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q06.yaml", "rubric_file": "tasks/quantum/rubrics/Q06.json"} +{"id": "Q07", "domain": "quantum", "title": "Matrix Product State classifier vs neural network at matched parameter count", "topic": "Matrix Product State classifier vs neural network at matched parameter count on downsampled image classification, characterizing the bond-dimension scaling of tensor-network classifiers as a quantum-inspired classical baseline for QML", "domains": ["quantum-inspired-classical", "tensor-networks", "supervised-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1500, "synthesis": "A Matrix Product State (MPS) is a tensor network in which a target\ntensor of N indices is decomposed into a 1D chain of N small tensors,\neach connected to its neighbors by \"bond\" indices of dimension chi.\nMPS originated in condensed-matter physics as the representation\nunderlying the Density Matrix Renormalization Group (DMRG) for 1D\nground-state computation, and Schollwock's 2011 review remains the\ncanonical reference. Stoudenmire and Schwab (NeurIPS 2016, \"Supervised\nLearning with Tensor Networks\") repurposed the MPS as a classical\nsupervised classifier. The idea is simple: each input feature\n(pixel) is mapped to a small feature vector (e.g. cos(pi*x/2),\nsin(pi*x/2) for grayscale x in [0,1]), and the per-pixel feature\nvectors are contracted with a chain of MPS tensors to produce a\nclass-score vector. The MPS bond dimension chi controls how much\ncorrelation between distant pixels the classifier can capture,\ngiving an interpretable expressivity knob.\n\nCrucially, the MPS classifier runs entirely on classical hardware.\nThis makes it a \"quantum-inspired classical\" baseline: the same\nmathematical machinery as a low-entanglement quantum state, but\nimplemented as a NumPy tensor contraction. Glasser et al. (PRX 2018)\nextended the framework to richer tensor networks. More recent\nbenchmarks (Liu et al. 2022 Nature Communications Physics) have asked\nwhere the bond-dimension sweet spot is for image classification and\nwhether MPS classifiers can be competitive with CNNs at small\nparameter budgets.\n\nThis topic asks whether an MPS classifier matches a parameter-matched\nCNN baseline on a CPU-feasible image classification task. The\ncomparison is not \"does MPS beat CNN at large scale\" (it doesn't, on\nhigh-resolution images) but rather \"at matched small parameter\ncounts, is the MPS inductive bias competitive with a small CNN's\nspatial bias on 8x8 downsampled MNIST and Fashion-MNIST?\" The\nquestion is timely because tensor-network ML is increasingly cited\nas the honest classical baseline against which to measure quantum\nmachine learning claims of advantage.\n\nImplementation guidance. Use pure NumPy or PyTorch for the MPS\ncontraction (no qiskit required for this topic; it is quantum-inspired\nclassical computation). The standard MPS classifier from Stoudenmire\n& Schwab 2016 stores N+1 tensors of shape (chi, d, chi) where N is\nthe number of pixels (64 for an 8x8 image), d=2 is the per-pixel\nfeature dimension (we use the cos/sin embedding), and chi is the\nbond dimension. The CNN baseline is a single 3x3 convolution layer\nwith K filters followed by a fully connected output layer; K is\nchosen so the total parameter count is within 10 percent of the MPS\nparameter count at each bond dimension.\n\nExperimental protocol. Three MPS bond dimensions (chi=4, chi=8,\nchi=16) are compared against two classical baselines (logistic\nregression on flattened 8x8 pixels; small CNN with parameter count\nmatched to the chi=8 MPS). All five conditions are evaluated on two\ndatasets: 8x8 downsampled MNIST (10 classes, 5000 train / 1000 test\nimages) and 8x8 downsampled Fashion-MNIST (10 classes, same\nsize). Each (condition, dataset) cell is averaged over 3 random\nseeds (controlling MPS / CNN initialization and the train / test\nsplit). Total cells: 5 conditions x 2 datasets x 3 seeds = 30 cells.\n\nResearch question: *On 8x8 downsampled MNIST and Fashion-MNIST, do\nMPS classifiers at moderate bond dimension chi reach the test\naccuracy of a parameter-matched CNN, and how does the accuracy\nscale with chi?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On 8x8 downsampled MNIST, the MPS classifier at bond dimension chi=16 reaches at least 92 percent test accuracy (3-seed mean), matching the Stoudenmire & Schwab 2016 result scaled to this image size.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On 8x8 downsampled Fashion-MNIST, the parameter-matched CNN beats the chi=16 MPS classifier by at least 5 absolute percentage points (3-seed mean), reflecting the CNN's stronger inductive bias for spatial texture features that are more important on Fashion-MNIST than on plain digits.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Test accuracy of the MPS classifier scales log-linearly with bond dimension: on 8x8 MNIST the chi=4 to chi=8 gain in 3-seed mean accuracy is greater than the chi=8 to chi=16 gain (consistent with saturating expressivity at moderate chi).\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"On 8x8 downsampled MNIST and Fashion-MNIST, do MPS classifiers at moderate bond dimension chi reach the test accuracy of a parameter-matched CNN, and how does accuracy scale with chi?\", \"conditions\": [{\"name\": \"mps_chi4\", \"description\": \"Matrix Product State classifier with bond dimension chi=4. 64 pixels x 2-dim per-pixel feature embedding x chi=4 bonds, with a per-class output index. Approximate parameter count: 64 * 2 * 4 * 4 = 2048 parameters. Trained with Adam on cross-entropy for 50 epochs.\"}, {\"name\": \"mps_chi8\", \"description\": \"MPS classifier with chi=8. Approximate parameter count: 64 * 2 * 8 * 8 = 8192. Trained with Adam on cross-entropy for 50 epochs.\"}, {\"name\": \"mps_chi16\", \"description\": \"MPS classifier with chi=16. Approximate parameter count: 64 * 2 * 16 * 16 = 32768. Trained with Adam on cross-entropy for 50 epochs.\"}], \"baselines\": [\"logistic_regression: sklearn.linear_model.LogisticRegression(C=1.0, max_iter=1000, multi_class='multinomial') on flattened 8x8 pixels. Classical linear baseline, ~640 parameters.\", \"small_cnn_matched: PyTorch CNN with a single 3x3 conv layer of K=12 filters followed by an FC(K*6*6, 10) output, chosen so total parameter count is within 10 percent of the chi=8 MPS (target ~8200 parameters). Trained with Adam for 50 epochs.\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Top-1 classification accuracy on the 1000-image held-out test set, 3-seed mean. Primary metric.\"}, {\"name\": \"trainable_parameter_count\", \"direction\": \"minimize\", \"description\": \"Total number of trainable parameters in the model. Reported for parity validation across conditions: matched-parameter comparisons require this to be within 10 percent across the relevant condition pair.\"}, {\"name\": \"training_time_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock seconds for 50 epochs of training, measured per cell. Tests the practical compute cost of each method at matched accuracy.\"}], \"datasets\": [{\"name\": \"mnist_8x8_downsampled\", \"source\": \"sklearn.datasets.fetch_openml('mnist_784') downsampled to 8x8 via skimage.transform.resize\", \"description\": \"MNIST handwritten digits 0-9, downsampled from 28x28 to 8x8 grayscale, normalized to [0, 1]. 5000 training images, 1000 test images (stratified split, fixed random_state=seed). The 8x8 resolution is small enough for CPU MPS contraction but large enough to retain class structure (LogReg should reach ~85 percent here).\"}, {\"name\": \"fashion_mnist_8x8_downsampled\", \"source\": \"sklearn.datasets.fetch_openml('Fashion-MNIST') downsampled to 8x8\", \"description\": \"Fashion-MNIST clothing items (T-shirt, trouser, pullover, etc.) downsampled from 28x28 to 8x8. Tests model performance on texture-rich images where CNN inductive bias should be more important than for handwritten digits.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1500}}", "requirements": "", "rubric": "{\"id\": \"q07-root\", \"requirements\": \"An empirical study of Matrix Product State (MPS) classifiers vs neural network baselines at matched parameter count on 8x8 downsampled image classification. The agent must (a) build an MPS classifier following Stoudenmire and Schwab 2016 with cos/sin per-pixel feature embedding and bond dimensions chi in {4, 8, 16}, (b) train each MPS via Adam for 50 epochs on 5000 training images, (c) compare against sklearn LogisticRegression and a small PyTorch CNN whose parameter count is within 10 percent of the chi=8 MPS, (d) evaluate test accuracy on a 1000-image held-out set, and (e) score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"MPS classifier studies are scored on (i) correctness of the MPS contraction (the per-pixel features cos(pi*x/2), sin(pi*x/2) must enter as a rank-2 tensor at each site, NOT collapsed to a single scalar), (ii) the parameter-count parity between chi=8 MPS and small_cnn_matched within 10 percent (deviation > 10 percent fails q11-code-cnn-parity), (iii) the same train/test split being used across all conditions for a given seed, and (iv) accuracy values being in [0, 1] with non-degenerate predictions. Quantitative result leaves use a graded scale.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q07-code\", \"requirements\": \"Code-development bucket: MPS classifier implemented correctly with proper per-pixel feature embedding, CNN baseline parameter-matched, and training pipeline shared across conditions.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q07-code-mps\", \"requirements\": \"MPS classifier uses pure NumPy or PyTorch tensor contractions. Per-pixel feature embedding is phi(x) = [cos(pi*x/2), sin(pi*x/2)] for grayscale x in [0,1] (Stoudenmire and Schwab 2016 standard). The MPS is a chain of 64 tensors of shape (chi, 2, chi) (interior sites) plus 2 boundary tensors with one chi leg removed, with one site carrying an additional class index of size 10. Trained with Adam (lr=0.01, 50 epochs, batch_size=64). A sanity check confirms that MPS at chi=4 has approximately 64 * 2 * 16 = 2048 trainable parameters within 20 percent.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q07-code-cnn-parity\", \"requirements\": \"small_cnn_matched is implemented in PyTorch as Conv2d(1, K, kernel_size=3, padding=1) + ReLU + Flatten + Linear(K*8*8, 10) with K chosen so total parameter count is within 10 percent of the chi=8 MPS parameter count. K is calculated explicitly and the parameter counts are logged for both. Trained with Adam (same hyperparams as MPS) for 50 epochs.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"q07-code-pipeline\", \"requirements\": \"Nested loop over 5 conditions x 2 datasets x 3 seeds = 30 cells. Each cell uses the same train/test split given a fixed seed (i.e. the per-seed split is computed once and shared across the 5 conditions). MNIST is downsampled to 8x8 via skimage.transform.resize. Per-cell results are logged to stdout as METRIC_RESULT JSON lines containing condition, dataset, seed, test_accuracy, trainable_parameter_count, training_time_sec.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q07-exec\", \"requirements\": \"Execution-validity bucket: all 30 cells ran with valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q07-exec-cells-ran\", \"requirements\": \"At least 27 cells out of 30 expected (5 conditions x 2 datasets x 3 seeds) completed without unhandled errors and produced a test_accuracy value. Missing more than 3 cells (10 percent) without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q07-exec-numerical\", \"requirements\": \"Numerical validity: test_accuracy values are in [0, 1], not equal to 1/n_classes (would mean random predictions, model didn't train), and the 3 MPS chi levels produce different mean test_accuracy on at least one dataset (max - min across chi=4/8/16 mean accuracy on MNIST is at least 0.01). trainable_parameter_count is reported per cell and matches the expected formula within 1 percent.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q07-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q07-result-h1-quant\", \"requirements\": \"Quantitative test of H1. Does the MPS at chi=16 reach test_accuracy >= 92 percent on 8x8 downsampled MNIST (3-seed mean)? 100% if >= 92 percent, 67% if >= 85 percent, 33% if >= 75 percent, 0% otherwise.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q07-result-h2-quant\", \"requirements\": \"Quantitative test of H2. On 8x8 Fashion-MNIST, does the parameter-matched CNN beat chi=16 MPS by at least 5 absolute percentage points (3-seed mean)? 100% if CNN gap >= 5pp, 67% if CNN gap 2-5pp, 33% if CNN gap 0-2pp, 0% if MPS matches or beats CNN.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q07-result-h3-quant\", \"requirements\": \"Quantitative test of H3. On 8x8 MNIST, is the chi=4 to chi=8 accuracy gain greater than the chi=8 to chi=16 accuracy gain (3-seed mean)? Specifically: (acc_chi8 - acc_chi4) > (acc_chi16 - acc_chi8). 100% if cleanly satisfied with difference of differences >= 1pp, 67% if log-linear trend holds with smaller gap, 33% if marginally so, 0% if linear or super-linear scaling observed.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q07-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by per-(condition, dataset) mean test accuracies and the trainable_parameter_count parity check between chi=8 MPS and small_cnn_matched. References Stoudenmire and Schwab NeurIPS 2016 and discusses whether the observed MNIST vs Fashion-MNIST gap is consistent with the CNN's stronger spatial-bias inductive prior.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q07.yaml", "rubric_file": "tasks/quantum/rubrics/Q07.json"} +{"id": "Q08", "domain": "quantum", "title": "Layerwise learning vs end-to-end training for variational quantum classifiers", "topic": "Layerwise learning vs end-to-end training for variational quantum classifiers: testing the Skolik 2021 claim that incremental ansatz growth avoids barren plateaus on small UCI binary classification tasks", "domains": ["quantum-machine-learning", "training-strategies", "barren-plateaus"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1500, "synthesis": "Variational quantum classifiers (VQCs) at moderate depth (reps >= 5)\nsuffer from barren plateaus: the gradient of the loss with respect to\nany single parameter is exponentially suppressed when the ansatz is\ninitialized randomly across all layers. Skolik et al. (Quantum 2021,\n\"Layerwise learning for quantum neural networks\") proposed a training\nschedule that avoids this trap by adding ansatz layers incrementally.\nConcretely, the agent trains a reps=1 ansatz to convergence, freezes\nits parameters, prepends or appends a fresh reps=1 block initialized\nto identity, trains only the new block to convergence, and so on\nuntil the target depth is reached. Each layer of training sees a much\nsmaller parameter space, so the gradient signal stays well above the\nbarren-plateau floor at every step.\n\nThe empirical question this topic addresses is whether the layerwise\nschedule actually delivers higher final test accuracy AND faster\npractical convergence than naive end-to-end training of the full\nreps=5 ansatz on small UCI binary classification tasks. A\nparameter-matched classical baseline (sklearn MLPClassifier) and a\nlogistic regression baseline serve as the cross-paradigm controls\nrequired by the bench's PROCEED gate; they also calibrate the\ndifficulty of each dataset (logistic regression should fit linear\nseparable cases cleanly).\n\nImplementation guidance is provided by the quantum-qiskit skill,\nwhich is automatically injected at stage 10. Use\nqiskit.circuit.library.EfficientSU2(num_qubits=4, reps=5,\nentanglement='linear') as the underlying ansatz family, and use\nqiskit_machine_learning.algorithms.VQC for training (it works in\nqiskit 2.x; do NOT use qiskit_algorithms.VQE which is broken). For\nthe layerwise condition, the agent must implement the layer-freezing\nschedule manually: construct an ansatz with only the first k blocks\ntrainable and previous blocks bound to their converged parameters.\nThe classical baselines train on the same 80/20 train/test split\nusing the same per-seed random_state.\n\nExperimental protocol. Three quantum conditions compare training\nschedules at the same final depth (reps=5): layerwise growth from\nreps=1 to reps=5, end-to-end training of reps=5 from random\ninitialization, and a random-init control (no training, evaluate\nthe randomly initialized reps=5 ansatz). Two classical baselines\n(logistic regression, MLPClassifier with parameter count matched to\nthe VQC's 40-parameter count) provide the cross-paradigm reference.\nEach (condition, dataset) cell is averaged over 3 seeds. Total:\n5 conditions x 2 datasets x 3 seeds = 30 cells.\n\nResearch question: *Does layerwise incremental training of a VQC\nreach higher test accuracy or faster convergence than end-to-end\ntraining of the same final-depth ansatz, and does the layerwise\ngradient norm at each newly added layer remain above the\nbarren-plateau threshold?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Layerwise training reaches higher test accuracy than end-to-end training on at least 1 of 2 datasets, with a 3-seed-mean gap of at least 3 absolute percentage points.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The mean gradient norm during the first 10 optimization steps of the layerwise schedule (averaged across all newly added blocks) is at least 5 times larger than the mean gradient norm during the first 10 steps of end-to-end training (3-seed mean), confirming the Skolik 2021 mechanism.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Both layerwise and end-to-end VQC outperform the random-init control (no training) by at least 5 absolute percentage points on both datasets (3-seed mean), confirming that training actually contributes — the ansatz itself is not memorizing class structure at initialization.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does layerwise incremental training of a 4-qubit reps=5 VQC reach higher test accuracy or faster convergence than end-to-end training of the same ansatz on small UCI binary classification?\", \"conditions\": [{\"name\": \"layerwise_growth\", \"description\": \"EfficientSU2(num_qubits=4, reps=5, entanglement='linear'). Training schedule: (1) train only the first reps=1 block via VQC.fit() for 80 COBYLA iterations; (2) freeze those parameters, add a fresh reps=1 block initialized to small N(0, 0.01) noise, train only the new block for 80 iterations; repeat until all 5 blocks are trained. Record the gradient norm at the start of each new layer's training.\"}, {\"name\": \"end_to_end\", \"description\": \"EfficientSU2(num_qubits=4, reps=5, entanglement='linear'). Single VQC.fit() call training all 40 parameters simultaneously from random N(0, 0.1) initialization for 400 COBYLA iterations (5 x 80, matching the total iteration budget of the layerwise schedule).\"}, {\"name\": \"random_init_control\", \"description\": \"Same EfficientSU2 ansatz, parameters drawn from Uniform[-pi, pi] and NEVER trained. Evaluate predict() directly on the test set. Floor for trainability — any trained model should beat this.\"}], \"baselines\": [\"logistic_regression: sklearn.linear_model.LogisticRegression(max_iter=1000, random_state=seed). Linear classical baseline.\", \"mlp_matched: sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(8,), activation='relu', max_iter=500, random_state=seed). Parameter count: 4*8 + 8 + 8*1 + 1 = 49 parameters, within 20 percent of the VQC's 40 parameters.\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Held-out 20 percent test split accuracy, 3-seed mean. Primary metric.\"}, {\"name\": \"mean_gradient_norm_first10\", \"direction\": \"maximize\", \"description\": \"L2 norm of the gradient vector averaged across the first 10 optimization steps. For layerwise, averaged across the first 10 steps of each newly added block. For end-to-end, the first 10 steps of the single training run. Tests the Skolik mechanism.\"}, {\"name\": \"iterations_to_target_accuracy\", \"direction\": \"minimize\", \"description\": \"Number of total COBYLA iterations to reach test accuracy >= 0.8 (or to budget 400 if never reached). Tests practical convergence speed.\"}], \"datasets\": [{\"name\": \"uci_iris_binary_pca4\", \"source\": \"sklearn.datasets.load_iris + PCA to 4 dimensions\", \"description\": \"Iris dataset restricted to classes {versicolor=1, virginica=2} (binary, 100 samples). PCA to 4 dimensions on standard-scaled features. 80/20 split. Approximately linearly separable; logistic regression should reach >=85 percent.\"}, {\"name\": \"breast_cancer_pca4\", \"source\": \"sklearn.datasets.load_breast_cancer + PCA to 4 dimensions\", \"description\": \"Wisconsin breast cancer dataset, 569 samples, 30 features reduced to 4 via PCA. 80/20 split. Non-trivially separable; logistic regression reaches around 0.92.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1500}}", "requirements": "", "rubric": "{\"id\": \"q08-root\", \"requirements\": \"An empirical study of layerwise vs end-to-end training of a 4-qubit reps=5 EfficientSU2 VQC on UCI binary classification (iris-binary and breast-cancer). The agent must (a) implement the layerwise training schedule manually (train reps=1, freeze, add reps=1, train, ...), (b) implement the end-to-end baseline that trains all 5 reps together, (c) measure mean gradient norm during the first 10 optimization steps of each schedule to test the Skolik 2021 mechanism, and (d) compare against logistic regression + parameter-matched MLP baselines. Score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"Layerwise studies are scored on (i) correctness of the freeze-and-add mechanism (verifiable: the layerwise condition should have N optimizer.minimize() calls each operating on a subset of parameters, not 1 single call over all parameters), (ii) parameter counts matching across conditions (the layerwise and end-to-end VQCs must end with the same 40-parameter ansatz), (iii) the gradient-norm probe being measured at consistent points across conditions, (iv) the random-init control producing accuracy near 0.5 on both datasets (otherwise the ansatz is leaking labels through initialization).\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q08-code\", \"requirements\": \"Code-development bucket: layerwise freeze-and-add schedule implemented manually, classical baselines parameter-matched, gradient-norm probe consistent.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q08-code-layerwise-mechanism\", \"requirements\": \"The layerwise condition trains the EfficientSU2(reps=5) ansatz by 5 sequential optimizer.minimize() calls. Each call trains only the parameters belonging to the newly added reps=1 block while the previous blocks have their parameters bound to their previously converged values via qiskit.QuantumCircuit.assign_parameters() on a partial dictionary. After all 5 calls the final fitted VQC has 40 trainable parameters, identical in count to the end-to-end VQC.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q08-code-gradient-probe\", \"requirements\": \"Mean gradient norm during the first 10 optimization steps is recorded per condition via the qiskit_machine_learning VQC callback or via a custom wrapper around the loss function that uses parameter-shift gradients. For layerwise, the metric is averaged across the first 10 steps of each newly added block (so total of 50 steps averaged, since 5 blocks x 10). For end-to-end, the metric is the first 10 steps of the single training run.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q08-code-classical-baselines\", \"requirements\": \"mlp_matched uses sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(8,)) for ~49 parameters, within 20 percent of the VQC's 40 parameters. logistic_regression uses sklearn.linear_model.LogisticRegression(max_iter=1000). Both train on the same 80/20 train/test split as the VQC conditions using the same per-seed random_state.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q08-exec\", \"requirements\": \"Execution-validity bucket: all 30 cells ran and produced numerically valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q08-exec-cells-ran\", \"requirements\": \"At least 27 cells out of 30 expected (5 conditions x 2 datasets x 3 seeds) completed without unhandled errors and produced a test_accuracy value. Missing more than 3 cells (10 percent) without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q08-exec-numerical\", \"requirements\": \"Numerical validity: test_accuracy in [0, 1] with non-degenerate predictions (no condition collapses to a single-class predictor on all test samples). The random_init_control condition produces test_accuracy approximately 0.5 +/- 0.1 on both datasets (otherwise label leakage in the untrained ansatz). mean_gradient_norm_first10 is strictly positive across all cells.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q08-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q08-result-h1-quant\", \"requirements\": \"Quantitative test of H1. On at least 1 of 2 datasets, is layerwise test_accuracy >= end-to-end test_accuracy + 3 absolute pp (3-seed mean)? 100% if gap >= 3pp on at least 1 dataset, 67% if gap >= 1pp on at least 1 dataset, 33% if layerwise marginally faster on any dataset, 0% if end-to-end equals or beats layerwise on both datasets.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q08-result-h2-quant\", \"requirements\": \"Quantitative test of H2. Is mean_gradient_norm_first10 of layerwise >= 5x mean_gradient_norm_first10 of end-to-end (3-seed mean, across both datasets combined)? 100% if ratio >= 5x, 67% if 2-5x, 33% if any positive ratio (layerwise > end-to-end), 0% otherwise.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q08-result-h3-quant\", \"requirements\": \"Quantitative test of H3. Do both layerwise and end-to-end test_accuracy exceed random_init_control test_accuracy by >= 5 absolute pp on both datasets (3-seed mean)? 100% if both gaps >= 5pp on both datasets, 67% if both on 1 dataset, 33% if at least one VQC beats random anywhere, 0% if random matches or beats trained VQCs.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q08-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by specific 3-seed mean test_accuracy, mean_gradient_norm_first10, and iterations_to_target_accuracy values per (condition, dataset). References Skolik et al. Quantum 2021 and discusses whether the layerwise gradient-norm advantage matches their theoretical prediction.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q08.yaml", "rubric_file": "tasks/quantum/rubrics/Q08.json"} +{"id": "Q09", "domain": "quantum", "title": "Noise-aware variational quantum classifier training", "topic": "Noise-aware variational quantum classifier training: comparing training under simulated depolarizing noise vs ideal-statevector training, evaluating both robustness to test-time noise and clean-test transfer", "domains": ["ai-for-quantum", "noisy-quantum-training", "robustness"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1500, "synthesis": "Real quantum hardware exhibits gate errors, readout errors, and\ndecoherence at rates that vary across devices. A VQC trained on a\nnoiseless statevector simulator may converge to parameters that work\non the ideal training distribution but degrade sharply when deployed\non a noisy device. The noise-aware training strategy is to inject\nsimulated noise during training (e.g. a depolarizing channel after\neach two-qubit gate) so the optimizer can find parameters that are\nrobust to that noise model. The closest classical analog is\nadversarial training or training-time data augmentation.\n\nTwo open empirical questions follow. First, does noise-aware training\nproduce strictly more robust models on noisy test data than ideal\ntraining, and at what training noise rate is the robustness gain\nworth the loss of clean-test accuracy? Second, does training at high\nnoise (p=0.01) cause negative transfer to clean test data — i.e.\ndoes the model overfit to the noise statistics and underperform an\nideal-trained model on noise-free evaluation? Wang et al. (NeurIPS\n2022 \"QuantumNAS: Noise-Adaptive Search for Robust Quantum\nCircuits\") and Sharma et al. (PRX Quantum 2022 \"Trainability of\ndissipative perceptron-based quantum neural networks\") explored\nthese tradeoffs at small scale, leaving the precise sweet-spot\nnoise rate as a benchmark question.\n\nImplementation guidance is provided by the quantum-qiskit skill at\nstage 10. Use qiskit_aer.AerSimulator with a custom NoiseModel that\napplies a single-qubit and two-qubit depolarizing channel after each\nparameterized gate. The training uses\nqiskit_machine_learning.algorithms.VQC with a custom\nqiskit.primitives.BackendSamplerV2 bound to the noisy AerSimulator\nfor noisy-train conditions, and the noiseless reference\nqiskit.primitives.StatevectorSampler for the ideal-train condition.\nEvaluation always runs on both an ideal statevector test set and a\nnoisy test set at p=0.01 (the \"deployment noise rate\") so each cell\nproduces two test_accuracy values: clean and noisy. The primary\nmetric is the average across both.\n\nExperimental protocol. Four quantum training conditions vary the\ntraining noise rate: ideal (p=0), low noise (p=0.001), mid noise\n(p=0.005), high noise (p=0.01). Two baselines complete the bench\ngate: a no-training control (random initialization, no fit) and\nlogistic regression on the same 4D features. All six conditions\nare evaluated on a single synthetic binary classification dataset\n(make_classification with class_sep=0.5, 200 samples, 4 features),\naveraged over 3 seeds. Total: 6 conditions x 1 dataset x 3 seeds\n= 18 cells.\n\nResearch question: *On a synthetic 4D binary classification task,\ndoes noise-aware VQC training at a moderate training noise rate\n(p=0.005) produce a model that is more robust to test-time\ndepolarizing noise (p=0.01) than an ideal-trained VQC, and does\nhigh training noise (p=0.01) cause negative transfer to clean\ntest data?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On the noisy test set at p=0.01, the noisy-trained-mid VQC (training noise p=0.005) achieves test accuracy at least 5 absolute percentage points higher than the ideal-trained VQC (3-seed mean), demonstrating that moderate noise-aware training improves test-time noise robustness.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On the ideal test set (noiseless), the ideal-trained VQC outperforms every noisy-trained variant by at least 2 absolute percentage points (3-seed mean), reflecting the no-free-lunch tradeoff that noise-aware training sacrifices some clean-test accuracy.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The high-noise-trained VQC (training noise p=0.01) performs worse than the mid-noise-trained VQC on BOTH clean and noisy test sets (3-seed mean), indicating that p=0.01 is past the sweet spot and the optimizer is overfitting to noise statistics — negative transfer.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does noise-aware VQC training at a moderate training noise rate (p=0.005) produce a model more robust to test-time depolarizing noise than ideal training, and does high training noise cause negative transfer on clean test data?\", \"conditions\": [{\"name\": \"train_ideal\", \"description\": \"VQC trained on noiseless qiskit.primitives.StatevectorSampler. EfficientSU2(num_qubits=4, reps=2, entanglement='linear') ansatz + ZFeatureMap feature map. qiskit_machine_learning.VQC(optimizer=COBYLA(maxiter=200), sampler=StatevectorSampler()). After training, evaluate on BOTH ideal test (StatevectorSampler) and noisy test (depolarizing p=0.01 NoiseModel).\"}, {\"name\": \"train_noisy_low\", \"description\": \"Same ansatz + feature map. VQC trained on a noisy AerSimulator with depolarizing channel p=0.001 applied after every single-qubit and two-qubit gate. Same COBYLA maxiter=200. Evaluate on both ideal and noisy test sets.\"}, {\"name\": \"train_noisy_mid\", \"description\": \"Same as train_noisy_low but training noise rate p=0.005.\"}, {\"name\": \"train_noisy_high\", \"description\": \"Same as train_noisy_low but training noise rate p=0.01 (matches the test-noise rate).\"}], \"baselines\": [\"no_training_control: Same ansatz but parameters randomly initialized and never trained. Evaluate predict() directly on both test sets. Establishes the untrained floor.\", \"logistic_regression: sklearn.linear_model.LogisticRegression(max_iter=1000, random_state=seed) on the same 4 features. Classical baseline.\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Average of test_accuracy_ideal and test_accuracy_noisy_p01, 3-seed mean. Primary metric for the bench.\"}, {\"name\": \"test_accuracy_ideal\", \"direction\": \"maximize\", \"description\": \"Test accuracy on the noiseless test set (StatevectorSampler evaluation), 3-seed mean.\"}, {\"name\": \"test_accuracy_noisy_p01\", \"direction\": \"maximize\", \"description\": \"Test accuracy on the noisy test set at depolarizing rate p=0.01 (AerSimulator with NoiseModel), 3-seed mean. The 'deployment-noise' robustness metric.\"}], \"datasets\": [{\"name\": \"synthetic_classification_4d_sep05\", \"source\": \"sklearn.datasets.make_classification\", \"description\": \"make_classification(n_samples=200, n_features=4, n_informative=3, n_redundant=0, n_clusters_per_class=1, class_sep=0.5, flip_y=0.05, random_state=seed). Moderately difficult binary classification, native 4D. StandardScaler fit on train only. 80/20 split.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1500}}", "requirements": "", "rubric": "{\"id\": \"q09-root\", \"requirements\": \"An empirical study of noise-aware VQC training. Four quantum training conditions vary the training-time depolarizing noise rate (ideal, p=0.001, p=0.005, p=0.01), evaluated on both an ideal test set and a noisy test set at p=0.01. The agent must (a) implement the noise model via qiskit_aer.NoiseModel applying single-qubit and two-qubit depolarizing channels, (b) train VQC under each noise level using qiskit_machine_learning.VQC with the appropriate sampler, (c) evaluate each trained model on BOTH ideal and noisy test sets to expose the robustness-vs-clean-accuracy tradeoff, and (d) score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"Noise-aware training studies are scored on (i) correctness of the qiskit_aer NoiseModel construction (depolarizing channel must be applied after every gate, not only after measurement; verifiable by inspecting the agent's noise model setup), (ii) test_accuracy reporting BOTH ideal and noisy variants per cell (without both, H1 cannot be evaluated), (iii) the noise-rate sweep producing visibly different test accuracies (cells reporting identical numbers across noise levels indicate the noise model is not actually being applied during training), and (iv) numerical evidence backing the three-way comparison: ideal-train vs noisy-mid-train vs noisy-high-train.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q09-code\", \"requirements\": \"Code-development bucket: NoiseModel correctly constructed, training pipeline applies noise, evaluation on both ideal and noisy test.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q09-code-noise-model\", \"requirements\": \"qiskit_aer.NoiseModel with depolarizing_error applied to all single-qubit gates (h, ry, rz, etc.) AND two-qubit gates (cx). For a noisy-train condition at rate p, the single-qubit error rate is p and the two-qubit error rate is p (or some agent-documented scaling of p). A sanity check verifies that the same ansatz parameters produce different expectation values under the noise model vs under StatevectorSampler (otherwise the noise is not being applied).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q09-code-training-pipeline\", \"requirements\": \"Each training condition uses qiskit_machine_learning.VQC with a qiskit.primitives.BackendSamplerV2 (or BackendSampler) bound to either AerSimulator (for noisy training) or StatevectorSampler (for ideal training). Same ansatz family (EfficientSU2 num_qubits=4 reps=2 entanglement='linear'), same optimizer (COBYLA maxiter=200), same per-seed random initialization across noise-rate conditions for paired comparison.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"q09-code-dual-eval\", \"requirements\": \"Each trained VQC is evaluated on TWO test sets: (a) ideal StatevectorSampler (test_accuracy_ideal), (b) noisy AerSimulator at depolarizing p=0.01 (test_accuracy_noisy_p01). Both numbers are logged per cell. The primary 'test_accuracy' metric is the average of the two. Each cell's METRIC_RESULT JSON includes all three numbers.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q09-exec\", \"requirements\": \"Execution-validity bucket: all 18 cells ran with valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q09-exec-cells-ran\", \"requirements\": \"At least 16 cells out of 18 expected (6 conditions x 1 dataset x 3 seeds) completed without unhandled errors and produced both test_accuracy_ideal AND test_accuracy_noisy_p01 values. Missing more than 2 cells (10 percent) without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q09-exec-numerical\", \"requirements\": \"Numerical validity: both test accuracies in [0, 1]; for any single training condition, test_accuracy_ideal and test_accuracy_noisy_p01 differ by at least 0.01 (cells reporting bit-identical clean and noisy accuracy mean the noise model isn't being applied at evaluation); no_training_control accuracy is in [0.4, 0.6] (random predictor floor).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q09-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q09-result-h1-quant\", \"requirements\": \"Quantitative test of H1. On the noisy test set at p=0.01, is train_noisy_mid (training noise p=0.005) test_accuracy >= train_ideal test_accuracy + 5 absolute pp (3-seed mean)? 100% if gap >= 5pp, 67% if 2-5pp, 33% if any positive gap, 0% if ideal train matches or beats noisy-mid train on noisy test.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q09-result-h2-quant\", \"requirements\": \"Quantitative test of H2. On the ideal test set, does train_ideal beat EVERY noisy-trained variant by >= 2 absolute pp (3-seed mean)? 100% if gap >= 2pp vs all three noisy variants, 67% if gap >= 2pp vs at least 2 of 3, 33% if marginal advantage on at least 1, 0% if noisy training matches or beats ideal training on clean test (no tradeoff).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q09-result-h3-quant\", \"requirements\": \"Quantitative test of H3. Does train_noisy_high underperform train_noisy_mid on BOTH the clean and the noisy test set (3-seed mean)? 100% if cleanly worse on both, 67% if worse on 1 of 2 by >= 3pp, 33% if marginally worse on 1, 0% if high-noise training matches or beats mid-noise on both test sets (no negative transfer observed).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q09-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by per-(training_noise_rate, test_set) 3-seed mean accuracy values. References Wang NeurIPS 2022 QuantumNAS and Sharma PRX Quantum 2022. Discusses whether the observed sweet-spot noise rate matches the test-deployment rate, and acknowledges that AerSimulator depolarizing channel is a simplified model relative to real hardware noise (T1/T2, crosstalk, readout asymmetry not captured).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q09.yaml", "rubric_file": "tasks/quantum/rubrics/Q09.json"} +{"id": "Q10", "domain": "quantum", "title": "Quantum autoencoder for pure-state compression", "topic": "Quantum autoencoder for pure-state compression: training a parameterized circuit to compress n-qubit Haar-random states into m state (verified via a\nSWAP test against fresh |0> ancillas), which is equivalent to\nmaximizing the fidelity between the reconstructed state and the\noriginal input state.\n\nThe training data is an ensemble of input states drawn from some\ndistribution. If the ensemble is concentrated in a low-dimensional\nmanifold of the 2^n Hilbert space, the autoencoder can in principle\nreach near-perfect reconstruction at significant compression (m <\nn). If the ensemble is Haar-random pure states, no compression is\nachievable in principle and the average reconstruction fidelity is\nupper bounded by 2^(m-n) (the latent space is too small to hold a\ngeneric state).\n\nThe empirical question this topic addresses is what compression\nfidelity is achievable on a small Haar-random ensemble at a few\ncompression ratios, and whether the trained autoencoder\nmeaningfully beats a random-unitary baseline (which represents the\nnull hypothesis: a random encoder cannot do better than chance).\nThis is the cleanest possible test of \"does the autoencoder learn\nanything\" without confounding from structured ensembles.\n\nImplementation guidance is provided by the quantum-qiskit skill at\nstage 10. The encoder and decoder are EfficientSU2(num_qubits=n,\nreps=3, entanglement='linear') circuits built from\nqiskit.circuit.library. The SWAP-test loss is implemented manually\nvia auxiliary qubits and a Hadamard-controlled-SWAP-Hadamard pattern.\nUse qiskit.primitives.StatevectorSampler for the SWAP-test\nmeasurement and qiskit.primitives.StatevectorEstimator for the\nreconstruction-fidelity diagnostic. Training uses\nqiskit_algorithms.optimizers.COBYLA.minimize() over the SWAP-test\nloss (DO NOT use qiskit_algorithms.VQE — see the quantum-qiskit\nskill's note about qiskit 2.x compatibility).\n\nExperimental protocol. Three quantum compression conditions probe\nthe compression-fidelity tradeoff: compress 4 qubits -> 2 latent,\ncompress 4 -> 3, compress 6 -> 3. Two no-skill baselines establish\nthe floor: no_compression (identity encoder, m=n, fidelity should\nbe ~1.0) and random_unitary_compression (encoder parameters drawn\nrandomly and never trained). Test data is an ensemble of 20\nHaar-random pure states per seed. Each (condition, seed) cell\nreports the mean reconstruction fidelity over the 20 test states.\nTotal: 5 conditions x 1 ensemble x 3 seeds = 15 cells.\n\nResearch question: *On an ensemble of Haar-random pure states, what\nreconstruction fidelity does a trained quantum autoencoder achieve\nat compression ratios 4->2, 4->3, and 6->3, and how much better is\nit than an untrained (random-unitary) encoder at the same\ncompression ratio?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The trained autoencoder achieves reconstruction fidelity at least 0.2 absolute higher than a random-unitary encoder at the same compression ratio (3-seed mean across the 20 Haar test states), on at least 2 of 3 compression-ratio conditions, confirming that training extracts non-trivial structure from the Haar ensemble.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Trained reconstruction fidelity is strictly monotone in latent dimension: the 4->3 condition achieves fidelity at least 0.15 higher than the 4->2 condition (3-seed mean), as expected from the Hilbert-space capacity argument 2^m / 2^n.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The no_compression baseline (identity encoder, m=n) achieves reconstruction fidelity at least 0.95 (3-seed mean), confirming the experimental setup is correctly implemented — the trash-qubit register is non-functional when m=n and the test pipeline should report near-perfect fidelity.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"On an ensemble of Haar-random pure states, what reconstruction fidelity does a trained quantum autoencoder achieve at compression ratios 4->2, 4->3, 6->3, and how much better is it than an untrained encoder at the same compression?\", \"conditions\": [{\"name\": \"compress_4_to_2\", \"description\": \"Encoder: EfficientSU2(num_qubits=4, reps=3, entanglement='linear'). After applying the encoder, qubits 2 and 3 are the 'trash' register and the SWAP test against fresh |0> ancillas is summed into the loss. Latent qubits: 0, 1. Decoder is the inverse encoder. Training: 100 iterations of qiskit_algorithms.optimizers.COBYLA(maxiter=100).minimize() on a fixed batch of 5 Haar-random states drawn once per seed.\"}, {\"name\": \"compress_4_to_3\", \"description\": \"Same as compress_4_to_2 but with 1 trash qubit (qubit 3) and 3 latent qubits (0, 1, 2). Easier compression task.\"}, {\"name\": \"compress_6_to_3\", \"description\": \"Encoder: EfficientSU2(num_qubits=6, reps=3, entanglement='linear'). 3 trash qubits (3, 4, 5) and 3 latent qubits (0, 1, 2). Harder compression task (50 percent compression on a larger Hilbert space).\"}], \"baselines\": [\"no_compression: Encoder is the identity circuit (no parameters). All qubits are latent (m=n); no trash qubits. The SWAP test trivially passes and reconstruction fidelity equals 1.0 exactly. Sanity check on the eval pipeline.\", \"random_unitary_compression: Same EfficientSU2 ansatz as the trained conditions, but parameters drawn randomly from Uniform[-pi, pi] once per seed and NEVER trained. Establishes the no-skill floor — represents 'what if the encoder is just random noise'.\"], \"metrics\": [{\"name\": \"reconstruction_fidelity\", \"direction\": \"maximize\", \"description\": \"Mean of ||^2 over the 20 Haar-random test states, 3-seed mean. Primary metric. Computed as 1 - SWAP_test_probability_of_outcome_1 averaged over the trash qubits, OR via direct Statevector inner product when running on the noiseless simulator.\"}, {\"name\": \"swap_test_loss\", \"direction\": \"minimize\", \"description\": \"Training loss at the final iteration. Sum over the m trash qubits of P(SWAP_test_outcome=1) on each. Equivalent to 1 - average_trash_qubit_fidelity_with_|0>.\"}, {\"name\": \"training_time_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock seconds for the 100 COBYLA iterations of training. Reported for context — the compression task should fit well under 60 seconds per cell.\"}], \"datasets\": [{\"name\": \"haar_random_states_n4\", \"source\": \"qiskit.quantum_info.random_statevector(2**4, seed=ensemble_seed) — generate 20 Haar-random 4-qubit pure states with a fixed per-seed ensemble seed\", \"description\": \"An ensemble of 20 Haar-random 4-qubit pure states (for compress_4_to_2 and compress_4_to_3 conditions). A separate Haar ensemble of 20 6-qubit states is generated for the compress_6_to_3 condition. Training uses a fixed batch of 5 of these states; testing uses all 20. The ensemble is fully unstructured by construction, so any non-trivial reconstruction fidelity above the random-unitary baseline reflects the autoencoder learning a structured compression rule on Haar states.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1200}}", "requirements": "", "rubric": "{\"id\": \"q10-root\", \"requirements\": \"An empirical study of a quantum autoencoder on Haar-random pure states. The agent must (a) construct encoder + decoder circuits as EfficientSU2(reps=3) from qiskit.circuit.library, (b) implement the SWAP-test loss via auxiliary qubits + Hadamard-controlled-SWAP-Hadamard or compute trash-qubit fidelity-to-|0> directly via Statevector, (c) train each compression-ratio condition by qiskit_algorithms.optimizers.COBYLA.minimize() on a fixed batch of 5 Haar states per seed, (d) evaluate on 20 unseen Haar states reporting reconstruction_fidelity, and (e) score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"Quantum autoencoder studies are scored on (i) correctness of the SWAP-test or fidelity computation (verifiable: no_compression baseline must report fidelity ~1.0, otherwise the eval pipeline is broken), (ii) the random_unitary_compression baseline producing fidelity in the expected range for that compression ratio (theoretical floor is 2^(m-n) per Page formula, e.g. for 4->2 the random baseline fidelity is around 0.0625), (iii) the trained conditions being measurably above the random baseline (otherwise no learning), and (iv) numerical evidence for the latent-dimension capacity effect (4->3 better than 4->2).\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q10-code\", \"requirements\": \"Code-development bucket: encoder/decoder built from qiskit.circuit.library, SWAP-test or direct-fidelity loss implemented correctly, training pipeline runs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q10-code-encoder-decoder\", \"requirements\": \"Encoder is qiskit.circuit.library.EfficientSU2(num_qubits=n, reps=3, entanglement='linear'). Decoder is the inverse of the encoder (via QuantumCircuit.inverse()) sharing the same parameters but acting in reverse, applied after fresh |0> ancillas have replaced the trash qubits. Both encoder and decoder are built once from qiskit.circuit.library — NOT hand-coded as H/RY/RZ + CNOT stacks (the quantum-qiskit skill warns against this anti-pattern).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q10-code-loss-eval\", \"requirements\": \"Training loss is implemented as either (a) a SWAP-test circuit using auxiliary qubits and the standard Hadamard-controlled-SWAP-Hadamard pattern, OR (b) a direct Statevector computation: trace out the trash register, compute its partial-trace overlap with |0><0|, and sum across trash qubits. Both implementations are valid. The reconstruction_fidelity metric on the test set uses ||^2 computed via qiskit.quantum_info.Statevector inner product (no SWAP test needed at evaluation since we have access to both states).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q10-code-pipeline\", \"requirements\": \"Training: 100 iterations of qiskit_algorithms.optimizers.COBYLA.minimize() over the SWAP-test loss, summed over a fixed batch of 5 Haar-random states drawn once per (condition, seed) pair via qiskit.quantum_info.random_statevector. Evaluation: 20 fresh Haar-random states drawn from a different RNG stream than the training batch. Each cell logs METRIC_RESULT JSON with condition, seed, reconstruction_fidelity_mean (over 20 test states), swap_test_loss_final, training_time_sec.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q10-exec\", \"requirements\": \"Execution-validity bucket: all 15 cells ran with valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q10-exec-cells-ran\", \"requirements\": \"At least 14 cells out of 15 expected (5 conditions x 1 ensemble x 3 seeds) completed without unhandled errors and produced a reconstruction_fidelity_mean value. Missing more than 1 cell without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q10-exec-numerical\", \"requirements\": \"Numerical validity: reconstruction_fidelity is in [0, 1] for every cell; the no_compression baseline reports fidelity in [0.95, 1.0] (else eval pipeline is broken); the random_unitary_compression baseline for 4->2 reports fidelity in [0.04, 0.15] (matching the theoretical 2^(m-n) = 0.0625 average for Haar states); no cell reports identical fidelity across all 3 seeds (would indicate the Haar sampler is not actually randomizing per seed).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q10-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q10-result-h1-quant\", \"requirements\": \"Quantitative test of H1. On at least 2 of 3 compression-ratio conditions, is trained reconstruction_fidelity >= random_unitary_compression reconstruction_fidelity + 0.2 absolute (3-seed mean)? 100% if gap >= 0.2 on at least 2/3 conditions, 67% if gap >= 0.1 on at least 2/3, 33% if any positive gap on at least 1, 0% if random unitary equals or beats trained on all 3.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q10-result-h2-quant\", \"requirements\": \"Quantitative test of H2. Is reconstruction_fidelity(compress_4_to_3) - reconstruction_fidelity(compress_4_to_2) >= 0.15 absolute (3-seed mean)? 100% if gap >= 0.15, 67% if 0.05-0.15, 33% if any positive gap (capacity effect at least directionally correct), 0% otherwise.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q10-result-h3-quant\", \"requirements\": \"Quantitative test of H3. Does no_compression baseline report reconstruction_fidelity >= 0.95 (3-seed mean)? 100% if >= 0.95, 67% if >= 0.85, 33% if >= 0.70, 0% otherwise — failure of this sanity check means the eval pipeline is misconstructed.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q10-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by 3-seed mean reconstruction_fidelity per condition. References Romero, Olson, Aspuru-Guzik Quantum Sci Tech 2017 and discusses whether the trained autoencoder's advantage over random_unitary_compression on Haar-random data is consistent with the Page-formula expectation (Haar states have no exploitable structure, so any improvement reflects the optimizer finding a non-trivial subspace).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q10.yaml", "rubric_file": "tasks/quantum/rubrics/Q10.json"} +{"id": "B01", "domain": "biology", "title": "E. coli succinate-production strain optimization via constraint-based knockout screen", "topic": "E. coli succinate-production strain optimization via constraint-based knockout screen on iAF1260", "domains": ["systems-biology", "metabolic-engineering", "constraint-based-modelling"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1800, "synthesis": "Succinate is a top-12 platform chemical and a recurring case study in\nmetabolic engineering. The standard genome-scale model E. coli iAF1260\n(2382 reactions, 1668 metabolites, 1261 genes) provides a well-validated\ntestbed for in-silico strain design: from a fixed glucose-minimal medium\none can predict (a) the maximum aerobic biomass growth rate, (b) the\nmaximum theoretical succinate secretion flux once growth is held at a feasible\nfraction of optimum, (c) the set of single- and double-gene knockouts\nthat decouple biomass from succinate secretion enough to push the strain\ntoward a high-secretion non-growth-coupled phenotype.\n\nA credible CPU-scale study of this topic (a) loads iAF1260 from BIGG and\nvalidates aerobic biomass > 0.7 1/h on glucose-minimal medium, (b) runs\nparsimonious FBA (pFBA) at the wild-type optimum and reports the central\ncarbon flux distribution, (c) sweeps the production envelope (biomass vs\nsuccinate secretion) using flux variability analysis (FVA) on the\nsuccinate exchange across a grid of biomass-fraction-of-optimum values,\n(d) runs a single-gene knockout screen (≤100 candidate genes drawn from\ncentral carbon metabolism, fermentation, and TCA cycle) and ranks each KO\nby post-KO succinate secretion flux at 50% of WT growth, (e) reports the top three\nKO strains with mechanistic explanation tied to the network topology.\n\nThe research question is: *which single-gene knockouts of central carbon\nmetabolism in E. coli iAF1260 produce the largest predicted succinate\nsecretion flux while preserving at least 50% of wild-type biomass growth, and what\nis the mechanistic role of each in shifting flux toward succinate?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Wild-type aerobic E. coli iAF1260 on glucose-minimal medium predicts a biomass growth rate within ±10% of 0.736 1/h (the published BIGG iAF1260 reference value).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The biomass-vs-succinate production envelope, computed via FVA at biomass fractions from 0.1 to 1.0 of WT optimum, is monotonically non-increasing in succinate as biomass approaches the WT optimum (i.e., succinate is growth-competing under aerobic glucose conditions).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"At least 3 of the top-5 single-gene knockouts identified by the screen as maximizing succinate secretion flux at 50% WT-growth disrupt canonical competing by-product or respiration pathways described in the metabolic-engineering literature (e.g., pyruvate-formate-lyase pflB, lactate dehydrogenase ldhA, alcohol dehydrogenase adhE, acetate kinase ackA, phosphate acetyltransferase pta, or their isozymes), or are accompanied by a mechanistic explanation for why the knockout redirects flux toward succinate under the chosen medium.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which single-gene knockouts of central carbon metabolism in E. coli iAF1260 produce the largest predicted succinate secretion flux while preserving at least 50% of wild-type biomass growth, and what is the mechanistic role of each in shifting flux toward succinate?\", \"conditions\": [{\"name\": \"wt_aerobic_glucose\", \"description\": \"Wild-type iAF1260 on glucose-minimal medium with O2 unconstrained. Reference condition.\"}, {\"name\": \"wt_aerobic_glucose_pfba\", \"description\": \"Same as wt_aerobic_glucose but with pFBA (parsimonious FBA) for unique flux distribution.\"}, {\"name\": \"production_envelope_succinate\", \"description\": \"FVA on succinate exchange across biomass-fraction-of-optimum grid {0.1, 0.2, ..., 1.0}.\"}, {\"name\": \"single_ko_screen_central_carbon\", \"description\": \"Single-gene knockouts on a curated set of ≤100 central-carbon / fermentation / TCA genes (glycolysis, PPP, TCA, anaplerotic, fermentation by-products); for each, FBA at fixed biomass = 0.5 × WT optimum; record succinate exchange flux.\"}], \"baselines\": [\"wt_aerobic_glucose (no perturbation) is the no-engineering baseline\", \"Published BIGG iAF1260 reference growth rate (0.736 1/h on glucose-minimal aerobic) — the model-validity gate\"], \"metrics\": [{\"name\": \"wt_growth_rate\", \"direction\": \"match_reference\", \"description\": \"Wild-type biomass exchange flux (1/h) on glucose-minimal aerobic; reference value 0.736.\"}, {\"name\": \"max_succinate_flux_mmol_gDW_h_at_zero_growth\", \"direction\": \"maximize\", \"description\": \"Succinate exchange flux (mmol/gDW/h) at biomass = 0 (theoretical maximum).\"}, {\"name\": \"succinate_flux_mmol_gDW_h_at_half_growth\", \"direction\": \"maximize\", \"description\": \"Succinate exchange flux at biomass = 0.5 × WT optimum, wild-type strain.\"}, {\"name\": \"best_ko_succinate_flux_mmol_gDW_h\", \"direction\": \"maximize\", \"description\": \"Maximum succinate exchange flux over the single-KO screen at biomass = 0.5 × WT optimum.\"}], \"datasets\": [{\"name\": \"iAF1260\", \"source\": \"BIGG database (http://bigg.ucsd.edu/models/iAF1260) via cobra.io.load_model('iAF1260')\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1800}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metrics (object of numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_wt_growth_value\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain a numeric value for `wt_growth_observed_1_per_h` (or an equivalent wt_growth_*_1_per_h key) that is non-null and finite. The wild-type growth rate must be reported, otherwise H1 cannot be evaluated.\", \"must_pass\": true}, {\"id\": \"req_top5_ko\", \"type\": \"discussion\", \"description\": \"results.json structured_results MUST contain a list of the top 5 ranked single-gene knockouts with at least {gene_id, gene_name, ko_succinate_flux_mmol_gDW_h, is_canonical} fields each. The list need not include the textbook canonical genes — but it MUST exist and identify at least 5 candidates.\", \"must_pass\": true}, {\"id\": \"req_envelope_figure\", \"type\": \"artifact\", \"description\": \"A production-envelope figure (biomass-vs-succinate, or analogous) must exist under figures/ or analysis/ in PDF or PNG format with axes labeled and units shown. Either a single multi-curve figure or per-condition figures both qualify.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"Top-3 KO targets are discussed mechanistically — naming the pathway each KO disables and a one-sentence rationale for why the disruption redirects flux to succinate. Acceptable inside summary, structured_results, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and any RNG seeds used (e.g. for sampling) are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B01\", \"requirements\": \"A credible constraint-based experiment on E. coli iAF1260 that (a) loads the BIGG model and validates wild-type growth, (b) computes a flux distribution under the standard objective, (c) characterises the biomass-vs-succinate production envelope, (d) runs a single-gene knockout screen targeted at central carbon metabolism, and (e) ties the top-ranked knockouts to the network mechanism. Partial but well-motivated evidence deserves partial credit; rigid wording or naming should not penalize a substantively correct experiment.\", \"judging_note\": \"Score on biological substance and quantitative correctness, not exact threshold satisfaction. Reproducing canonical FBA numbers (WT growth ~0.7, well-known knockouts like pflB/ldhA/adhE/ackA-pta) is strong evidence; novel-but-coherent design choices are acceptable.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b01-code\", \"requirements\": \"The constraint-based pipeline is implemented with COBRApy and a BIGG genome-scale model.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b01-code-model\", \"requirements\": \"The submission loads E. coli iAF1260 (or a comparable validated genome-scale metabolic model) from BIGG via COBRApy, with the medium constraints (glucose uptake, O2 availability) explicitly set and the biomass objective explicitly named.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b01-code-fba\", \"requirements\": \"FBA and pFBA are invoked correctly to obtain wild-type growth and a unique flux distribution; output flux table covers exchange + central-carbon reactions at minimum.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b01-code-envelope\", \"requirements\": \"The biomass-vs-succinate production envelope is computed by sweeping a fraction-of-optimum biomass constraint and running FVA (or two-step FBA) on the succinate exchange at each grid point.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b01-code-koscreen\", \"requirements\": \"The single-gene knockout screen iterates over a curated central-carbon / fermentation / TCA gene set (≤100), applies model.genes..knock_out() (or equivalent reaction-bound deletion), and records succinate flux at fixed biomass = 0.5 × WT optimum.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b01-exec\", \"requirements\": \"Execution produces the FBA / FVA / KO numbers needed to evaluate the hypotheses without crashing.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b01-exec-validity\", \"requirements\": \"Solver returns optimal status for the wild-type FBA and at least 90% of the KO conditions; no negative biomass or non-physical fluxes (succinate secretion negative under aerobic glucose, etc.) appear without explanation.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b01-exec-physics\", \"requirements\": \"Mass balance, charge balance, and biomass-positive checks pass for the loaded model; this is the biology-validity gate analogous to physics-validity (gauge invariance, unitarity) in the physics rubric.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b01-exec-results\", \"requirements\": \"A machine-readable results artifact (results.json or simulations/*.csv) records WT growth rate, the production-envelope grid (biomass fraction → succinate secretion flux in mmol/gDW/h), and a per-KO succinate-flux table. If yield is reported, define it separately as succinate secretion flux divided by glucose uptake flux.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b01-results\", \"requirements\": \"The results analysis addresses the three hypotheses with quantitative evidence and a clear narrative tied to network mechanism.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b01-result-h1-quant\", \"requirements\": \"Quantitative test of H1: predicted WT aerobic growth rate is reported and compared to the published BIGG reference 0.736 1/h. Score 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b01-result-h2-quant\", \"requirements\": \"Quantitative test of H2: production envelope shows succinate is growth-competing — succinate flux is non-increasing as biomass approaches WT optimum. Score 100% if monotonic over ≥8 of 10 grid points, 67% if ≥6 of 10, 33% if ≥4 of 10.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b01-result-h3-quant\", \"requirements\": \"Quantitative test of H3: among the top-5 KO ranked by succinate secretion flux at 0.5 × WT growth, count how many disrupt canonical competing by-product or respiration pathways for succinate-overproduction logic, such as {pflB, ldhA, adhE, ackA, pta, acetate/ethanol/lactate branch competitors, oxygen-respiration competitors}, or include a specific mechanistic explanation for redirecting flux toward succinate under the chosen medium. Score 100% if ≥3 of 5 meet this canonical-or-mechanistic criterion, 67% if 2 of 5, 33% if 1 of 5, 0% otherwise.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b01-result-figure\", \"requirements\": \"At least one publication-quality figure is produced — typically the production envelope (biomass on x, succinate secretion flux on y, with WT and best KO overlaid) and/or the KO essentiality / flux heatmap. Axes labeled with units (1/h, mmol/gDW/h), legend present.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b01-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, names the top knockouts with their gene/enzyme identity, and gives a one-paragraph mechanistic interpretation (which competing pathways the KOs disable, why this redirects flux to succinate).\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b01-repro\", \"requirements\": \"Reproducibility: the model, run cards, and seeds are checked into artifacts so a fresh clone can rerun and obtain matching numbers.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b01-repro-model\", \"requirements\": \"The loaded GSMM is either persisted (models/iAF1260.json) or pinned by version + BIGG URL in the writeup; a downstream user can locate the exact model object used.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b01-repro-runcards\", \"requirements\": \"The medium definition (exchange bounds), objective function, FVA fraction-of-optimum, and KO gene list are saved to a config / params file (not only inline in code).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b01-repro-seeds\", \"requirements\": \"Solver settings (LP backend name + version, tolerance) are recorded; if any sampling step is used, the RNG seed is logged. A second run reproduces the central WT growth rate to within solver tolerance.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B01.yaml", "rubric_file": "tasks/biology/rubrics/B01.json"} +{"id": "B02", "domain": "biology", "title": "E. coli acetate overflow metabolism phase map under glucose and oxygen limitation", "topic": "E. coli acetate overflow metabolism phase map under glucose and oxygen limitation", "domains": ["systems-biology", "constraint-based-modelling", "phase-plane-analysis"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1800, "synthesis": "Overflow acetate secretion in E. coli is a canonical constraint-based\nmodelling phenotype. A credible CPU-scale study loads a validated E. coli\ngenome-scale model, fixes a glucose-minimal medium, sweeps glucose and oxygen\nuptake bounds, computes growth and major secretion products, and identifies\nthe transition from respiratory growth to overflow/fermentative by-product\nsecretion.\n\nThe research question is: under which glucose and oxygen uptake regimes does\nE. coli switch from primarily respiratory growth to acetate-secreting overflow\nmetabolism, and how robust is that regime boundary across FBA and pFBA?", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The selected E. coli BIGG model grows on aerobic glucose-minimal medium with a positive biomass objective and no infeasible solver status.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"A two-dimensional glucose-vs-oxygen phase plane contains distinct regimes with high oxygen/low acetate and low oxygen/high acetate secretion.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"pFBA reduces total internal flux while preserving the same growth-rate regime boundaries to within a small tolerance.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Under which glucose and oxygen uptake regimes does E. coli switch from respiratory growth to acetate overflow?\", \"conditions\": [{\"name\": \"aerobic_glucose_reference\", \"description\": \"Wild-type E. coli iJO1366 or iAF1260 on glucose-minimal medium with oxygen available.\"}, {\"name\": \"glucose_oxygen_phase_plane\", \"description\": \"Grid over glucose uptake and oxygen uptake bounds; compute biomass, acetate, ethanol, lactate, succinate, and CO2 exchange fluxes.\"}, {\"name\": \"pfba_phase_plane_check\", \"description\": \"Run pFBA on the same grid or a representative subset and compare growth plus secretion regimes.\"}], \"baselines\": [\"High-oxygen glucose condition is the respiratory baseline.\", \"Zero-oxygen glucose condition is the fermentative baseline.\"], \"metrics\": [{\"name\": \"growth_rate_1_per_h\", \"direction\": \"maximize\", \"description\": \"Biomass objective flux for each nutrient grid point.\"}, {\"name\": \"acetate_flux_mmol_gDW_h\", \"direction\": \"characterize\", \"description\": \"Acetate exchange secretion flux.\"}, {\"name\": \"overflow_boundary_o2_bound\", \"direction\": \"estimate\", \"description\": \"Approximate oxygen bound below which acetate secretion exceeds a chosen threshold.\"}, {\"name\": \"pfba_total_flux_norm\", \"direction\": \"minimize\", \"description\": \"Total absolute flux under pFBA.\"}], \"datasets\": [{\"name\": \"E. coli GSMM\", \"source\": \"BIGG iJO1366 or iAF1260 via cobra.io.load_model\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1800}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A results.json file exists with primary_metric, metrics, hypotheses, summary, and structured_results.\", \"must_pass\": true}, {\"id\": \"req_phase_plane_table\", \"type\": \"artifact\", \"description\": \"A machine-readable phase-plane table exists with glucose_bound, oxygen_bound, growth_rate, and acetate_flux columns.\", \"must_pass\": true}, {\"id\": \"req_phase_plane_figure\", \"type\": \"artifact\", \"description\": \"A PNG or PDF heatmap/phase-plane figure exists with glucose and oxygen axes and units.\", \"must_pass\": true}, {\"id\": \"req_aerobic_ref_growth\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain a numeric value for `aerobic_glucose_growth_rate_1_per_h` (or an equivalent aerobic_growth_*_1_per_h key) that is non-null, finite, and positive. The aerobic reference growth rate must be reported, otherwise H1 cannot be evaluated.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\\n\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The overflow boundary and its mechanistic basis are discussed — why E. coli switches to acetate-secreting overflow at the identified nutrient threshold. Acceptable inside summary, phase_plane_interpretation, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B02\", \"requirements\": \"A credible constraint-based E. coli acetate-overflow phase-plane study that loads a validated BIGG model, sets glucose and oxygen medium bounds, sweeps a two-dimensional nutrient grid, reports growth and by-product secretion, and interprets the acetate overflow boundary.\", \"judging_note\": \"Score biological substance and quantitative reproducibility over exact thresholds. iJO1366, iAF1260, or a comparable curated E. coli GSMM are acceptable.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b02-code\", \"requirements\": \"The COBRApy implementation defines the model, medium, phase-plane grid, FBA/pFBA runs, and secretion extraction.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b02-code-model\", \"requirements\": \"Loads a validated E. coli BIGG model, explicitly sets glucose and oxygen exchange bounds, and names the biomass objective.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b02-code-grid\", \"requirements\": \"Constructs a two-dimensional glucose-by-oxygen grid with at least 8 values per axis and restores model state between conditions.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b02-code-products\", \"requirements\": \"Extracts biomass plus acetate, ethanol, lactate, succinate, CO2, and O2/glucose exchange fluxes where present.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b02-code-pfba\", \"requirements\": \"Runs pFBA on the full grid or a justified representative subset and records total absolute flux.\", \"weight\": 4, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b02-exec\", \"requirements\": \"Execution produces stable phase-plane artifacts without infeasible or missing-condition failures dominating the study.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b02-exec-status\", \"requirements\": \"At least 90% of grid points solve to optimal or biologically expected zero-growth status with explicit handling.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b02-exec-physics\", \"requirements\": \"Mass-balance check passes for the loaded model; no non-physical secretion fluxes appear under aerobic conditions without explicit justification (e.g. acetate secretion is zero under high-oxygen/low-glucose conditions in the reference FBA); biomass flux is positive under aerobic glucose reference. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b02-exec-artifacts\", \"requirements\": \"Writes results.json and a CSV/TSV phase-plane table with nutrient bounds, growth, and secretion fluxes.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b02-results\", \"requirements\": \"The analysis identifies and explains acetate-overflow regimes with figures and quantitative criteria.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b02-result-h1\", \"requirements\": \"Reports reference aerobic glucose growth and confirms model feasibility and positive biomass production. Score 100% if a positive finite biomass flux is reported for aerobic glucose with optimal solver status; 67% if growth is positive but solver status is not verified; 33% if only feasibility is stated without a numeric growth value; 0% otherwise.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b02-result-h2\", \"requirements\": \"Quantifies an acetate secretion boundary or threshold across oxygen/glucose regimes and distinguishes respiratory vs overflow conditions. Score 100% if an acetate-overflow boundary is quantified (O2 or glucose uptake threshold stated) with ≥2 distinct secretion regimes visible in the phase-plane table; 67% if overflow is identified without a quantified boundary; 33% if only one secretion regime is described; 0% otherwise.\", \"weight\": 12, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b02-result-h3\", \"requirements\": \"Compares FBA and pFBA growth/secretion regimes and explains where they agree or differ. Score 100% if FBA and pFBA growth rates are numerically compared and total flux norms are reported for both; 67% if only growth rates are compared without flux-norm comparison; 33% if pFBA was run but not compared to FBA; 0% otherwise.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b02-result-figure\", \"requirements\": \"Produces at least one labelled heatmap or contour plot for growth and acetate secretion with units.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b02-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, characterises the acetate-overflow regime boundary with quantitative support, and gives a one-paragraph mechanistic interpretation of why E. coli shifts from respiratory to acetate-secreting overflow metabolism at the identified nutrient threshold.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b02-repro\", \"requirements\": \"The model ID, medium, grid values, solver, and thresholds are recorded for rerun reproducibility.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b02-repro-model\", \"requirements\": \"The loaded E. coli GSMM is either persisted (models/.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object used.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b02-repro-config\", \"requirements\": \"Saves model ID, objective reaction, nutrient grid, and secretion threshold in config or results metadata.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b02-repro-solver\", \"requirements\": \"Records solver backend, tolerance, COBRApy version, and rerun consistency for the aerobic reference point.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B02.yaml", "rubric_file": "tasks/biology/rubrics/B02.json"} +{"id": "B03", "domain": "biology", "title": "Anaerobic E. coli lactate-overproduction knockout screen", "topic": "Anaerobic E. coli lactate-overproduction knockout screen", "domains": ["systems-biology", "metabolic-engineering", "constraint-based-modelling"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1800, "synthesis": "Lactate production under anaerobic glucose conditions is a tractable\nmetabolic-engineering task for constraint-based modelling. A credible study\nloads an E. coli BIGG model, validates anaerobic growth, computes WT lactate\nsecretion, screens central-carbon and fermentation knockouts, and ranks\ninterventions that increase lactate secretion while preserving growth.", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The model supports positive anaerobic glucose growth after oxygen uptake is closed.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The lactate production envelope shows a tradeoff between biomass growth and maximum lactate secretion.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Top lactate-improving knockouts disable competing ethanol, formate, acetate, or succinate by-product routes, or have a specific mechanistic explanation.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which single-gene knockouts increase anaerobic lactate secretion in E. coli while preserving at least 30-50% of WT growth?\", \"conditions\": [{\"name\": \"wt_anaerobic_glucose\", \"description\": \"E. coli iJO1366 or iAF1260 on glucose-minimal medium with oxygen uptake closed.\"}, {\"name\": \"lactate_production_envelope\", \"description\": \"Sweep biomass fraction and optimize lactate secretion.\"}, {\"name\": \"single_ko_screen_fermentation\", \"description\": \"Screen central-carbon and fermentation genes; rank by lactate flux at fixed growth fraction.\"}], \"baselines\": [\"Wild-type anaerobic glucose lactate secretion.\", \"No-knockout lactate envelope.\"], \"metrics\": [{\"name\": \"anaerobic_wt_growth_1_per_h\", \"direction\": \"maximize\", \"description\": \"WT biomass flux with oxygen uptake set to zero.\"}, {\"name\": \"lactate_flux_mmol_gDW_h\", \"direction\": \"maximize\", \"description\": \"Lactate exchange secretion flux.\"}, {\"name\": \"lactate_yield_per_glucose\", \"direction\": \"maximize\", \"description\": \"Lactate secretion divided by glucose uptake.\"}, {\"name\": \"growth_fraction\", \"direction\": \"threshold\", \"description\": \"KO biomass relative to WT anaerobic growth.\"}], \"datasets\": [{\"name\": \"E. coli GSMM\", \"source\": \"BIGG iJO1366 or iAF1260 via cobra.io.load_model\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1800}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"results.json contains primary_metric, metrics, hypotheses, summary, and top KO structured results.\", \"must_pass\": true}, {\"id\": \"req_anaerobic_medium\", \"type\": \"numeric\", \"description\": \"Oxygen uptake is explicitly closed and anaerobic WT growth is reported.\", \"must_pass\": true}, {\"id\": \"req_top5_ko\", \"type\": \"discussion\", \"description\": \"Top 5 lactate-ranked knockouts are reported with gene_id, gene_name, growth_fraction, lactate_flux, and mechanism/canonical flag.\", \"must_pass\": true}, {\"id\": \"req_envelope_figure\", \"type\": \"artifact\", \"description\": \"A biomass-vs-lactate production envelope figure exists with labelled axes and units.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\\n\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"Top-3 lactate-improving KO targets are discussed mechanistically — naming the pathway each KO disables and a one-sentence rationale for why the disruption redirects anaerobic flux to lactate. Acceptable inside summary, structured_results, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B03\", \"requirements\": \"A credible anaerobic E. coli lactate-overproduction study using COBRApy that validates anaerobic growth, computes a lactate production envelope, screens a focused central-carbon/fermentation knockout set, and explains top candidates mechanistically.\", \"judging_note\": \"Accept iJO1366, iAF1260, or comparable curated E. coli models. Score coherent anaerobic setup and mechanistic KO interpretation over exact gene names.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b03-code\", \"requirements\": \"COBRApy code implements anaerobic medium, lactate objective/envelope, and knockout screening.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b03-code-medium\", \"requirements\": \"Loads an E. coli BIGG model, sets glucose uptake, closes oxygen uptake, and names biomass and lactate exchange reactions.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b03-code-envelope\", \"requirements\": \"Computes biomass-vs-lactate production envelope by constraining biomass fractions and optimizing or FVA-bounding lactate secretion.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b03-code-ko\", \"requirements\": \"Screens a focused set of no more than 100 central-carbon and fermentation genes with model state restored between knockouts.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b03-code-yield\", \"requirements\": \"Computes lactate yield as lactate secretion divided by absolute glucose uptake, separate from flux.\", \"weight\": 4, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b03-exec\", \"requirements\": \"Execution produces valid anaerobic WT, envelope, and KO tables.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b03-exec-validity\", \"requirements\": \"WT anaerobic FBA is optimal with positive biomass and at least 85% of KO simulations solve or are explicitly labelled infeasible/lethal.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b03-exec-physics\", \"requirements\": \"Mass-balance check passes for the loaded anaerobic model; no aerobic by-products (O2 uptake or CO2 via respiration) appear under strictly anaerobic conditions without explanation; biomass flux is positive under anaerobic glucose reference. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b03-exec-artifacts\", \"requirements\": \"Writes results.json plus machine-readable envelope and KO-ranking tables.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b03-results\", \"requirements\": \"Results quantify lactate production, growth tradeoffs, and KO mechanisms.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b03-result-h1\", \"requirements\": \"Reports anaerobic WT growth and confirms oxygen uptake is zero or bounded closed. Score 100% if positive anaerobic WT biomass flux is reported with O2 exchange confirmed ≤0 mmol/gDW/h; 67% if growth is positive but O2 status is not explicitly confirmed; 33% if anaerobic feasibility is stated without a numeric growth value; 0% otherwise.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b03-result-h2\", \"requirements\": \"Shows lactate secretion changes across biomass fractions and identifies whether production is growth-coupled, competing, or partially coupled. Score 100% if lactate secretion flux is non-decreasing as biomass fraction decreases across ≥8 of 10 envelope grid points; 67% if non-decreasing over ≥6 of 10 points; 33% if ≥4 of 10 points; 0% otherwise.\", \"weight\": 10, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b03-result-h3\", \"requirements\": \"Top 5 KO list includes growth fraction, lactate flux/yield, and pathway-level rationale for at least top 3 targets. Score 100% if ≥3 of the top-5 KOs disrupt canonical competing by-product pathways (acetate, ethanol, formate, or succinate routes) or include a specific mechanistic explanation; 67% if 2 of 5 meet this criterion; 33% if 1 of 5; 0% otherwise.\", \"weight\": 12, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b03-result-figure\", \"requirements\": \"Produces a labelled lactate envelope or KO ranking figure with units.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b03-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, names the top lactate-improving knockouts with their gene/enzyme identity, and gives a one-paragraph mechanistic interpretation (which competing pathways the KOs disable, why this redirects anaerobic flux to lactate).\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b03-repro\", \"requirements\": \"Medium, model, gene set, solver, and thresholds are reproducibly recorded.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b03-repro-model\", \"requirements\": \"The loaded E. coli GSMM is either persisted (models/.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object and reproduce the anaerobic medium setup.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b03-repro-runcard\", \"requirements\": \"Saves anaerobic medium bounds, biomass threshold, KO gene set, and lactate reaction ID.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b03-repro-solver\", \"requirements\": \"Records COBRApy/solver versions and confirms rerun consistency for WT growth.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B03.yaml", "rubric_file": "tasks/biology/rubrics/B03.json"} +{"id": "B04", "domain": "biology", "title": "S. cerevisiae ethanol yield under oxygen limitation and carbon-source changes", "topic": "S. cerevisiae ethanol yield under oxygen limitation and carbon-source changes", "domains": ["systems-biology", "metabolic-engineering", "constraint-based-modelling"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1800, "synthesis": "Yeast ethanol fermentation is a natural extension beyond E. coli while still\nstaying inside BIGG/COBRApy constraint-based modelling. A credible study\nloads iMM904 or a comparable yeast GSMM, validates growth on glucose, sweeps\noxygen availability, compares glucose and alternative carbon sources, and\nquantifies ethanol secretion and yield.", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The yeast model grows on glucose-minimal medium and produces a feasible FBA solution.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Ethanol secretion/yield increases as oxygen uptake is restricted relative to fully aerobic growth.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Carbon-source swaps produce distinct growth and ethanol-yield profiles, with glucose supporting stronger fermentation than at least one alternative carbon source.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do oxygen limitation and carbon source alter predicted ethanol secretion and yield in S. cerevisiae?\", \"conditions\": [{\"name\": \"yeast_glucose_reference\", \"description\": \"S. cerevisiae iMM904 or comparable model on glucose-minimal medium.\"}, {\"name\": \"oxygen_sweep\", \"description\": \"Sweep oxygen uptake from anaerobic/low oxygen to aerobic while glucose uptake is fixed.\"}, {\"name\": \"carbon_source_swap\", \"description\": \"Compare glucose, fructose, galactose, glycerol, and acetate when supported by the model.\"}], \"baselines\": [\"Aerobic glucose condition.\", \"Anaerobic or oxygen-limited glucose condition.\"], \"metrics\": [{\"name\": \"growth_rate_1_per_h\", \"direction\": \"maximize\", \"description\": \"Biomass flux.\"}, {\"name\": \"ethanol_flux_mmol_gDW_h\", \"direction\": \"maximize\", \"description\": \"Ethanol exchange secretion flux.\"}, {\"name\": \"ethanol_yield_per_carbon_uptake\", \"direction\": \"maximize\", \"description\": \"Ethanol secretion divided by substrate uptake.\"}], \"datasets\": [{\"name\": \"S. cerevisiae iMM904\", \"source\": \"BIGG iMM904 via cobra.io.load_model if available, or documented local SBML/JSON yeast model.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1800}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"results.json contains yeast model ID, metrics, hypotheses, and structured oxygen/carbon-source results.\", \"must_pass\": true}, {\"id\": \"req_oxygen_sweep\", \"type\": \"artifact\", \"description\": \"A machine-readable oxygen-sweep table exists with oxygen bound, growth, ethanol flux, and yield.\", \"must_pass\": true}, {\"id\": \"req_carbon_source_table\", \"type\": \"artifact\", \"description\": \"A carbon-source comparison table exists, including unsupported sources marked explicitly.\", \"must_pass\": true}, {\"id\": \"req_figure\", \"type\": \"artifact\", \"description\": \"At least one oxygen-vs-ethanol or carbon-source yield figure exists with units.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\\n\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The ethanol-yield response to oxygen and carbon source is discussed mechanistically — why oxygen restriction increases ethanol yield and how carbon-source metabolism shapes fermentation. Acceptable inside summary, structured_results, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B04\", \"requirements\": \"A credible S. cerevisiae ethanol-yield study that loads a yeast GSMM, validates growth, sweeps oxygen uptake, swaps carbon sources, and reports ethanol secretion/yield with interpretable figures.\", \"judging_note\": \"The main challenge is adapting the pipeline beyond E. coli while remaining within COBRApy/BIGG capabilities. Accept iMM904 or another documented yeast GSMM.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b04-code\", \"requirements\": \"The code implements yeast model loading, medium setup, oxygen sweep, carbon-source swap, and ethanol-yield calculations.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b04-code-model\", \"requirements\": \"Loads iMM904 or comparable yeast model, identifies biomass, glucose, oxygen, and ethanol exchange reactions, and validates positive reference growth.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b04-code-o2\", \"requirements\": \"Sweeps oxygen uptake across at least 8 values while keeping substrate uptake defined and restoring state between runs.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b04-code-carbon\", \"requirements\": \"Compares at least 4 carbon sources where model reactions exist and explicitly records unsupported sources.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b04-code-yield\", \"requirements\": \"Calculates ethanol yield using absolute substrate uptake and separates yield from raw secretion flux.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b04-exec\", \"requirements\": \"Execution produces oxygen-sweep and carbon-source artifacts without crashing.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b04-exec-status\", \"requirements\": \"Reference glucose condition is optimal and at least 80% of supported oxygen/carbon conditions solve or are clearly marked infeasible.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b04-exec-physics\", \"requirements\": \"Mass-balance check passes for the yeast GSMM; no non-physical fluxes appear under aerobic glucose reference (e.g. ethanol secretion should be low under full aerobic conditions); biomass flux is positive under yeast glucose-minimal reference. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b04-exec-artifacts\", \"requirements\": \"Writes results.json, oxygen_sweep.csv, and carbon_source_comparison.csv or equivalent structured outputs.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b04-results\", \"requirements\": \"Results quantify oxygen and substrate effects on ethanol secretion/yield.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b04-result-h1\", \"requirements\": \"Reports yeast reference growth with model ID and medium definition. Score 100% if positive yeast biomass flux and ethanol secretion are both reported under aerobic glucose-minimal medium with optimal solver status and a named model ID; 67% if growth is positive but ethanol is not reported; 33% if feasibility is stated without numeric values; 0% otherwise.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b04-result-h2\", \"requirements\": \"Quantifies how ethanol flux or yield changes as oxygen decreases and identifies the low-oxygen fermentation regime. Score 100% if ethanol flux or yield is non-decreasing as oxygen uptake decreases across ≥6 of 8 oxygen-sweep points; 67% if non-decreasing over ≥4 of 8 points; 33% if the trend is described without a quantitative grid; 0% otherwise.\", \"weight\": 12, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b04-result-h3\", \"requirements\": \"Compares growth and ethanol yield across carbon sources with a biological interpretation. Score 100% if at least 3 carbon sources are compared with distinct numeric ethanol yields and a biological interpretation; 67% if 2 sources are compared with numeric differences; 33% if the comparison is qualitative or only one carbon source produces positive growth; 0% otherwise.\", \"weight\": 10, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b04-result-figure\", \"requirements\": \"Produces labelled figures for oxygen sweep and/or carbon-source yield comparison.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b04-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, names the oxygen regime and carbon source that maximise ethanol yield, and gives a one-paragraph mechanistic interpretation of why oxygen restriction switches yeast from respiratory growth to fermentative ethanol production.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b04-repro\", \"requirements\": \"Model source, medium, oxygen grid, carbon-source reactions, solver, and versions are documented.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b04-repro-model\", \"requirements\": \"The loaded yeast GSMM is either persisted (models/.json) or pinned by BIGG model ID, version, and source URL in the writeup; a downstream user can locate the exact model object used.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b04-repro-config\", \"requirements\": \"Saves reaction IDs, bounds, grid values, and carbon-source list in config or results metadata.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b04-repro-version\", \"requirements\": \"Records COBRApy, solver, model source/version, and rerun consistency for reference growth.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B04.yaml", "rubric_file": "tasks/biology/rubrics/B04.json"} +{"id": "B05", "domain": "biology", "title": "M. tuberculosis condition-specific essentiality and metabolic drug-target prioritisation", "topic": "M. tuberculosis condition-specific essentiality and metabolic drug-target prioritisation", "domains": ["systems-biology", "drug-target-prioritisation", "constraint-based-modelling"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 2400, "synthesis": "Constraint-based essentiality analysis can prioritise metabolic drug-target\nhypotheses in pathogen models. A credible study loads an M. tuberculosis GSMM\nsuch as iNJ661 when available, validates biomass production under a documented\nmedium, performs single-gene and/or single-reaction deletion, and ranks\nessential targets by growth impact and subsystem interpretability.", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The selected M. tuberculosis model produces positive biomass under the documented reference medium.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Single-gene or single-reaction deletion identifies a non-empty set of essential metabolic targets under the reference condition.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Prioritised targets are enriched in interpretable core metabolic subsystems such as cell-wall precursor, cofactor, lipid, energy, or amino-acid metabolism.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which condition-specific metabolic genes or reactions are predicted essential in M. tuberculosis and are plausible drug-target hypotheses?\", \"conditions\": [{\"name\": \"mtb_reference_medium\", \"description\": \"M. tuberculosis iNJ661 or comparable model under a documented reference medium.\"}, {\"name\": \"single_gene_deletion\", \"description\": \"Single-gene deletion screen when GPR rules are available.\"}, {\"name\": \"single_reaction_deletion\", \"description\": \"Single-reaction deletion screen as a fallback or complementary target set.\"}], \"baselines\": [\"Wild-type reference growth.\", \"Nonessential deletion growth distribution.\"], \"metrics\": [{\"name\": \"wt_growth_rate_1_per_h\", \"direction\": \"maximize\", \"description\": \"Reference biomass flux.\"}, {\"name\": \"growth_fraction_after_deletion\", \"direction\": \"minimize\", \"description\": \"Deletion biomass divided by WT biomass.\"}, {\"name\": \"essential_target_count\", \"direction\": \"characterize\", \"description\": \"Count of genes/reactions below essentiality threshold.\"}, {\"name\": \"subsystem_enrichment\", \"direction\": \"characterize\", \"description\": \"Subsystem distribution among essential targets.\"}], \"datasets\": [{\"name\": \"M. tuberculosis GSMM\", \"source\": \"BIGG iNJ661 or documented local SBML/JSON model.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 2400}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"results.json contains model ID, WT growth, essential target counts, top targets, and hypothesis verdicts.\", \"must_pass\": true}, {\"id\": \"req_deletion_table\", \"type\": \"artifact\", \"description\": \"A gene or reaction deletion table exists with target ID, status, growth, growth_fraction, and essential flag.\", \"must_pass\": true}, {\"id\": \"req_top_targets\", \"type\": \"discussion\", \"description\": \"Top 10 prioritised essential targets are reported with subsystem/pathway and rationale.\", \"must_pass\": true}, {\"id\": \"req_figure\", \"type\": \"artifact\", \"description\": \"At least one essentiality distribution or subsystem-enrichment figure exists.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\\n\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"Top-3 essential target hypotheses are framed mechanistically — naming the metabolic subsystem each target belongs to and why its disruption is predicted lethal under the reference medium. Claims must be framed as computational hypotheses, not validated drugs. Acceptable inside summary, structured_results, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B05\", \"requirements\": \"A credible M. tuberculosis essentiality study using COBRApy that validates a pathogen GSMM, runs single-gene and/or single-reaction deletions, identifies essential targets, and prioritises interpretable drug-target hypotheses.\", \"judging_note\": \"Accept gene deletions, reaction deletions, or both depending on model GPR support. Penalize unsupported biological claims more than absence of external drug databases.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b05-code\", \"requirements\": \"The code loads the pathogen model, validates medium, runs deletion screens, and annotates essential targets.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b05-code-model\", \"requirements\": \"Loads iNJ661 or a documented M. tuberculosis GSMM, defines medium/objective, and validates positive WT biomass.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b05-code-deletion\", \"requirements\": \"Runs single-gene deletion when GPR rules are usable, or single-reaction deletion as an explicit fallback/complement.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b05-code-threshold\", \"requirements\": \"Defines essentiality threshold such as growth <5% or <10% of WT and applies it consistently.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b05-code-annotation\", \"requirements\": \"Extracts subsystem, reaction name, gene name, GPR, or other model-native annotations for top targets.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b05-exec\", \"requirements\": \"Deletion runs complete with interpretable status handling and structured outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b05-exec-status\", \"requirements\": \"WT reference condition is optimal and at least 85% of deletion simulations solve or are explicitly marked infeasible/lethal.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b05-exec-physics\", \"requirements\": \"Mass-balance check passes for the loaded pathogen GSMM; WT biomass is positive under the documented medium; deletion table contains no negative growth values; model GPR rules produce logically consistent essential gene predictions. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b05-exec-artifacts\", \"requirements\": \"Writes results.json plus deletion table with growth, growth_fraction, status, and essential flag.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b05-results\", \"requirements\": \"Results identify essential targets and provide conservative biological interpretation.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b05-result-h1\", \"requirements\": \"Reports WT growth, medium, model ID, and validation caveats. Score 100% if positive WT biomass flux is reported under the documented medium with optimal solver status and model ID named; 67% if growth is positive but medium is not explicitly documented; 33% if feasibility is stated without a numeric value; 0% otherwise.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b05-result-h2\", \"requirements\": \"Reports essential target count and distribution of growth fractions after deletion. Score 100% if ≥5 essential genes/reactions are identified with growth fractions below the stated threshold and the deletion table covers ≥50 candidates; 67% if essential targets are identified but the candidate set covers fewer than 50 entries; 33% if essential targets are named without a deletion table; 0% otherwise.\", \"weight\": 10, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b05-result-h3\", \"requirements\": \"Top 10 targets include subsystem/pathway and one-sentence rationale; claims are framed as modelling hypotheses, not validated drugs. Score 100% if ≥3 of the top-10 prioritised targets belong to named core metabolic subsystems (cell-wall precursor, cofactor, lipid, energy, or amino-acid biosynthesis) with at least a one-sentence pathway rationale; 67% if 2 of 10 with rationale; 33% if 1 of 10 with rationale; 0% otherwise.\", \"weight\": 12, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b05-result-figure\", \"requirements\": \"Produces a labelled essentiality histogram, target ranking plot, or subsystem enrichment chart.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b05-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, names the top prioritised essential targets with subsystem/enzyme identity, and gives a one-paragraph mechanistic rationale for why each target's disruption is lethal. Claims are explicitly framed as computational hypotheses requiring wet-lab validation.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b05-repro\", \"requirements\": \"Model, medium, deletion type, threshold, solver, and annotations are reproducibly recorded.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b05-repro-model\", \"requirements\": \"The loaded M. tuberculosis GSMM is either persisted (models/.json) or pinned by BIGG model ID, version, or local file hash in the writeup; a downstream user can locate the exact model object used.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b05-repro-config\", \"requirements\": \"Saves model source, medium bounds, objective, deletion mode, and essentiality threshold.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b05-repro-version\", \"requirements\": \"Records COBRApy/solver versions and confirms rerun consistency for WT growth and essential count.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B05.yaml", "rubric_file": "tasks/biology/rubrics/B05.json"} +{"id": "B06", "domain": "biology", "title": "E. coli carbon-source robustness and condition-dependent essential genes", "topic": "E. coli carbon-source robustness and condition-dependent essential genes", "domains": ["systems-biology", "essentiality-analysis", "constraint-based-modelling"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 2400, "synthesis": "Carbon-source swaps are a direct mfa-agent capability and produce interpretable\ncondition-dependent phenotypes. A credible study loads an E. coli GSMM, defines\na base minimal medium, tests multiple single-carbon sources, runs essentiality\nanalysis on a focused gene set under each feasible source, and identifies genes\nwhose essentiality changes with carbon source.", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The model predicts positive growth on glucose and at least two additional supported carbon sources.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Growth rates and secretion profiles differ substantially across carbon sources.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"A focused central-carbon gene set contains condition-dependent essential genes whose deletion effects vary by carbon source.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which E. coli metabolic vulnerabilities are carbon-source dependent across glucose, glycerol, acetate, succinate, and fructose-like conditions?\", \"conditions\": [{\"name\": \"carbon_source_panel\", \"description\": \"Close background carbon uptake, open one carbon source at a time, and run FBA/pFBA.\"}, {\"name\": \"focused_gene_essentiality_by_carbon\", \"description\": \"For feasible carbon sources, delete a focused set of central-carbon genes and record growth fractions.\"}], \"baselines\": [\"Glucose-minimal medium.\", \"Wild-type growth for each carbon source.\"], \"metrics\": [{\"name\": \"growth_rate_by_carbon\", \"direction\": \"characterize\", \"description\": \"WT biomass flux per carbon source.\"}, {\"name\": \"major_secretions_by_carbon\", \"direction\": \"characterize\", \"description\": \"Positive exchange fluxes under pFBA.\"}, {\"name\": \"condition_dependent_essential_count\", \"direction\": \"maximize\", \"description\": \"Genes essential under one source but not another.\"}], \"datasets\": [{\"name\": \"E. coli GSMM\", \"source\": \"BIGG iJO1366 or iAF1260 via cobra.io.load_model\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 2400}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"results.json contains carbon-source growth table summary, essentiality counts, hypothesis verdicts, and summary.\", \"must_pass\": true}, {\"id\": \"req_carbon_table\", \"type\": \"artifact\", \"description\": \"A carbon-source comparison table exists with source, exchange reaction, status, growth, and secretion profile fields.\", \"must_pass\": true}, {\"id\": \"req_essentiality_matrix\", \"type\": \"artifact\", \"description\": \"A gene-by-carbon essentiality/growth-fraction matrix exists for feasible carbon sources.\", \"must_pass\": true}, {\"id\": \"req_heatmap\", \"type\": \"artifact\", \"description\": \"A heatmap or clustered plot of condition-dependent gene essentiality exists with labelled axes.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\\n\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"Top-3 condition-dependent essential genes are explained mechanistically — why each gene is essential under one carbon source but not another, referencing the metabolic pathway context. Acceptable inside summary, structured_results, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B06\", \"requirements\": \"A credible E. coli carbon-source robustness study that loads a validated GSMM, swaps carbon sources, reports growth/secretion profiles, and identifies condition-dependent essential genes from a focused gene set.\", \"judging_note\": \"Score explicit medium handling and condition-dependent interpretation. It is acceptable if some carbon sources are unsupported, provided they are recorded and not silently dropped.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b06-code\", \"requirements\": \"The code implements carbon-source medium swaps, FBA/pFBA, and focused essentiality comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b06-code-model\", \"requirements\": \"Loads E. coli BIGG model, identifies biomass and candidate carbon exchange reactions, and defines a minimal medium policy.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b06-code-swap\", \"requirements\": \"Closes background carbon uptake and opens one carbon source at a time with documented uptake bounds.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b06-code-secretion\", \"requirements\": \"Runs FBA/pFBA per source and records major positive exchange fluxes.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b06-code-essentiality\", \"requirements\": \"Runs focused single-gene deletions for feasible carbon sources and computes growth fractions relative to each source-specific WT.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b06-exec\", \"requirements\": \"Execution produces carbon-source and essentiality artifacts.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b06-exec-validity\", \"requirements\": \"Glucose reference is optimal and at least two non-glucose sources are either feasible or clearly marked unsupported/infeasible.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b06-exec-physics\", \"requirements\": \"Mass-balance check passes for the E. coli GSMM under each tested carbon source; no carbon source produces negative biomass or non-physical secretion without explicit handling; model state is restored between carbon-source conditions. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b06-exec-artifacts\", \"requirements\": \"Writes results.json, carbon-source table, and gene-by-condition growth-fraction matrix.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b06-results\", \"requirements\": \"Results compare carbon sources and identify condition-dependent vulnerabilities.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b06-result-h1\", \"requirements\": \"Reports feasible carbon-source count and growth rates, including unsupported-source handling. Score 100% if ≥3 carbon sources including glucose support positive biomass growth with explicit exchange bounds documented; 67% if 2 sources support positive growth; 33% if only glucose is feasible with explicit confirmation that other sources are infeasible or unsupported; 0% otherwise.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b06-result-h2\", \"requirements\": \"Compares secretion profiles and growth rates across sources with quantitative differences. Score 100% if growth rates differ by >10% between at least 2 feasible carbon sources AND major secretion fluxes differ qualitatively across sources; 67% if growth rate differences are reported without secretion-profile comparison; 33% if differences are noted qualitatively without quantification; 0% otherwise.\", \"weight\": 9, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b06-result-h3\", \"requirements\": \"Identifies genes with large growth-fraction changes across carbon sources and explains pathway context for top hits. Score 100% if ≥3 genes with condition-dependent essentiality changes (essential under one carbon source but not another) are identified with pathway context; 67% if 2 genes are identified; 33% if 1 gene is identified with pathway context; 0% otherwise.\", \"weight\": 12, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b06-result-figure\", \"requirements\": \"Produces labelled bar plots and/or heatmaps for growth, secretion, and condition-dependent essentiality.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b06-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, names supported and unsupported carbon sources, identifies condition-dependent essential genes, and gives a one-paragraph mechanistic interpretation linking carbon-source specific metabolism to the observed vulnerability changes.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b06-repro\", \"requirements\": \"Carbon-source definitions, gene set, thresholds, solver, and model are documented.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b06-repro-model\", \"requirements\": \"The loaded E. coli GSMM is either persisted (models/.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object and reproduce each carbon-source medium setup.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b06-repro-runcard\", \"requirements\": \"Saves carbon exchange IDs, uptake bounds, oxygen setting, gene list, and essentiality threshold.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b06-repro-version\", \"requirements\": \"Records COBRApy/solver versions and rerun consistency for glucose and at least one non-glucose source.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B06.yaml", "rubric_file": "tasks/biology/rubrics/B06.json"} +{"id": "B07", "domain": "biology", "title": "Reproducible FBA protocol benchmark: FBA vs pFBA vs loopless/FVA on E. coli", "topic": "Reproducible FBA protocol benchmark: FBA vs pFBA vs loopless/FVA on E. coli", "domains": ["systems-biology", "method-benchmarking", "constraint-based-modelling"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1800, "synthesis": "A method benchmark is well matched to mfa-agent because it tests robust use of\nCOBRApy APIs rather than biological novelty alone. A credible study loads a\nvalidated E. coli GSMM, runs standard FBA, pFBA, loopless FBA when available,\nand FVA at a fixed fraction of optimum, then compares growth, flux sparsity,\nruntime, and reaction-level variability.", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"FBA, pFBA, and loopless FBA preserve the same biomass optimum within solver tolerance under the same medium.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"pFBA produces a lower total absolute flux norm and/or fewer active reactions than unconstrained FBA.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"FVA reveals that only a subset of central-carbon reactions are tightly constrained near optimum while others remain variable.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do standard FBA, pFBA, loopless FBA, and FVA differ in flux parsimony and variability while preserving E. coli growth predictions?\", \"conditions\": [{\"name\": \"standard_fba\", \"description\": \"Maximize biomass on aerobic glucose-minimal medium.\"}, {\"name\": \"pfba\", \"description\": \"Maximize biomass then minimize total absolute flux.\"}, {\"name\": \"loopless_fba\", \"description\": \"Run loopless solution when available and compare objective/fluxes.\"}, {\"name\": \"fva_95_percent_growth\", \"description\": \"Run FVA at 95% of optimum and classify rigid vs flexible reactions.\"}], \"baselines\": [\"Standard FBA biomass optimum.\", \"COBRApy solver status and runtime.\"], \"metrics\": [{\"name\": \"growth_rate_1_per_h\", \"direction\": \"match\", \"description\": \"Biomass flux for each method.\"}, {\"name\": \"total_abs_flux\", \"direction\": \"minimize\", \"description\": \"Sum of absolute reaction fluxes.\"}, {\"name\": \"active_reaction_count\", \"direction\": \"minimize\", \"description\": \"Number of reactions with absolute flux above tolerance.\"}, {\"name\": \"fva_width\", \"direction\": \"characterize\", \"description\": \"Maximum minus minimum FVA interval per reaction.\"}], \"datasets\": [{\"name\": \"E. coli GSMM\", \"source\": \"BIGG iJO1366 or iAF1260 via cobra.io.load_model\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1800}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"results.json contains method-level metrics, hypotheses, summary, and solver/version metadata.\", \"must_pass\": true}, {\"id\": \"req_method_table\", \"type\": \"artifact\", \"description\": \"A method-comparison table exists with method, status, growth, total_abs_flux, active_reactions, and runtime.\", \"must_pass\": true}, {\"id\": \"req_fva_table\", \"type\": \"artifact\", \"description\": \"An FVA table exists with reaction ID, minimum, maximum, width, and rigid/flexible classification.\", \"must_pass\": true}, {\"id\": \"req_figures\", \"type\": \"artifact\", \"description\": \"At least one labelled figure compares methods or FVA variability.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\\n\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The protocol comparison is summarised with a method recommendation — which method is most reproducible and why, referencing flux-norm and FVA evidence. Acceptable inside summary, method_comparison, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B07\", \"requirements\": \"A reproducible FBA-method benchmark on E. coli that compares standard FBA, pFBA, loopless FBA when available, and FVA near optimum using consistent medium, metrics, runtime logging, and interpretable figures.\", \"judging_note\": \"This is a protocol-quality task. Reward robust implementation, structured outputs, and careful handling of unavailable loopless solvers over biological novelty.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b07-code\", \"requirements\": \"The code implements method comparison with consistent model state and metrics.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b07-code-model\", \"requirements\": \"Loads E. coli BIGG model, sets medium and biomass objective, and validates reference FBA.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b07-code-methods\", \"requirements\": \"Runs standard FBA, pFBA, and loopless FBA or records a justified loopless-unavailable status.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b07-code-fva\", \"requirements\": \"Runs FVA at a documented fraction of optimum and computes interval widths and rigid/flexible labels.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b07-code-metrics\", \"requirements\": \"Computes total absolute flux, active reaction count, objective value, solver status, and runtime for each method.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b07-exec\", \"requirements\": \"Execution produces complete benchmark artifacts and handles optional method failures explicitly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b07-exec-status\", \"requirements\": \"Standard FBA and pFBA solve optimally; loopless and FVA either solve or have explicit, non-crashing failure records.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b07-exec-physics\", \"requirements\": \"Mass-balance check passes for the loaded model under the benchmark medium; FBA, pFBA, and loopless FBA all return non-negative biomass fluxes; no negative flux norms or ill-conditioned solver status appear without explicit logging. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b07-exec-artifacts\", \"requirements\": \"Writes results.json, method comparison table, FVA table, and method flux tables.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b07-results\", \"requirements\": \"Results compare objective preservation, parsimony, and reaction variability.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b07-result-h1\", \"requirements\": \"Reports objective differences among methods and checks agreement within solver tolerance where methods succeed. Score 100% if FBA and pFBA biomass objectives agree within 1% relative error and any available loopless FBA also agrees within 1%; 67% if FBA and pFBA agree but loopless comparison is missing or failed with explicit explanation; 33% if only qualitative agreement is stated; 0% otherwise.\", \"weight\": 9, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b07-result-h2\", \"requirements\": \"Quantifies pFBA parsimony using total flux norm and active reaction count compared with standard FBA. Score 100% if pFBA total absolute flux norm is numerically lower than FBA AND active reaction count is lower under pFBA; 67% if flux norm is lower but active reaction count is not compared; 33% if pFBA parsimony is stated qualitatively without numeric comparison; 0% otherwise.\", \"weight\": 10, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b07-result-h3\", \"requirements\": \"Summarizes FVA width distribution and names central-carbon reactions that are rigid or flexible near optimum. Score 100% if FVA interval widths are reported for all model reactions and ≥5 central-carbon reactions are classified as rigid (interval width < 1 mmol/gDW/h) with subsystem identification; 67% if rigid/flexible classification is reported without central-carbon specifics; 33% if FVA results are reported without classification; 0% otherwise.\", \"weight\": 10, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b07-result-figure\", \"requirements\": \"Produces labelled method-comparison and/or FVA-width figures with units and clear legends.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b07-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, summarises the method-level comparison (parsimony, variability, runtime), and gives a one-paragraph protocol recommendation stating which method is most suitable for reproducible phenotype prediction and why.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b07-repro\", \"requirements\": \"The benchmark is reproducible with pinned model, medium, tolerance, solver, and code path.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b07-repro-model\", \"requirements\": \"The loaded E. coli GSMM is either persisted (models/.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object and reproduce the benchmark medium.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b07-repro-config\", \"requirements\": \"Saves model ID, objective, medium bounds, FVA fraction, active-flux tolerance, and method list.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b07-repro-version\", \"requirements\": \"Records COBRApy, optlang, solver backend/version, and runtime environment.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B07.yaml", "rubric_file": "tasks/biology/rubrics/B07.json"} +{"id": "S01", "domain": "statistics", "title": "Bootstrap confidence intervals under non-Gaussian data", "topic": "Bootstrap confidence interval coverage under light-tailed, skewed, and heavy-tailed data", "domains": ["statistics", "resampling-methods", "simulation-study"], "arxiv_id": null, "venue": "ARC-Bench Statistics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 300, "synthesis": "Bootstrap confidence intervals are widely used when analytic standard errors\nare inconvenient, but their finite-sample coverage can change substantially\nwith skewness, heavy tails, contamination, and the choice of statistic. A\ncredible study of this topic should simulate data-generating processes with\nknown estimands, compare several bootstrap confidence interval methods, and\nevaluate empirical coverage and interval width.\n\nThe study should not only report whether nominal 95% intervals contain the\ntruth, but also explain when wider intervals, robust estimators, or\nstudentized intervals improve reliability. The research question is: *how do\nbootstrap interval choices and estimator choices affect empirical coverage\nunder light-tailed, skewed, and heavy-tailed data?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Percentile bootstrap intervals for the sample mean achieve near-nominal coverage under light-tailed Gaussian data, especially at moderate sample sizes.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Percentile bootstrap intervals for the sample mean under-cover under heavy-tailed or contaminated data.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Robust location estimators, such as the median or trimmed mean, improve coverage stability or interval behavior under heavy-tailed or contaminated data.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do bootstrap confidence interval methods behave across light-tailed, skewed, and heavy-tailed distributions?\", \"estimands\": [{\"name\": \"population_mean\", \"description\": \"The true mean of each simulated data-generating process when it exists.\"}, {\"name\": \"robust_location\", \"description\": \"A robust target such as the median or trimmed mean, used when heavy tails or contamination make mean-based inference unstable.\"}], \"conditions\": [{\"name\": \"normal_theory_mean_ci\", \"description\": \"Classical normal or t-based interval for the sample mean.\"}, {\"name\": \"percentile_bootstrap_mean\", \"description\": \"Percentile bootstrap interval for the sample mean.\"}, {\"name\": \"studentized_bootstrap_mean\", \"description\": \"Studentized or bootstrap-t interval for the sample mean, or a justified bootstrap alternative.\"}, {\"name\": \"robust_estimator_bootstrap\", \"description\": \"Bootstrap interval for a robust estimator such as the median or trimmed mean.\"}], \"data_generating_conditions\": [{\"name\": \"gaussian\", \"description\": \"Light-tailed data where standard mean inference should work well.\"}, {\"name\": \"lognormal_skewed\", \"description\": \"Skewed data where percentile intervals may show asymmetric finite-sample behavior.\"}, {\"name\": \"student_t_heavy_tailed\", \"description\": \"Heavy-tailed data with unstable sample means.\"}, {\"name\": \"contaminated_normal\", \"description\": \"Mostly normal data with a small fraction of extreme outliers.\"}], \"metrics\": [{\"name\": \"coverage\", \"direction\": \"target_0.95\", \"description\": \"Empirical fraction of intervals containing the true estimand.\"}, {\"name\": \"coverage_error\", \"direction\": \"minimize_abs\", \"description\": \"Absolute deviation from nominal 95% coverage.\"}, {\"name\": \"interval_width\", \"direction\": \"minimize_given_coverage\", \"description\": \"Average interval width, interpreted jointly with coverage.\"}, {\"name\": \"failure_rate\", \"direction\": \"minimize\", \"description\": \"Fraction of simulation runs where an interval could not be computed or produced invalid values.\"}], \"simulation_settings\": {\"sample_sizes\": [30, 100, 500], \"monte_carlo_repetitions\": 500, \"bootstrap_resamples\": 1000, \"seeds\": [0, 1, 2, 3, 4]}, \"expected_artifacts\": [{\"path\": \"src/experiment.py\", \"description\": \"Executable simulation code implementing confidence interval methods and data-generating processes.\"}, {\"path\": \"results/metrics.json\", \"description\": \"Machine-readable coverage, coverage error, interval width, and failure rate by method, distribution, and sample size.\"}, {\"path\": \"results/figures/\", \"description\": \"Diagnostic plots comparing coverage and interval width across methods and data-generating conditions.\"}, {\"path\": \"report/paper.md\", \"description\": \"Paper-style report explaining the setup, methods, results, limitations, and per-hypothesis conclusions.\"}], \"dependencies\": {\"python\": [\"numpy\", \"pandas\", \"scipy\", \"matplotlib\"]}, \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 300}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_coverage_metrics_table\", \"type\": \"artifact\", \"description\": \"A machine-readable metrics artifact (e.g. results/metrics.json) exists reporting empirical coverage AND interval width broken down by CI method, data-generating distribution, and sample size.\", \"must_pass\": true}, {\"id\": \"req_coverage_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) empirical coverage values in [0, 1] for distinct (method, distribution) cells, including at least one Gaussian cell and one heavy-tailed or contaminated cell so H1 and H2 can be evaluated.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical coverage / interval-width evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_diagnostic_figure\", \"type\": \"artifact\", \"description\": \"At least one figure file (PNG or PDF, ≥150 DPI for raster) exists under results/figures/ comparing coverage and/or interval width across methods and data-generating conditions, with axes labeled and a legend.\", \"must_pass\": false}, {\"id\": \"req_width_tradeoff_writeup\", \"type\": \"discussion\", \"description\": \"The report discusses the coverage-versus-interval-width tradeoff — recognising that higher coverage may simply come from wider intervals and that width must be read jointly with coverage. Nice-to-have, not blocking.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names the Monte Carlo repetition count, bootstrap resample count, and at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"s01-root\", \"requirements\": \"A credible simulation study evaluating whether bootstrap confidence interval choice affects empirical coverage under light-tailed, skewed, and heavy-tailed data. The submission should implement multiple CI methods, simulate several data-generating distributions and sample sizes, report coverage and interval width over repeated trials, and connect the numeric findings to the three hypotheses.\", \"judging_note\": \"Score on statistical substance, correct simulation design, and directional correctness of evidence. Do not require exact repetition counts if the submission is computationally reasonable. Partial but well-motivated simulations deserve partial credit; rigid naming differences should not penalize a substantively correct experiment.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"s01-code\", \"requirements\": \"The bootstrap simulation code implements meaningful confidence interval comparisons across relevant distributional regimes.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"s01-code-ci-methods\", \"requirements\": \"The submission implements at least two bootstrap confidence interval methods, typically percentile bootstrap and studentized/bootstrap-t or another justified alternative. Methods should be implemented as distinct code paths rather than cosmetic options.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s01-code-robust-estimator\", \"requirements\": \"The submission includes a robust location estimator, such as median or trimmed mean, and compares it against the ordinary sample mean under heavy-tailed or contaminated data.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s01-code-dgps\", \"requirements\": \"The submission simulates multiple data-generating processes covering at least light-tailed and heavy-tailed cases, with skewed data such as lognormal or contaminated data receiving additional credit.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"s01-code-sample-sizes\", \"requirements\": \"The simulation evaluates more than one sample size so that finite-sample behavior can be distinguished from asymptotic behavior.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"s01-exec\", \"requirements\": \"Execution produces coverage and interval-quality metrics adequate to evaluate bootstrap CI behavior.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"s01-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact, such as results/metrics.json, containing numeric empirical coverage and interval width by condition, distribution, and sample size. Coverage error or failure rate may supplement these metrics.\", \"weight\": 15.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s01-exec-repetitions\", \"requirements\": \"Reported metrics are based on repeated Monte Carlo trials and bootstrap resampling. The exact number of repetitions need not match the topic file, but it should be large enough to make coverage comparisons meaningful and should be honestly reported.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s01-exec-truth\", \"requirements\": \"The simulation correctly defines the true estimand for each data-generating process, such as the true mean or true robust location target, and checks whether confidence intervals contain that estimand.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"s01-paper\", \"requirements\": \"The final paper or report addresses the three bootstrap hypotheses with quantitative evidence and a clear statistical narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"s01-result-h1\", \"requirements\": \"The submission evaluates whether percentile bootstrap confidence intervals achieve near-nominal coverage under light-tailed Gaussian data, especially at moderate or larger sample sizes, and states whether H1 is supported, refuted, or inconclusive.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s01-result-h2\", \"requirements\": \"The submission evaluates whether percentile bootstrap intervals for the sample mean undercover under heavy-tailed data and supports the conclusion with empirical coverage numbers.\", \"weight\": 15.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s01-result-h3\", \"requirements\": \"The submission compares robust location estimators against the ordinary sample mean under heavy-tailed or contaminated data and discusses whether robustness improves coverage stability or interval behavior.\", \"weight\": 15.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s01-result-width-tradeoff\", \"requirements\": \"The analysis discusses interval-width tradeoffs, recognizing that higher coverage may come from wider intervals and that width should be interpreted jointly with coverage.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s01-result-writeup\", \"requirements\": \"The README or writeup describes the simulation setup, CI methods, data-generating processes, key numeric results, and per-hypothesis outcomes with appropriate caveats on repetition count, bootstrap count, and Monte Carlo uncertainty.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 12, "manifest_file": "tasks/statistics/manifests/S01.yaml", "rubric_file": "tasks/statistics/rubrics/S01.json"} +{"id": "S02", "domain": "statistics", "title": "Double machine learning for average treatment effect estimation", "topic": "Double machine learning for average treatment effect estimation under nonlinear confounding", "domains": ["statistics", "causal-inference", "semiparametric-estimation"], "arxiv_id": null, "venue": "ARC-Bench Statistics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 300, "synthesis": "Semiparametric causal inference separates a low-dimensional target parameter,\nsuch as the average treatment effect, from infinite-dimensional nuisance\nfunctions such as the propensity score e(X) and outcome regressions m0(X) and\nm1(X). Double machine learning studies how the target parameter can remain\nestimable when those nuisance functions are learned flexibly rather than\nspecified by a finite-dimensional parametric model.\n\nThe benchmark should make this relationship explicit. The study should\nimplement and evaluate treatment effect estimators in simulated observational\ndata with nonlinear confounding, compare naive regression or inverse-propensity\nweighting against orthogonalized / doubly robust estimators, and test how\nNeyman orthogonality and cross-fitting reduce first-order sensitivity to\nnuisance estimation error. The research question is: *when the nuisance\nfunctions are treated as infinite-dimensional objects and estimated with\nflexible learners, does orthogonalization preserve reliable inference for the\nfinite-dimensional ATE?*", "num_hypotheses": 4, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"A naive difference-in-means estimator is biased under confounded treatment assignment.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"A doubly robust / orthogonalized estimator has lower absolute bias than simple plug-in regression or IPW when nuisance functions are nonlinear.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Cross-fitting improves stability or reduces bias compared with fitting nuisance functions and estimating the treatment effect on the same sample.\", \"measurable\": true}, {\"id\": \"H4\", \"statement\": \"Neyman-orthogonal scores make the ATE estimate less sensitive to nuisance-function estimation error than non-orthogonal plug-in estimators.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How does double machine learning estimate a finite-dimensional ATE while using flexible estimators for infinite-dimensional nuisance functions under nonlinear confounding?\", \"estimand\": {\"name\": \"average_treatment_effect\", \"description\": \"ATE = E[Y(1) - Y(0)] in a simulated observational study with known ground truth.\"}, \"semiparametric_structure\": {\"target_parameter\": {\"name\": \"finite_dimensional_ate\", \"description\": \"The scalar ATE is the parameter of scientific interest.\"}, \"nuisance_functions\": [{\"name\": \"propensity_score\", \"notation\": \"e(X) = P(A = 1 | X)\", \"role\": \"Controls confounded treatment assignment and appears in IPW and AIPW scores.\"}, {\"name\": \"outcome_regression_treated\", \"notation\": \"m1(X) = E[Y | A = 1, X]\", \"role\": \"Models the treated potential-outcome regression.\"}, {\"name\": \"outcome_regression_control\", \"notation\": \"m0(X) = E[Y | A = 0, X]\", \"role\": \"Models the control potential-outcome regression.\"}], \"key_relationship\": \"The benchmark should evaluate whether an orthogonal score protects the finite-dimensional ATE estimate from first-order errors in flexible, effectively infinite-dimensional nuisance estimates.\"}, \"conditions\": [{\"name\": \"difference_in_means\", \"description\": \"Naive treated-control mean difference without confounding adjustment.\"}, {\"name\": \"ols_adjustment\", \"description\": \"Linear regression adjustment for covariates.\"}, {\"name\": \"ipw_logistic\", \"description\": \"Inverse propensity weighting using logistic regression propensity scores.\"}, {\"name\": \"aipw_no_crossfit\", \"description\": \"Augmented inverse propensity weighting using nuisance models fit on the same sample.\"}, {\"name\": \"dml_aipw_crossfit\", \"description\": \"Doubly robust AIPW estimator with K-fold cross-fitting and flexible nuisance models.\"}, {\"name\": \"nuisance_quality_sensitivity\", \"description\": \"A diagnostic condition comparing weaker and stronger nuisance learners to test sensitivity of each estimator to nuisance estimation error.\"}], \"nuisance_models\": [{\"name\": \"propensity_model\", \"candidates\": [\"logistic_regression\", \"random_forest_classifier\", \"gradient_boosting_classifier\"]}, {\"name\": \"outcome_model\", \"candidates\": [\"linear_regression\", \"random_forest_regressor\", \"gradient_boosting_regressor\"]}], \"metrics\": [{\"name\": \"bias\", \"direction\": \"minimize_abs\", \"description\": \"Mean estimated ATE minus true ATE across simulation repetitions.\"}, {\"name\": \"rmse\", \"direction\": \"minimize\", \"description\": \"Root mean squared error of ATE estimates.\"}, {\"name\": \"coverage\", \"direction\": \"target_0.95\", \"description\": \"Empirical coverage of nominal 95% confidence intervals.\"}, {\"name\": \"interval_width\", \"direction\": \"minimize_given_coverage\", \"description\": \"Average confidence interval width.\"}, {\"name\": \"estimate_std\", \"direction\": \"diagnostic\", \"description\": \"Monte Carlo standard deviation of ATE estimates.\"}], \"simulation_settings\": {\"sample_sizes\": [500, 1000, 3000], \"monte_carlo_repetitions\": 500, \"crossfit_folds\": 2, \"seeds\": [0, 1, 2, 3, 4]}, \"data_generating_process\": {\"covariates\": \"X ~ multivariate normal or uniform with nonlinear transformations\", \"treatment\": \"A ~ Bernoulli(sigmoid nonlinear function of X)\", \"outcome\": \"Y = tau * A + nonlinear function of X + noise\", \"true_ate\": 1.0}, \"expected_artifacts\": [{\"path\": \"src/experiment.py\", \"description\": \"Executable simulation code implementing all ATE estimators.\"}, {\"path\": \"results/metrics.json\", \"description\": \"Machine-readable bias, RMSE, coverage, and interval width by estimator and sample size.\"}, {\"path\": \"results/figures/\", \"description\": \"Plots or tables summarizing estimator bias, RMSE, coverage, interval width, and cross-fitting effects.\"}, {\"path\": \"report/paper.md\", \"description\": \"Paper-style report explaining the causal estimand, DGP, estimators, uncertainty method, results, limitations, and per-hypothesis conclusions.\"}, {\"path\": \"README.md\", \"description\": \"Setup and run instructions for reproducing the full experiment from a clean checkout.\"}], \"dependencies\": {\"python\": [\"numpy\", \"pandas\", \"scipy\", \"scikit-learn\", \"matplotlib\"]}, \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 300}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3/h4 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_ate_metrics_table\", \"type\": \"artifact\", \"description\": \"A machine-readable metrics artifact (e.g. results/metrics.json) exists reporting ATE estimate, bias (or absolute bias), and RMSE broken down by estimator and sample size, with confidence-interval coverage or width where applicable.\", \"must_pass\": true}, {\"id\": \"req_bias_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain numeric (non-null, finite) bias or absolute-bias values for at least the naive difference-in-means estimator and the doubly robust / cross-fitted DML estimator, evaluated against the known true ATE, so H1 and H2 can be evaluated quantitatively.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3/h4 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific bias / RMSE / coverage values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_crossfit_implemented\", \"type\": \"artifact\", \"description\": \"The DML condition implements sample splitting or K-fold cross-fitting so nuisance models are trained out-of-fold relative to the observations used in the orthogonal score; a non-cross-fitted AIPW comparison is also present so H3 can be evaluated.\", \"must_pass\": true}, {\"id\": \"req_estimand_writeup\", \"type\": \"discussion\", \"description\": \"The report clearly distinguishes the finite-dimensional causal estimand, the infinite-dimensional nuisance functions, the orthogonal score, and the evaluation metrics — avoiding confusion between prediction performance and ATE estimation quality. Nice-to-have, not blocking.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names the Monte Carlo repetition count, cross-fitting fold count, and at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"s02-root\", \"requirements\": \"A credible semiparametric simulation study evaluating how double machine learning / orthogonalized AIPW estimates a finite-dimensional average treatment effect while using flexible estimators for infinite-dimensional nuisance functions under nonlinear confounding. The submission should implement multiple estimators, simulate observational data with known true ATE, evaluate bias, RMSE, confidence interval behavior, and sensitivity to nuisance estimation quality, and tie the numeric findings to the hypotheses.\", \"judging_note\": \"Score on causal and semiparametric substance rather than exact package choices. A correct hand-written AIPW / DML implementation should receive full credit even if it does not use a specialized causal inference library. Partial credit should reward clear separation of the finite-dimensional estimand from infinite-dimensional nuisance functions, correct use of orthogonal scores, nuisance estimation, cross-fitting, and honest uncertainty reporting.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"s02-code\", \"requirements\": \"The code implements a meaningful comparison of ATE estimators under simulated confounding with known ground truth.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"s02-code-dgp\", \"requirements\": \"The submission implements a simulated observational data-generating process with covariates, confounded treatment assignment, outcome generation, known true average treatment effect, and explicit true or approximate nuisance functions such as propensity scores and outcome regressions.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"s02-code-baselines\", \"requirements\": \"The submission implements simple baseline estimators such as difference-in-means, regression adjustment, and/or inverse propensity weighting so that DML is compared against meaningful alternatives.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s02-code-aipw\", \"requirements\": \"The submission implements a doubly robust or Neyman-orthogonal AIPW-style estimator using estimated propensity and outcome nuisance functions, and explains why the score targets the finite-dimensional ATE while treating those nuisances as flexible or infinite-dimensional.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s02-code-crossfit\", \"requirements\": \"The DML condition uses sample splitting or K-fold cross-fitting so nuisance models are trained out-of-fold relative to the observations used in the orthogonal score.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s02-code-nuisance-models\", \"requirements\": \"The nuisance functions are estimated with reasonable models for the simulated setting, such as logistic regression, random forests, gradient boosting, or other justified supervised learning methods, with at least one comparison that changes nuisance-model flexibility or quality.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"s02-exec\", \"requirements\": \"Execution produces ATE estimation metrics adequate to evaluate bias, precision, and interval behavior.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"s02-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact, such as results/metrics.json, containing numeric ATE estimates, bias or absolute bias, RMSE, and preferably confidence interval coverage or interval width by estimator and sample size.\", \"weight\": 17.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s02-exec-repetitions\", \"requirements\": \"Reported metrics are aggregated over multiple simulation repetitions or random seeds, with some dispersion reporting such as standard deviation, standard error, confidence interval, or quantiles.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s02-exec-sample-sizes\", \"requirements\": \"The experiment evaluates at least one nontrivial sample size and receives more credit for multiple sample sizes showing finite-sample behavior.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s02-exec-uncertainty\", \"requirements\": \"The submission computes uncertainty estimates, such as influence-function standard errors, bootstrap standard errors, or Monte Carlo confidence intervals, sufficient to discuss coverage or reliability.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"s02-paper\", \"requirements\": \"The final paper or report addresses the semiparametric hypotheses with quantitative evidence and a clear causal-inference narrative about finite-dimensional targets and infinite-dimensional nuisance functions.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"s02-result-h1\", \"requirements\": \"The submission evaluates whether the naive difference-in-means estimator is biased under confounded treatment assignment and supports the conclusion using the known true ATE.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s02-result-h2\", \"requirements\": \"The submission compares the doubly robust / orthogonalized estimator against simpler plug-in regression or IPW estimators and states whether DML reduces absolute bias or RMSE under nonlinear nuisance functions.\", \"weight\": 17.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s02-result-h3\", \"requirements\": \"The submission compares cross-fitted and non-cross-fitted versions of the doubly robust estimator, or otherwise clearly analyzes the contribution of cross-fitting to bias, RMSE, or stability.\", \"weight\": 15.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s02-result-h4\", \"requirements\": \"The submission evaluates whether Neyman-orthogonal scores make the ATE estimate less sensitive to nuisance-function estimation error than non-orthogonal plug-in estimators, using weaker versus stronger nuisance learners or another explicit nuisance-quality diagnostic.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s02-result-estimand\", \"requirements\": \"The writeup clearly distinguishes the finite-dimensional causal estimand, infinite-dimensional nuisance functions, orthogonal score or estimator, and evaluation metrics, avoiding confusion between prediction performance and ATE estimation quality.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s02-result-writeup\", \"requirements\": \"The README or writeup describes the data-generating process, estimators, nuisance models, cross-fitting procedure, semiparametric target-vs-nuisance relationship, key numeric results, and per-hypothesis outcomes with appropriate caveats on sample size, simulation repetitions, and model misspecification.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/statistics/manifests/S02.yaml", "rubric_file": "tasks/statistics/rubrics/S02.json"} +{"id": "S03", "domain": "statistics", "title": "Reliability of LLM-assisted statistical model selection", "topic": "Reliability of LLM-assisted statistical model selection under assumption violations", "domains": ["statistics", "model-selection", "llm-evaluation"], "arxiv_id": null, "venue": "ARC-Bench Statistics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 300, "synthesis": "Large language models are increasingly used to assist with statistical data\nanalysis, including model selection, test choice, and interpretation. However,\nstatistical model selection requires matching assumptions to data-generating\nconditions, not merely producing plausible-sounding analysis text. An LLM or\nLLM-like rule-based assistant may recommend inappropriate tests when data are\nskewed, heteroskedastic, non-independent, or affected by outliers.\n\nA credible study of this topic creates a controlled benchmark of small\nstatistical-analysis tasks with known ground truth. The system should compare\nmodel-selection recommendations against an oracle or rule-based reference\nacross simulated datasets. The research question is: *can an LLM-assisted\nstatistical workflow choose appropriate statistical procedures under\nassumption violations, and how often do its recommendations lead to invalid\ninference?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"A simple prompt-only LLM-style recommendation system is more likely to choose standard parametric tests even when assumptions are violated.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Adding computed diagnostic statistics, such as skewness, variance ratio, normality tests, and sample-size information, improves statistical procedure selection accuracy.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Incorrect procedure selection increases false positive rate or reduces coverage under assumption violations such as heteroskedasticity, skew, or dependence.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Can LLM-assisted statistical model selection reliably choose appropriate procedures when assumptions are violated?\", \"task_type\": {\"name\": \"statistical_test_selection\", \"description\": \"Given dataset summaries and analysis goals, choose an appropriate statistical test or model.\"}, \"conditions\": [{\"name\": \"text_only_recommender\", \"description\": \"Procedure selection using only natural-language task description and variable metadata.\"}, {\"name\": \"diagnostics_augmented_recommender\", \"description\": \"Procedure selection using natural-language description plus computed diagnostics.\"}, {\"name\": \"oracle_rule_based_selector\", \"description\": \"Reference selector using known data-generating process or explicit rules.\"}, {\"name\": \"always_parametric_baseline\", \"description\": \"Baseline that always chooses common parametric procedures such as t-test, ANOVA, or OLS.\"}], \"statistical_tasks\": [{\"name\": \"two_sample_mean_comparison\", \"candidate_methods\": [\"student_t_test\", \"welch_t_test\", \"mann_whitney_u\", \"permutation_test\"]}, {\"name\": \"correlation_analysis\", \"candidate_methods\": [\"pearson_correlation\", \"spearman_correlation\", \"robust_regression\"]}, {\"name\": \"linear_effect_estimation\", \"candidate_methods\": [\"ordinary_least_squares\", \"heteroskedasticity_robust_ols\", \"rank_based_method\", \"permutation_inference\"]}], \"data_generating_conditions\": [{\"name\": \"normal_equal_variance\", \"description\": \"Parametric assumptions approximately hold.\"}, {\"name\": \"normal_unequal_variance\", \"description\": \"Heteroskedastic two-sample setting.\"}, {\"name\": \"skewed_lognormal\", \"description\": \"Strongly skewed outcome distribution.\"}, {\"name\": \"outlier_contaminated\", \"description\": \"Small fraction of extreme observations.\"}, {\"name\": \"nonlinear_monotone_relationship\", \"description\": \"Correlation exists but is nonlinear or rank-based.\"}], \"metrics\": [{\"name\": \"selection_accuracy\", \"direction\": \"maximize\", \"description\": \"Fraction of tasks where selected method matches oracle-acceptable method set.\"}, {\"name\": \"false_positive_rate\", \"direction\": \"target_nominal\", \"description\": \"Empirical Type I error under null simulations.\"}, {\"name\": \"coverage\", \"direction\": \"target_0.95\", \"description\": \"Empirical confidence interval coverage when interval estimates are produced.\"}, {\"name\": \"assumption_violation_detection_rate\", \"direction\": \"maximize\", \"description\": \"Fraction of cases where the system correctly identifies relevant assumption violations.\"}, {\"name\": \"explanation_grounding_score\", \"direction\": \"maximize\", \"description\": \"Whether the written explanation cites the correct diagnostic evidence rather than generic statistical advice.\"}], \"implementation_note\": \"If external LLM API access is unavailable, the submission may simulate an\\nLLM-assisted workflow using templated recommendations, local heuristics, or\\nstored model outputs. The benchmark should focus on statistical validity of\\nrecommendations and downstream inference, not on proprietary model access.\\n\", \"simulation_settings\": {\"sample_sizes\": [30, 100, 500], \"monte_carlo_repetitions\": 500, \"seeds\": [0, 1, 2, 3, 4]}, \"expected_artifacts\": [{\"path\": \"src/generate_tasks.py\", \"description\": \"Code for generating synthetic statistical-analysis tasks.\"}, {\"path\": \"src/evaluate_selectors.py\", \"description\": \"Code for evaluating selectors and downstream inference.\"}, {\"path\": \"results/metrics.json\", \"description\": \"Machine-readable selection accuracy, false positive rate, coverage, and diagnostic-detection metrics.\"}, {\"path\": \"results/selector_decisions.csv\", \"description\": \"Machine-readable selector decisions with task metadata, diagnostics, oracle-acceptable methods, and selected procedures.\"}, {\"path\": \"results/figures/\", \"description\": \"Plots or tables comparing selectors across task types, data-generating conditions, and assumption violations.\"}, {\"path\": \"report/paper.md\", \"description\": \"Paper-style report explaining task generation, selector designs, oracle rules, diagnostic evidence, results, limitations, and per-hypothesis conclusions.\"}, {\"path\": \"README.md\", \"description\": \"Setup and run instructions for reproducing the full benchmark from a clean checkout.\"}], \"dependencies\": {\"python\": [\"numpy\", \"pandas\", \"scipy\", \"scikit-learn\", \"statsmodels\", \"matplotlib\"]}, \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 300}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_selector_metrics_table\", \"type\": \"artifact\", \"description\": \"A machine-readable metrics artifact (e.g. results/metrics.json) exists reporting selection accuracy and false-positive rate (or Type I error) broken down by selector and data-generating condition, plus a results/selector_decisions.csv with per-task selector decisions and oracle-acceptable method labels.\", \"must_pass\": true}, {\"id\": \"req_selection_accuracy_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain numeric (non-null, finite) selection accuracy values in [0, 1] for at least the text-only recommender and the diagnostics-augmented recommender, evaluated over more than one assumption-violation condition, so H1 and H2 can be evaluated.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific accuracy / false-positive-rate / coverage values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_oracle_and_selectors\", \"type\": \"artifact\", \"description\": \"The submission implements or simulates all four selectors — text-only recommender, diagnostics-augmented recommender, oracle / rule-based reference selector, and an always-parametric baseline — and runs the selected procedures to record downstream inference (p-values, coverage, or Type I error).\", \"must_pass\": true}, {\"id\": \"req_method_validity_writeup\", \"type\": \"discussion\", \"description\": \"The report clearly distinguishes the analysis goal, data-generating condition, oracle-acceptable method set, selected method, diagnostic evidence, and downstream inference metric. Nice-to-have, not blocking.\", \"must_pass\": false}, {\"id\": \"req_llm_access_disclosed\", \"type\": \"discussion\", \"description\": \"The report states whether an external LLM API was used or whether a templated / heuristic / stored-output recommender stood in for it. Required for honest reporting but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"s03-root\", \"requirements\": \"A credible controlled simulation study evaluating whether LLM-assisted or LLM-like statistical model selection chooses appropriate procedures under assumption violations. The submission should generate statistical-analysis tasks with known reference decisions, compare text-only and diagnostics-augmented recommenders against an oracle or rule-based selector, evaluate downstream inference validity, and connect the numeric findings to the three hypotheses.\", \"judging_note\": \"Score on statistical validity, reproducible task generation, appropriate reference decisions, and honest discussion of downstream inference. Do not require access to an external LLM API; a templated, heuristic, local, or stored-output recommender can receive full credit if it supports the benchmark question. Penalize generic recommendation text that is not tied to diagnostics or data-generating conditions.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"s03-code\", \"requirements\": \"The code implements a controlled benchmark for statistical procedure selection under several data-generating conditions and selector designs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"s03-code-task-generator\", \"requirements\": \"The submission generates multiple statistical-analysis tasks, including two-sample mean comparison, correlation analysis, and linear effect estimation, with known null or alternative settings and fixed random seeds.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"s03-code-assumption-conditions\", \"requirements\": \"The generated tasks cover meaningful assumption regimes, including approximately normal equal-variance data and at least three violation regimes such as heteroskedasticity, skewness, outliers, nonlinear monotone relationships, or dependence.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"s03-code-selectors\", \"requirements\": \"The submission implements or simulates the required selectors: a text-only recommender, a diagnostics-augmented recommender, an oracle or rule-based reference selector, and a simple parametric baseline.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s03-code-diagnostics\", \"requirements\": \"The diagnostics-augmented condition computes relevant diagnostic information, such as skewness, variance ratio, normality checks, outlier indicators, sample size, monotonicity, or heteroskedasticity evidence, and makes those diagnostics available to the selector.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s03-code-downstream-inference\", \"requirements\": \"The code runs the selected statistical procedures or models and records downstream quantities such as p-values, confidence intervals, Type I error, coverage, or effect estimates where applicable.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"s03-exec\", \"requirements\": \"Execution produces machine-readable selector and inference metrics adequate to evaluate statistical reliability.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"s03-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact, such as results/metrics.json, containing selection accuracy, false positive rate or Type I error, confidence interval coverage where applicable, assumption-violation detection rate, and explanation grounding by selector and data condition.\", \"weight\": 17.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s03-exec-repetitions\", \"requirements\": \"Reported metrics are aggregated over multiple simulation repetitions, seeds, task instances, or sample sizes, with enough repetition to make selector differences meaningful.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s03-exec-task-coverage\", \"requirements\": \"The executed benchmark includes more than one task type and more than one assumption-violation condition, rather than evaluating a single isolated example.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s03-exec-artifacts\", \"requirements\": \"Execution artifacts include selector decisions, oracle-acceptable method labels, diagnostic summaries, and at least one plot or table comparing selectors across assumption regimes.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"s03-paper\", \"requirements\": \"The final paper or report addresses the three model-selection reliability hypotheses with quantitative evidence and a clear statistical narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"s03-paper-h1\", \"requirements\": \"The report evaluates whether the text-only recommender over-selects standard parametric procedures under assumption violations and supports the conclusion with selector-decision metrics.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s03-paper-h2\", \"requirements\": \"The report compares diagnostics-augmented and text-only selection and states whether computed diagnostics improve procedure selection accuracy or assumption-violation detection.\", \"weight\": 15.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s03-paper-h3\", \"requirements\": \"The report analyzes whether incorrect procedure selection leads to invalid inference, such as inflated false positive rates, poor coverage, or misleading effect estimates under specific violations.\", \"weight\": 15.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s03-paper-method-validity\", \"requirements\": \"The writeup clearly distinguishes the analysis goal, data-generating condition, oracle-acceptable method set, selected method, diagnostic evidence, and downstream inference metric.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s03-paper-writeup\", \"requirements\": \"The README or paper describes task generation, selector designs, diagnostic features, oracle rules, key numeric results, limitations, and per-hypothesis outcomes with appropriate caveats on simulation scope and any use or non-use of external LLM APIs.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 14, "manifest_file": "tasks/statistics/manifests/S03.yaml", "rubric_file": "tasks/statistics/rubrics/S03.json"} diff --git a/data/biology.jsonl b/data/biology.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d7a355f11fa80eb9a121e1be715a0d9f6462989b --- /dev/null +++ b/data/biology.jsonl @@ -0,0 +1,7 @@ +{"id": "B01", "domain": "biology", "title": "E. coli succinate-production strain optimization via constraint-based knockout screen", "topic": "E. coli succinate-production strain optimization via constraint-based knockout screen on iAF1260", "domains": ["systems-biology", "metabolic-engineering", "constraint-based-modelling"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1800, "synthesis": "Succinate is a top-12 platform chemical and a recurring case study in\nmetabolic engineering. The standard genome-scale model E. coli iAF1260\n(2382 reactions, 1668 metabolites, 1261 genes) provides a well-validated\ntestbed for in-silico strain design: from a fixed glucose-minimal medium\none can predict (a) the maximum aerobic biomass growth rate, (b) the\nmaximum theoretical succinate secretion flux once growth is held at a feasible\nfraction of optimum, (c) the set of single- and double-gene knockouts\nthat decouple biomass from succinate secretion enough to push the strain\ntoward a high-secretion non-growth-coupled phenotype.\n\nA credible CPU-scale study of this topic (a) loads iAF1260 from BIGG and\nvalidates aerobic biomass > 0.7 1/h on glucose-minimal medium, (b) runs\nparsimonious FBA (pFBA) at the wild-type optimum and reports the central\ncarbon flux distribution, (c) sweeps the production envelope (biomass vs\nsuccinate secretion) using flux variability analysis (FVA) on the\nsuccinate exchange across a grid of biomass-fraction-of-optimum values,\n(d) runs a single-gene knockout screen (≤100 candidate genes drawn from\ncentral carbon metabolism, fermentation, and TCA cycle) and ranks each KO\nby post-KO succinate secretion flux at 50% of WT growth, (e) reports the top three\nKO strains with mechanistic explanation tied to the network topology.\n\nThe research question is: *which single-gene knockouts of central carbon\nmetabolism in E. coli iAF1260 produce the largest predicted succinate\nsecretion flux while preserving at least 50% of wild-type biomass growth, and what\nis the mechanistic role of each in shifting flux toward succinate?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Wild-type aerobic E. coli iAF1260 on glucose-minimal medium predicts a biomass growth rate within ±10% of 0.736 1/h (the published BIGG iAF1260 reference value).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The biomass-vs-succinate production envelope, computed via FVA at biomass fractions from 0.1 to 1.0 of WT optimum, is monotonically non-increasing in succinate as biomass approaches the WT optimum (i.e., succinate is growth-competing under aerobic glucose conditions).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"At least 3 of the top-5 single-gene knockouts identified by the screen as maximizing succinate secretion flux at 50% WT-growth disrupt canonical competing by-product or respiration pathways described in the metabolic-engineering literature (e.g., pyruvate-formate-lyase pflB, lactate dehydrogenase ldhA, alcohol dehydrogenase adhE, acetate kinase ackA, phosphate acetyltransferase pta, or their isozymes), or are accompanied by a mechanistic explanation for why the knockout redirects flux toward succinate under the chosen medium.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which single-gene knockouts of central carbon metabolism in E. coli iAF1260 produce the largest predicted succinate secretion flux while preserving at least 50% of wild-type biomass growth, and what is the mechanistic role of each in shifting flux toward succinate?\", \"conditions\": [{\"name\": \"wt_aerobic_glucose\", \"description\": \"Wild-type iAF1260 on glucose-minimal medium with O2 unconstrained. Reference condition.\"}, {\"name\": \"wt_aerobic_glucose_pfba\", \"description\": \"Same as wt_aerobic_glucose but with pFBA (parsimonious FBA) for unique flux distribution.\"}, {\"name\": \"production_envelope_succinate\", \"description\": \"FVA on succinate exchange across biomass-fraction-of-optimum grid {0.1, 0.2, ..., 1.0}.\"}, {\"name\": \"single_ko_screen_central_carbon\", \"description\": \"Single-gene knockouts on a curated set of ≤100 central-carbon / fermentation / TCA genes (glycolysis, PPP, TCA, anaplerotic, fermentation by-products); for each, FBA at fixed biomass = 0.5 × WT optimum; record succinate exchange flux.\"}], \"baselines\": [\"wt_aerobic_glucose (no perturbation) is the no-engineering baseline\", \"Published BIGG iAF1260 reference growth rate (0.736 1/h on glucose-minimal aerobic) — the model-validity gate\"], \"metrics\": [{\"name\": \"wt_growth_rate\", \"direction\": \"match_reference\", \"description\": \"Wild-type biomass exchange flux (1/h) on glucose-minimal aerobic; reference value 0.736.\"}, {\"name\": \"max_succinate_flux_mmol_gDW_h_at_zero_growth\", \"direction\": \"maximize\", \"description\": \"Succinate exchange flux (mmol/gDW/h) at biomass = 0 (theoretical maximum).\"}, {\"name\": \"succinate_flux_mmol_gDW_h_at_half_growth\", \"direction\": \"maximize\", \"description\": \"Succinate exchange flux at biomass = 0.5 × WT optimum, wild-type strain.\"}, {\"name\": \"best_ko_succinate_flux_mmol_gDW_h\", \"direction\": \"maximize\", \"description\": \"Maximum succinate exchange flux over the single-KO screen at biomass = 0.5 × WT optimum.\"}], \"datasets\": [{\"name\": \"iAF1260\", \"source\": \"BIGG database (http://bigg.ucsd.edu/models/iAF1260) via cobra.io.load_model('iAF1260')\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1800}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metrics (object of numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_wt_growth_value\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain a numeric value for `wt_growth_observed_1_per_h` (or an equivalent wt_growth_*_1_per_h key) that is non-null and finite. The wild-type growth rate must be reported, otherwise H1 cannot be evaluated.\", \"must_pass\": true}, {\"id\": \"req_top5_ko\", \"type\": \"discussion\", \"description\": \"results.json structured_results MUST contain a list of the top 5 ranked single-gene knockouts with at least {gene_id, gene_name, ko_succinate_flux_mmol_gDW_h, is_canonical} fields each. The list need not include the textbook canonical genes — but it MUST exist and identify at least 5 candidates.\", \"must_pass\": true}, {\"id\": \"req_envelope_figure\", \"type\": \"artifact\", \"description\": \"A production-envelope figure (biomass-vs-succinate, or analogous) must exist under figures/ or analysis/ in PDF or PNG format with axes labeled and units shown. Either a single multi-curve figure or per-condition figures both qualify.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"Top-3 KO targets are discussed mechanistically — naming the pathway each KO disables and a one-sentence rationale for why the disruption redirects flux to succinate. Acceptable inside summary, structured_results, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and any RNG seeds used (e.g. for sampling) are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B01\", \"requirements\": \"A credible constraint-based experiment on E. coli iAF1260 that (a) loads the BIGG model and validates wild-type growth, (b) computes a flux distribution under the standard objective, (c) characterises the biomass-vs-succinate production envelope, (d) runs a single-gene knockout screen targeted at central carbon metabolism, and (e) ties the top-ranked knockouts to the network mechanism. Partial but well-motivated evidence deserves partial credit; rigid wording or naming should not penalize a substantively correct experiment.\", \"judging_note\": \"Score on biological substance and quantitative correctness, not exact threshold satisfaction. Reproducing canonical FBA numbers (WT growth ~0.7, well-known knockouts like pflB/ldhA/adhE/ackA-pta) is strong evidence; novel-but-coherent design choices are acceptable.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b01-code\", \"requirements\": \"The constraint-based pipeline is implemented with COBRApy and a BIGG genome-scale model.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b01-code-model\", \"requirements\": \"The submission loads E. coli iAF1260 (or a comparable validated genome-scale metabolic model) from BIGG via COBRApy, with the medium constraints (glucose uptake, O2 availability) explicitly set and the biomass objective explicitly named.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b01-code-fba\", \"requirements\": \"FBA and pFBA are invoked correctly to obtain wild-type growth and a unique flux distribution; output flux table covers exchange + central-carbon reactions at minimum.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b01-code-envelope\", \"requirements\": \"The biomass-vs-succinate production envelope is computed by sweeping a fraction-of-optimum biomass constraint and running FVA (or two-step FBA) on the succinate exchange at each grid point.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b01-code-koscreen\", \"requirements\": \"The single-gene knockout screen iterates over a curated central-carbon / fermentation / TCA gene set (≤100), applies model.genes..knock_out() (or equivalent reaction-bound deletion), and records succinate flux at fixed biomass = 0.5 × WT optimum.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b01-exec\", \"requirements\": \"Execution produces the FBA / FVA / KO numbers needed to evaluate the hypotheses without crashing.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b01-exec-validity\", \"requirements\": \"Solver returns optimal status for the wild-type FBA and at least 90% of the KO conditions; no negative biomass or non-physical fluxes (succinate secretion negative under aerobic glucose, etc.) appear without explanation.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b01-exec-physics\", \"requirements\": \"Mass balance, charge balance, and biomass-positive checks pass for the loaded model; this is the biology-validity gate analogous to physics-validity (gauge invariance, unitarity) in the physics rubric.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b01-exec-results\", \"requirements\": \"A machine-readable results artifact (results.json or simulations/*.csv) records WT growth rate, the production-envelope grid (biomass fraction → succinate secretion flux in mmol/gDW/h), and a per-KO succinate-flux table. If yield is reported, define it separately as succinate secretion flux divided by glucose uptake flux.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b01-results\", \"requirements\": \"The results analysis addresses the three hypotheses with quantitative evidence and a clear narrative tied to network mechanism.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b01-result-h1-quant\", \"requirements\": \"Quantitative test of H1: predicted WT aerobic growth rate is reported and compared to the published BIGG reference 0.736 1/h. Score 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b01-result-h2-quant\", \"requirements\": \"Quantitative test of H2: production envelope shows succinate is growth-competing — succinate flux is non-increasing as biomass approaches WT optimum. Score 100% if monotonic over ≥8 of 10 grid points, 67% if ≥6 of 10, 33% if ≥4 of 10.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b01-result-h3-quant\", \"requirements\": \"Quantitative test of H3: among the top-5 KO ranked by succinate secretion flux at 0.5 × WT growth, count how many disrupt canonical competing by-product or respiration pathways for succinate-overproduction logic, such as {pflB, ldhA, adhE, ackA, pta, acetate/ethanol/lactate branch competitors, oxygen-respiration competitors}, or include a specific mechanistic explanation for redirecting flux toward succinate under the chosen medium. Score 100% if ≥3 of 5 meet this canonical-or-mechanistic criterion, 67% if 2 of 5, 33% if 1 of 5, 0% otherwise.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b01-result-figure\", \"requirements\": \"At least one publication-quality figure is produced — typically the production envelope (biomass on x, succinate secretion flux on y, with WT and best KO overlaid) and/or the KO essentiality / flux heatmap. Axes labeled with units (1/h, mmol/gDW/h), legend present.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b01-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, names the top knockouts with their gene/enzyme identity, and gives a one-paragraph mechanistic interpretation (which competing pathways the KOs disable, why this redirects flux to succinate).\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b01-repro\", \"requirements\": \"Reproducibility: the model, run cards, and seeds are checked into artifacts so a fresh clone can rerun and obtain matching numbers.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b01-repro-model\", \"requirements\": \"The loaded GSMM is either persisted (models/iAF1260.json) or pinned by version + BIGG URL in the writeup; a downstream user can locate the exact model object used.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b01-repro-runcards\", \"requirements\": \"The medium definition (exchange bounds), objective function, FVA fraction-of-optimum, and KO gene list are saved to a config / params file (not only inline in code).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b01-repro-seeds\", \"requirements\": \"Solver settings (LP backend name + version, tolerance) are recorded; if any sampling step is used, the RNG seed is logged. A second run reproduces the central WT growth rate to within solver tolerance.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B01.yaml", "rubric_file": "tasks/biology/rubrics/B01.json"} +{"id": "B02", "domain": "biology", "title": "E. coli acetate overflow metabolism phase map under glucose and oxygen limitation", "topic": "E. coli acetate overflow metabolism phase map under glucose and oxygen limitation", "domains": ["systems-biology", "constraint-based-modelling", "phase-plane-analysis"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1800, "synthesis": "Overflow acetate secretion in E. coli is a canonical constraint-based\nmodelling phenotype. A credible CPU-scale study loads a validated E. coli\ngenome-scale model, fixes a glucose-minimal medium, sweeps glucose and oxygen\nuptake bounds, computes growth and major secretion products, and identifies\nthe transition from respiratory growth to overflow/fermentative by-product\nsecretion.\n\nThe research question is: under which glucose and oxygen uptake regimes does\nE. coli switch from primarily respiratory growth to acetate-secreting overflow\nmetabolism, and how robust is that regime boundary across FBA and pFBA?", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The selected E. coli BIGG model grows on aerobic glucose-minimal medium with a positive biomass objective and no infeasible solver status.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"A two-dimensional glucose-vs-oxygen phase plane contains distinct regimes with high oxygen/low acetate and low oxygen/high acetate secretion.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"pFBA reduces total internal flux while preserving the same growth-rate regime boundaries to within a small tolerance.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Under which glucose and oxygen uptake regimes does E. coli switch from respiratory growth to acetate overflow?\", \"conditions\": [{\"name\": \"aerobic_glucose_reference\", \"description\": \"Wild-type E. coli iJO1366 or iAF1260 on glucose-minimal medium with oxygen available.\"}, {\"name\": \"glucose_oxygen_phase_plane\", \"description\": \"Grid over glucose uptake and oxygen uptake bounds; compute biomass, acetate, ethanol, lactate, succinate, and CO2 exchange fluxes.\"}, {\"name\": \"pfba_phase_plane_check\", \"description\": \"Run pFBA on the same grid or a representative subset and compare growth plus secretion regimes.\"}], \"baselines\": [\"High-oxygen glucose condition is the respiratory baseline.\", \"Zero-oxygen glucose condition is the fermentative baseline.\"], \"metrics\": [{\"name\": \"growth_rate_1_per_h\", \"direction\": \"maximize\", \"description\": \"Biomass objective flux for each nutrient grid point.\"}, {\"name\": \"acetate_flux_mmol_gDW_h\", \"direction\": \"characterize\", \"description\": \"Acetate exchange secretion flux.\"}, {\"name\": \"overflow_boundary_o2_bound\", \"direction\": \"estimate\", \"description\": \"Approximate oxygen bound below which acetate secretion exceeds a chosen threshold.\"}, {\"name\": \"pfba_total_flux_norm\", \"direction\": \"minimize\", \"description\": \"Total absolute flux under pFBA.\"}], \"datasets\": [{\"name\": \"E. coli GSMM\", \"source\": \"BIGG iJO1366 or iAF1260 via cobra.io.load_model\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1800}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A results.json file exists with primary_metric, metrics, hypotheses, summary, and structured_results.\", \"must_pass\": true}, {\"id\": \"req_phase_plane_table\", \"type\": \"artifact\", \"description\": \"A machine-readable phase-plane table exists with glucose_bound, oxygen_bound, growth_rate, and acetate_flux columns.\", \"must_pass\": true}, {\"id\": \"req_phase_plane_figure\", \"type\": \"artifact\", \"description\": \"A PNG or PDF heatmap/phase-plane figure exists with glucose and oxygen axes and units.\", \"must_pass\": true}, {\"id\": \"req_aerobic_ref_growth\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain a numeric value for `aerobic_glucose_growth_rate_1_per_h` (or an equivalent aerobic_growth_*_1_per_h key) that is non-null, finite, and positive. The aerobic reference growth rate must be reported, otherwise H1 cannot be evaluated.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\\n\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The overflow boundary and its mechanistic basis are discussed — why E. coli switches to acetate-secreting overflow at the identified nutrient threshold. Acceptable inside summary, phase_plane_interpretation, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B02\", \"requirements\": \"A credible constraint-based E. coli acetate-overflow phase-plane study that loads a validated BIGG model, sets glucose and oxygen medium bounds, sweeps a two-dimensional nutrient grid, reports growth and by-product secretion, and interprets the acetate overflow boundary.\", \"judging_note\": \"Score biological substance and quantitative reproducibility over exact thresholds. iJO1366, iAF1260, or a comparable curated E. coli GSMM are acceptable.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b02-code\", \"requirements\": \"The COBRApy implementation defines the model, medium, phase-plane grid, FBA/pFBA runs, and secretion extraction.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b02-code-model\", \"requirements\": \"Loads a validated E. coli BIGG model, explicitly sets glucose and oxygen exchange bounds, and names the biomass objective.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b02-code-grid\", \"requirements\": \"Constructs a two-dimensional glucose-by-oxygen grid with at least 8 values per axis and restores model state between conditions.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b02-code-products\", \"requirements\": \"Extracts biomass plus acetate, ethanol, lactate, succinate, CO2, and O2/glucose exchange fluxes where present.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b02-code-pfba\", \"requirements\": \"Runs pFBA on the full grid or a justified representative subset and records total absolute flux.\", \"weight\": 4, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b02-exec\", \"requirements\": \"Execution produces stable phase-plane artifacts without infeasible or missing-condition failures dominating the study.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b02-exec-status\", \"requirements\": \"At least 90% of grid points solve to optimal or biologically expected zero-growth status with explicit handling.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b02-exec-physics\", \"requirements\": \"Mass-balance check passes for the loaded model; no non-physical secretion fluxes appear under aerobic conditions without explicit justification (e.g. acetate secretion is zero under high-oxygen/low-glucose conditions in the reference FBA); biomass flux is positive under aerobic glucose reference. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b02-exec-artifacts\", \"requirements\": \"Writes results.json and a CSV/TSV phase-plane table with nutrient bounds, growth, and secretion fluxes.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b02-results\", \"requirements\": \"The analysis identifies and explains acetate-overflow regimes with figures and quantitative criteria.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b02-result-h1\", \"requirements\": \"Reports reference aerobic glucose growth and confirms model feasibility and positive biomass production. Score 100% if a positive finite biomass flux is reported for aerobic glucose with optimal solver status; 67% if growth is positive but solver status is not verified; 33% if only feasibility is stated without a numeric growth value; 0% otherwise.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b02-result-h2\", \"requirements\": \"Quantifies an acetate secretion boundary or threshold across oxygen/glucose regimes and distinguishes respiratory vs overflow conditions. Score 100% if an acetate-overflow boundary is quantified (O2 or glucose uptake threshold stated) with ≥2 distinct secretion regimes visible in the phase-plane table; 67% if overflow is identified without a quantified boundary; 33% if only one secretion regime is described; 0% otherwise.\", \"weight\": 12, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b02-result-h3\", \"requirements\": \"Compares FBA and pFBA growth/secretion regimes and explains where they agree or differ. Score 100% if FBA and pFBA growth rates are numerically compared and total flux norms are reported for both; 67% if only growth rates are compared without flux-norm comparison; 33% if pFBA was run but not compared to FBA; 0% otherwise.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b02-result-figure\", \"requirements\": \"Produces at least one labelled heatmap or contour plot for growth and acetate secretion with units.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b02-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, characterises the acetate-overflow regime boundary with quantitative support, and gives a one-paragraph mechanistic interpretation of why E. coli shifts from respiratory to acetate-secreting overflow metabolism at the identified nutrient threshold.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b02-repro\", \"requirements\": \"The model ID, medium, grid values, solver, and thresholds are recorded for rerun reproducibility.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b02-repro-model\", \"requirements\": \"The loaded E. coli GSMM is either persisted (models/.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object used.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b02-repro-config\", \"requirements\": \"Saves model ID, objective reaction, nutrient grid, and secretion threshold in config or results metadata.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b02-repro-solver\", \"requirements\": \"Records solver backend, tolerance, COBRApy version, and rerun consistency for the aerobic reference point.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B02.yaml", "rubric_file": "tasks/biology/rubrics/B02.json"} +{"id": "B03", "domain": "biology", "title": "Anaerobic E. coli lactate-overproduction knockout screen", "topic": "Anaerobic E. coli lactate-overproduction knockout screen", "domains": ["systems-biology", "metabolic-engineering", "constraint-based-modelling"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1800, "synthesis": "Lactate production under anaerobic glucose conditions is a tractable\nmetabolic-engineering task for constraint-based modelling. A credible study\nloads an E. coli BIGG model, validates anaerobic growth, computes WT lactate\nsecretion, screens central-carbon and fermentation knockouts, and ranks\ninterventions that increase lactate secretion while preserving growth.", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The model supports positive anaerobic glucose growth after oxygen uptake is closed.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The lactate production envelope shows a tradeoff between biomass growth and maximum lactate secretion.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Top lactate-improving knockouts disable competing ethanol, formate, acetate, or succinate by-product routes, or have a specific mechanistic explanation.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which single-gene knockouts increase anaerobic lactate secretion in E. coli while preserving at least 30-50% of WT growth?\", \"conditions\": [{\"name\": \"wt_anaerobic_glucose\", \"description\": \"E. coli iJO1366 or iAF1260 on glucose-minimal medium with oxygen uptake closed.\"}, {\"name\": \"lactate_production_envelope\", \"description\": \"Sweep biomass fraction and optimize lactate secretion.\"}, {\"name\": \"single_ko_screen_fermentation\", \"description\": \"Screen central-carbon and fermentation genes; rank by lactate flux at fixed growth fraction.\"}], \"baselines\": [\"Wild-type anaerobic glucose lactate secretion.\", \"No-knockout lactate envelope.\"], \"metrics\": [{\"name\": \"anaerobic_wt_growth_1_per_h\", \"direction\": \"maximize\", \"description\": \"WT biomass flux with oxygen uptake set to zero.\"}, {\"name\": \"lactate_flux_mmol_gDW_h\", \"direction\": \"maximize\", \"description\": \"Lactate exchange secretion flux.\"}, {\"name\": \"lactate_yield_per_glucose\", \"direction\": \"maximize\", \"description\": \"Lactate secretion divided by glucose uptake.\"}, {\"name\": \"growth_fraction\", \"direction\": \"threshold\", \"description\": \"KO biomass relative to WT anaerobic growth.\"}], \"datasets\": [{\"name\": \"E. coli GSMM\", \"source\": \"BIGG iJO1366 or iAF1260 via cobra.io.load_model\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1800}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"results.json contains primary_metric, metrics, hypotheses, summary, and top KO structured results.\", \"must_pass\": true}, {\"id\": \"req_anaerobic_medium\", \"type\": \"numeric\", \"description\": \"Oxygen uptake is explicitly closed and anaerobic WT growth is reported.\", \"must_pass\": true}, {\"id\": \"req_top5_ko\", \"type\": \"discussion\", \"description\": \"Top 5 lactate-ranked knockouts are reported with gene_id, gene_name, growth_fraction, lactate_flux, and mechanism/canonical flag.\", \"must_pass\": true}, {\"id\": \"req_envelope_figure\", \"type\": \"artifact\", \"description\": \"A biomass-vs-lactate production envelope figure exists with labelled axes and units.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\\n\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"Top-3 lactate-improving KO targets are discussed mechanistically — naming the pathway each KO disables and a one-sentence rationale for why the disruption redirects anaerobic flux to lactate. Acceptable inside summary, structured_results, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B03\", \"requirements\": \"A credible anaerobic E. coli lactate-overproduction study using COBRApy that validates anaerobic growth, computes a lactate production envelope, screens a focused central-carbon/fermentation knockout set, and explains top candidates mechanistically.\", \"judging_note\": \"Accept iJO1366, iAF1260, or comparable curated E. coli models. Score coherent anaerobic setup and mechanistic KO interpretation over exact gene names.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b03-code\", \"requirements\": \"COBRApy code implements anaerobic medium, lactate objective/envelope, and knockout screening.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b03-code-medium\", \"requirements\": \"Loads an E. coli BIGG model, sets glucose uptake, closes oxygen uptake, and names biomass and lactate exchange reactions.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b03-code-envelope\", \"requirements\": \"Computes biomass-vs-lactate production envelope by constraining biomass fractions and optimizing or FVA-bounding lactate secretion.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b03-code-ko\", \"requirements\": \"Screens a focused set of no more than 100 central-carbon and fermentation genes with model state restored between knockouts.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b03-code-yield\", \"requirements\": \"Computes lactate yield as lactate secretion divided by absolute glucose uptake, separate from flux.\", \"weight\": 4, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b03-exec\", \"requirements\": \"Execution produces valid anaerobic WT, envelope, and KO tables.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b03-exec-validity\", \"requirements\": \"WT anaerobic FBA is optimal with positive biomass and at least 85% of KO simulations solve or are explicitly labelled infeasible/lethal.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b03-exec-physics\", \"requirements\": \"Mass-balance check passes for the loaded anaerobic model; no aerobic by-products (O2 uptake or CO2 via respiration) appear under strictly anaerobic conditions without explanation; biomass flux is positive under anaerobic glucose reference. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b03-exec-artifacts\", \"requirements\": \"Writes results.json plus machine-readable envelope and KO-ranking tables.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b03-results\", \"requirements\": \"Results quantify lactate production, growth tradeoffs, and KO mechanisms.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b03-result-h1\", \"requirements\": \"Reports anaerobic WT growth and confirms oxygen uptake is zero or bounded closed. Score 100% if positive anaerobic WT biomass flux is reported with O2 exchange confirmed ≤0 mmol/gDW/h; 67% if growth is positive but O2 status is not explicitly confirmed; 33% if anaerobic feasibility is stated without a numeric growth value; 0% otherwise.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b03-result-h2\", \"requirements\": \"Shows lactate secretion changes across biomass fractions and identifies whether production is growth-coupled, competing, or partially coupled. Score 100% if lactate secretion flux is non-decreasing as biomass fraction decreases across ≥8 of 10 envelope grid points; 67% if non-decreasing over ≥6 of 10 points; 33% if ≥4 of 10 points; 0% otherwise.\", \"weight\": 10, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b03-result-h3\", \"requirements\": \"Top 5 KO list includes growth fraction, lactate flux/yield, and pathway-level rationale for at least top 3 targets. Score 100% if ≥3 of the top-5 KOs disrupt canonical competing by-product pathways (acetate, ethanol, formate, or succinate routes) or include a specific mechanistic explanation; 67% if 2 of 5 meet this criterion; 33% if 1 of 5; 0% otherwise.\", \"weight\": 12, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b03-result-figure\", \"requirements\": \"Produces a labelled lactate envelope or KO ranking figure with units.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b03-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, names the top lactate-improving knockouts with their gene/enzyme identity, and gives a one-paragraph mechanistic interpretation (which competing pathways the KOs disable, why this redirects anaerobic flux to lactate).\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b03-repro\", \"requirements\": \"Medium, model, gene set, solver, and thresholds are reproducibly recorded.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b03-repro-model\", \"requirements\": \"The loaded E. coli GSMM is either persisted (models/.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object and reproduce the anaerobic medium setup.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b03-repro-runcard\", \"requirements\": \"Saves anaerobic medium bounds, biomass threshold, KO gene set, and lactate reaction ID.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b03-repro-solver\", \"requirements\": \"Records COBRApy/solver versions and confirms rerun consistency for WT growth.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B03.yaml", "rubric_file": "tasks/biology/rubrics/B03.json"} +{"id": "B04", "domain": "biology", "title": "S. cerevisiae ethanol yield under oxygen limitation and carbon-source changes", "topic": "S. cerevisiae ethanol yield under oxygen limitation and carbon-source changes", "domains": ["systems-biology", "metabolic-engineering", "constraint-based-modelling"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1800, "synthesis": "Yeast ethanol fermentation is a natural extension beyond E. coli while still\nstaying inside BIGG/COBRApy constraint-based modelling. A credible study\nloads iMM904 or a comparable yeast GSMM, validates growth on glucose, sweeps\noxygen availability, compares glucose and alternative carbon sources, and\nquantifies ethanol secretion and yield.", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The yeast model grows on glucose-minimal medium and produces a feasible FBA solution.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Ethanol secretion/yield increases as oxygen uptake is restricted relative to fully aerobic growth.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Carbon-source swaps produce distinct growth and ethanol-yield profiles, with glucose supporting stronger fermentation than at least one alternative carbon source.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do oxygen limitation and carbon source alter predicted ethanol secretion and yield in S. cerevisiae?\", \"conditions\": [{\"name\": \"yeast_glucose_reference\", \"description\": \"S. cerevisiae iMM904 or comparable model on glucose-minimal medium.\"}, {\"name\": \"oxygen_sweep\", \"description\": \"Sweep oxygen uptake from anaerobic/low oxygen to aerobic while glucose uptake is fixed.\"}, {\"name\": \"carbon_source_swap\", \"description\": \"Compare glucose, fructose, galactose, glycerol, and acetate when supported by the model.\"}], \"baselines\": [\"Aerobic glucose condition.\", \"Anaerobic or oxygen-limited glucose condition.\"], \"metrics\": [{\"name\": \"growth_rate_1_per_h\", \"direction\": \"maximize\", \"description\": \"Biomass flux.\"}, {\"name\": \"ethanol_flux_mmol_gDW_h\", \"direction\": \"maximize\", \"description\": \"Ethanol exchange secretion flux.\"}, {\"name\": \"ethanol_yield_per_carbon_uptake\", \"direction\": \"maximize\", \"description\": \"Ethanol secretion divided by substrate uptake.\"}], \"datasets\": [{\"name\": \"S. cerevisiae iMM904\", \"source\": \"BIGG iMM904 via cobra.io.load_model if available, or documented local SBML/JSON yeast model.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1800}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"results.json contains yeast model ID, metrics, hypotheses, and structured oxygen/carbon-source results.\", \"must_pass\": true}, {\"id\": \"req_oxygen_sweep\", \"type\": \"artifact\", \"description\": \"A machine-readable oxygen-sweep table exists with oxygen bound, growth, ethanol flux, and yield.\", \"must_pass\": true}, {\"id\": \"req_carbon_source_table\", \"type\": \"artifact\", \"description\": \"A carbon-source comparison table exists, including unsupported sources marked explicitly.\", \"must_pass\": true}, {\"id\": \"req_figure\", \"type\": \"artifact\", \"description\": \"At least one oxygen-vs-ethanol or carbon-source yield figure exists with units.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\\n\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The ethanol-yield response to oxygen and carbon source is discussed mechanistically — why oxygen restriction increases ethanol yield and how carbon-source metabolism shapes fermentation. Acceptable inside summary, structured_results, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B04\", \"requirements\": \"A credible S. cerevisiae ethanol-yield study that loads a yeast GSMM, validates growth, sweeps oxygen uptake, swaps carbon sources, and reports ethanol secretion/yield with interpretable figures.\", \"judging_note\": \"The main challenge is adapting the pipeline beyond E. coli while remaining within COBRApy/BIGG capabilities. Accept iMM904 or another documented yeast GSMM.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b04-code\", \"requirements\": \"The code implements yeast model loading, medium setup, oxygen sweep, carbon-source swap, and ethanol-yield calculations.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b04-code-model\", \"requirements\": \"Loads iMM904 or comparable yeast model, identifies biomass, glucose, oxygen, and ethanol exchange reactions, and validates positive reference growth.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b04-code-o2\", \"requirements\": \"Sweeps oxygen uptake across at least 8 values while keeping substrate uptake defined and restoring state between runs.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b04-code-carbon\", \"requirements\": \"Compares at least 4 carbon sources where model reactions exist and explicitly records unsupported sources.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b04-code-yield\", \"requirements\": \"Calculates ethanol yield using absolute substrate uptake and separates yield from raw secretion flux.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b04-exec\", \"requirements\": \"Execution produces oxygen-sweep and carbon-source artifacts without crashing.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b04-exec-status\", \"requirements\": \"Reference glucose condition is optimal and at least 80% of supported oxygen/carbon conditions solve or are clearly marked infeasible.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b04-exec-physics\", \"requirements\": \"Mass-balance check passes for the yeast GSMM; no non-physical fluxes appear under aerobic glucose reference (e.g. ethanol secretion should be low under full aerobic conditions); biomass flux is positive under yeast glucose-minimal reference. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b04-exec-artifacts\", \"requirements\": \"Writes results.json, oxygen_sweep.csv, and carbon_source_comparison.csv or equivalent structured outputs.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b04-results\", \"requirements\": \"Results quantify oxygen and substrate effects on ethanol secretion/yield.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b04-result-h1\", \"requirements\": \"Reports yeast reference growth with model ID and medium definition. Score 100% if positive yeast biomass flux and ethanol secretion are both reported under aerobic glucose-minimal medium with optimal solver status and a named model ID; 67% if growth is positive but ethanol is not reported; 33% if feasibility is stated without numeric values; 0% otherwise.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b04-result-h2\", \"requirements\": \"Quantifies how ethanol flux or yield changes as oxygen decreases and identifies the low-oxygen fermentation regime. Score 100% if ethanol flux or yield is non-decreasing as oxygen uptake decreases across ≥6 of 8 oxygen-sweep points; 67% if non-decreasing over ≥4 of 8 points; 33% if the trend is described without a quantitative grid; 0% otherwise.\", \"weight\": 12, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b04-result-h3\", \"requirements\": \"Compares growth and ethanol yield across carbon sources with a biological interpretation. Score 100% if at least 3 carbon sources are compared with distinct numeric ethanol yields and a biological interpretation; 67% if 2 sources are compared with numeric differences; 33% if the comparison is qualitative or only one carbon source produces positive growth; 0% otherwise.\", \"weight\": 10, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b04-result-figure\", \"requirements\": \"Produces labelled figures for oxygen sweep and/or carbon-source yield comparison.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b04-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, names the oxygen regime and carbon source that maximise ethanol yield, and gives a one-paragraph mechanistic interpretation of why oxygen restriction switches yeast from respiratory growth to fermentative ethanol production.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b04-repro\", \"requirements\": \"Model source, medium, oxygen grid, carbon-source reactions, solver, and versions are documented.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b04-repro-model\", \"requirements\": \"The loaded yeast GSMM is either persisted (models/.json) or pinned by BIGG model ID, version, and source URL in the writeup; a downstream user can locate the exact model object used.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b04-repro-config\", \"requirements\": \"Saves reaction IDs, bounds, grid values, and carbon-source list in config or results metadata.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b04-repro-version\", \"requirements\": \"Records COBRApy, solver, model source/version, and rerun consistency for reference growth.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B04.yaml", "rubric_file": "tasks/biology/rubrics/B04.json"} +{"id": "B05", "domain": "biology", "title": "M. tuberculosis condition-specific essentiality and metabolic drug-target prioritisation", "topic": "M. tuberculosis condition-specific essentiality and metabolic drug-target prioritisation", "domains": ["systems-biology", "drug-target-prioritisation", "constraint-based-modelling"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 2400, "synthesis": "Constraint-based essentiality analysis can prioritise metabolic drug-target\nhypotheses in pathogen models. A credible study loads an M. tuberculosis GSMM\nsuch as iNJ661 when available, validates biomass production under a documented\nmedium, performs single-gene and/or single-reaction deletion, and ranks\nessential targets by growth impact and subsystem interpretability.", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The selected M. tuberculosis model produces positive biomass under the documented reference medium.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Single-gene or single-reaction deletion identifies a non-empty set of essential metabolic targets under the reference condition.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Prioritised targets are enriched in interpretable core metabolic subsystems such as cell-wall precursor, cofactor, lipid, energy, or amino-acid metabolism.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which condition-specific metabolic genes or reactions are predicted essential in M. tuberculosis and are plausible drug-target hypotheses?\", \"conditions\": [{\"name\": \"mtb_reference_medium\", \"description\": \"M. tuberculosis iNJ661 or comparable model under a documented reference medium.\"}, {\"name\": \"single_gene_deletion\", \"description\": \"Single-gene deletion screen when GPR rules are available.\"}, {\"name\": \"single_reaction_deletion\", \"description\": \"Single-reaction deletion screen as a fallback or complementary target set.\"}], \"baselines\": [\"Wild-type reference growth.\", \"Nonessential deletion growth distribution.\"], \"metrics\": [{\"name\": \"wt_growth_rate_1_per_h\", \"direction\": \"maximize\", \"description\": \"Reference biomass flux.\"}, {\"name\": \"growth_fraction_after_deletion\", \"direction\": \"minimize\", \"description\": \"Deletion biomass divided by WT biomass.\"}, {\"name\": \"essential_target_count\", \"direction\": \"characterize\", \"description\": \"Count of genes/reactions below essentiality threshold.\"}, {\"name\": \"subsystem_enrichment\", \"direction\": \"characterize\", \"description\": \"Subsystem distribution among essential targets.\"}], \"datasets\": [{\"name\": \"M. tuberculosis GSMM\", \"source\": \"BIGG iNJ661 or documented local SBML/JSON model.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 2400}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"results.json contains model ID, WT growth, essential target counts, top targets, and hypothesis verdicts.\", \"must_pass\": true}, {\"id\": \"req_deletion_table\", \"type\": \"artifact\", \"description\": \"A gene or reaction deletion table exists with target ID, status, growth, growth_fraction, and essential flag.\", \"must_pass\": true}, {\"id\": \"req_top_targets\", \"type\": \"discussion\", \"description\": \"Top 10 prioritised essential targets are reported with subsystem/pathway and rationale.\", \"must_pass\": true}, {\"id\": \"req_figure\", \"type\": \"artifact\", \"description\": \"At least one essentiality distribution or subsystem-enrichment figure exists.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\\n\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"Top-3 essential target hypotheses are framed mechanistically — naming the metabolic subsystem each target belongs to and why its disruption is predicted lethal under the reference medium. Claims must be framed as computational hypotheses, not validated drugs. Acceptable inside summary, structured_results, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B05\", \"requirements\": \"A credible M. tuberculosis essentiality study using COBRApy that validates a pathogen GSMM, runs single-gene and/or single-reaction deletions, identifies essential targets, and prioritises interpretable drug-target hypotheses.\", \"judging_note\": \"Accept gene deletions, reaction deletions, or both depending on model GPR support. Penalize unsupported biological claims more than absence of external drug databases.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b05-code\", \"requirements\": \"The code loads the pathogen model, validates medium, runs deletion screens, and annotates essential targets.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b05-code-model\", \"requirements\": \"Loads iNJ661 or a documented M. tuberculosis GSMM, defines medium/objective, and validates positive WT biomass.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b05-code-deletion\", \"requirements\": \"Runs single-gene deletion when GPR rules are usable, or single-reaction deletion as an explicit fallback/complement.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b05-code-threshold\", \"requirements\": \"Defines essentiality threshold such as growth <5% or <10% of WT and applies it consistently.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b05-code-annotation\", \"requirements\": \"Extracts subsystem, reaction name, gene name, GPR, or other model-native annotations for top targets.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b05-exec\", \"requirements\": \"Deletion runs complete with interpretable status handling and structured outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b05-exec-status\", \"requirements\": \"WT reference condition is optimal and at least 85% of deletion simulations solve or are explicitly marked infeasible/lethal.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b05-exec-physics\", \"requirements\": \"Mass-balance check passes for the loaded pathogen GSMM; WT biomass is positive under the documented medium; deletion table contains no negative growth values; model GPR rules produce logically consistent essential gene predictions. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b05-exec-artifacts\", \"requirements\": \"Writes results.json plus deletion table with growth, growth_fraction, status, and essential flag.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b05-results\", \"requirements\": \"Results identify essential targets and provide conservative biological interpretation.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b05-result-h1\", \"requirements\": \"Reports WT growth, medium, model ID, and validation caveats. Score 100% if positive WT biomass flux is reported under the documented medium with optimal solver status and model ID named; 67% if growth is positive but medium is not explicitly documented; 33% if feasibility is stated without a numeric value; 0% otherwise.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b05-result-h2\", \"requirements\": \"Reports essential target count and distribution of growth fractions after deletion. Score 100% if ≥5 essential genes/reactions are identified with growth fractions below the stated threshold and the deletion table covers ≥50 candidates; 67% if essential targets are identified but the candidate set covers fewer than 50 entries; 33% if essential targets are named without a deletion table; 0% otherwise.\", \"weight\": 10, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b05-result-h3\", \"requirements\": \"Top 10 targets include subsystem/pathway and one-sentence rationale; claims are framed as modelling hypotheses, not validated drugs. Score 100% if ≥3 of the top-10 prioritised targets belong to named core metabolic subsystems (cell-wall precursor, cofactor, lipid, energy, or amino-acid biosynthesis) with at least a one-sentence pathway rationale; 67% if 2 of 10 with rationale; 33% if 1 of 10 with rationale; 0% otherwise.\", \"weight\": 12, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b05-result-figure\", \"requirements\": \"Produces a labelled essentiality histogram, target ranking plot, or subsystem enrichment chart.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b05-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, names the top prioritised essential targets with subsystem/enzyme identity, and gives a one-paragraph mechanistic rationale for why each target's disruption is lethal. Claims are explicitly framed as computational hypotheses requiring wet-lab validation.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b05-repro\", \"requirements\": \"Model, medium, deletion type, threshold, solver, and annotations are reproducibly recorded.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b05-repro-model\", \"requirements\": \"The loaded M. tuberculosis GSMM is either persisted (models/.json) or pinned by BIGG model ID, version, or local file hash in the writeup; a downstream user can locate the exact model object used.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b05-repro-config\", \"requirements\": \"Saves model source, medium bounds, objective, deletion mode, and essentiality threshold.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b05-repro-version\", \"requirements\": \"Records COBRApy/solver versions and confirms rerun consistency for WT growth and essential count.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B05.yaml", "rubric_file": "tasks/biology/rubrics/B05.json"} +{"id": "B06", "domain": "biology", "title": "E. coli carbon-source robustness and condition-dependent essential genes", "topic": "E. coli carbon-source robustness and condition-dependent essential genes", "domains": ["systems-biology", "essentiality-analysis", "constraint-based-modelling"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 2400, "synthesis": "Carbon-source swaps are a direct mfa-agent capability and produce interpretable\ncondition-dependent phenotypes. A credible study loads an E. coli GSMM, defines\na base minimal medium, tests multiple single-carbon sources, runs essentiality\nanalysis on a focused gene set under each feasible source, and identifies genes\nwhose essentiality changes with carbon source.", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The model predicts positive growth on glucose and at least two additional supported carbon sources.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Growth rates and secretion profiles differ substantially across carbon sources.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"A focused central-carbon gene set contains condition-dependent essential genes whose deletion effects vary by carbon source.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which E. coli metabolic vulnerabilities are carbon-source dependent across glucose, glycerol, acetate, succinate, and fructose-like conditions?\", \"conditions\": [{\"name\": \"carbon_source_panel\", \"description\": \"Close background carbon uptake, open one carbon source at a time, and run FBA/pFBA.\"}, {\"name\": \"focused_gene_essentiality_by_carbon\", \"description\": \"For feasible carbon sources, delete a focused set of central-carbon genes and record growth fractions.\"}], \"baselines\": [\"Glucose-minimal medium.\", \"Wild-type growth for each carbon source.\"], \"metrics\": [{\"name\": \"growth_rate_by_carbon\", \"direction\": \"characterize\", \"description\": \"WT biomass flux per carbon source.\"}, {\"name\": \"major_secretions_by_carbon\", \"direction\": \"characterize\", \"description\": \"Positive exchange fluxes under pFBA.\"}, {\"name\": \"condition_dependent_essential_count\", \"direction\": \"maximize\", \"description\": \"Genes essential under one source but not another.\"}], \"datasets\": [{\"name\": \"E. coli GSMM\", \"source\": \"BIGG iJO1366 or iAF1260 via cobra.io.load_model\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 2400}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"results.json contains carbon-source growth table summary, essentiality counts, hypothesis verdicts, and summary.\", \"must_pass\": true}, {\"id\": \"req_carbon_table\", \"type\": \"artifact\", \"description\": \"A carbon-source comparison table exists with source, exchange reaction, status, growth, and secretion profile fields.\", \"must_pass\": true}, {\"id\": \"req_essentiality_matrix\", \"type\": \"artifact\", \"description\": \"A gene-by-carbon essentiality/growth-fraction matrix exists for feasible carbon sources.\", \"must_pass\": true}, {\"id\": \"req_heatmap\", \"type\": \"artifact\", \"description\": \"A heatmap or clustered plot of condition-dependent gene essentiality exists with labelled axes.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\\n\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"Top-3 condition-dependent essential genes are explained mechanistically — why each gene is essential under one carbon source but not another, referencing the metabolic pathway context. Acceptable inside summary, structured_results, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B06\", \"requirements\": \"A credible E. coli carbon-source robustness study that loads a validated GSMM, swaps carbon sources, reports growth/secretion profiles, and identifies condition-dependent essential genes from a focused gene set.\", \"judging_note\": \"Score explicit medium handling and condition-dependent interpretation. It is acceptable if some carbon sources are unsupported, provided they are recorded and not silently dropped.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b06-code\", \"requirements\": \"The code implements carbon-source medium swaps, FBA/pFBA, and focused essentiality comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b06-code-model\", \"requirements\": \"Loads E. coli BIGG model, identifies biomass and candidate carbon exchange reactions, and defines a minimal medium policy.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b06-code-swap\", \"requirements\": \"Closes background carbon uptake and opens one carbon source at a time with documented uptake bounds.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"b06-code-secretion\", \"requirements\": \"Runs FBA/pFBA per source and records major positive exchange fluxes.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b06-code-essentiality\", \"requirements\": \"Runs focused single-gene deletions for feasible carbon sources and computes growth fractions relative to each source-specific WT.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b06-exec\", \"requirements\": \"Execution produces carbon-source and essentiality artifacts.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b06-exec-validity\", \"requirements\": \"Glucose reference is optimal and at least two non-glucose sources are either feasible or clearly marked unsupported/infeasible.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b06-exec-physics\", \"requirements\": \"Mass-balance check passes for the E. coli GSMM under each tested carbon source; no carbon source produces negative biomass or non-physical secretion without explicit handling; model state is restored between carbon-source conditions. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b06-exec-artifacts\", \"requirements\": \"Writes results.json, carbon-source table, and gene-by-condition growth-fraction matrix.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b06-results\", \"requirements\": \"Results compare carbon sources and identify condition-dependent vulnerabilities.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b06-result-h1\", \"requirements\": \"Reports feasible carbon-source count and growth rates, including unsupported-source handling. Score 100% if ≥3 carbon sources including glucose support positive biomass growth with explicit exchange bounds documented; 67% if 2 sources support positive growth; 33% if only glucose is feasible with explicit confirmation that other sources are infeasible or unsupported; 0% otherwise.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b06-result-h2\", \"requirements\": \"Compares secretion profiles and growth rates across sources with quantitative differences. Score 100% if growth rates differ by >10% between at least 2 feasible carbon sources AND major secretion fluxes differ qualitatively across sources; 67% if growth rate differences are reported without secretion-profile comparison; 33% if differences are noted qualitatively without quantification; 0% otherwise.\", \"weight\": 9, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b06-result-h3\", \"requirements\": \"Identifies genes with large growth-fraction changes across carbon sources and explains pathway context for top hits. Score 100% if ≥3 genes with condition-dependent essentiality changes (essential under one carbon source but not another) are identified with pathway context; 67% if 2 genes are identified; 33% if 1 gene is identified with pathway context; 0% otherwise.\", \"weight\": 12, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b06-result-figure\", \"requirements\": \"Produces labelled bar plots and/or heatmaps for growth, secretion, and condition-dependent essentiality.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b06-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, names supported and unsupported carbon sources, identifies condition-dependent essential genes, and gives a one-paragraph mechanistic interpretation linking carbon-source specific metabolism to the observed vulnerability changes.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b06-repro\", \"requirements\": \"Carbon-source definitions, gene set, thresholds, solver, and model are documented.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b06-repro-model\", \"requirements\": \"The loaded E. coli GSMM is either persisted (models/.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object and reproduce each carbon-source medium setup.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b06-repro-runcard\", \"requirements\": \"Saves carbon exchange IDs, uptake bounds, oxygen setting, gene list, and essentiality threshold.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b06-repro-version\", \"requirements\": \"Records COBRApy/solver versions and rerun consistency for glucose and at least one non-glucose source.\", \"weight\": 5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B06.yaml", "rubric_file": "tasks/biology/rubrics/B06.json"} +{"id": "B07", "domain": "biology", "title": "Reproducible FBA protocol benchmark: FBA vs pFBA vs loopless/FVA on E. coli", "topic": "Reproducible FBA protocol benchmark: FBA vs pFBA vs loopless/FVA on E. coli", "domains": ["systems-biology", "method-benchmarking", "constraint-based-modelling"], "arxiv_id": null, "venue": "ARC-Bench Biology 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1800, "synthesis": "A method benchmark is well matched to mfa-agent because it tests robust use of\nCOBRApy APIs rather than biological novelty alone. A credible study loads a\nvalidated E. coli GSMM, runs standard FBA, pFBA, loopless FBA when available,\nand FVA at a fixed fraction of optimum, then compares growth, flux sparsity,\nruntime, and reaction-level variability.", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"FBA, pFBA, and loopless FBA preserve the same biomass optimum within solver tolerance under the same medium.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"pFBA produces a lower total absolute flux norm and/or fewer active reactions than unconstrained FBA.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"FVA reveals that only a subset of central-carbon reactions are tightly constrained near optimum while others remain variable.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do standard FBA, pFBA, loopless FBA, and FVA differ in flux parsimony and variability while preserving E. coli growth predictions?\", \"conditions\": [{\"name\": \"standard_fba\", \"description\": \"Maximize biomass on aerobic glucose-minimal medium.\"}, {\"name\": \"pfba\", \"description\": \"Maximize biomass then minimize total absolute flux.\"}, {\"name\": \"loopless_fba\", \"description\": \"Run loopless solution when available and compare objective/fluxes.\"}, {\"name\": \"fva_95_percent_growth\", \"description\": \"Run FVA at 95% of optimum and classify rigid vs flexible reactions.\"}], \"baselines\": [\"Standard FBA biomass optimum.\", \"COBRApy solver status and runtime.\"], \"metrics\": [{\"name\": \"growth_rate_1_per_h\", \"direction\": \"match\", \"description\": \"Biomass flux for each method.\"}, {\"name\": \"total_abs_flux\", \"direction\": \"minimize\", \"description\": \"Sum of absolute reaction fluxes.\"}, {\"name\": \"active_reaction_count\", \"direction\": \"minimize\", \"description\": \"Number of reactions with absolute flux above tolerance.\"}, {\"name\": \"fva_width\", \"direction\": \"characterize\", \"description\": \"Maximum minus minimum FVA interval per reaction.\"}], \"datasets\": [{\"name\": \"E. coli GSMM\", \"source\": \"BIGG iJO1366 or iAF1260 via cobra.io.load_model\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1800}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"results.json contains method-level metrics, hypotheses, summary, and solver/version metadata.\", \"must_pass\": true}, {\"id\": \"req_method_table\", \"type\": \"artifact\", \"description\": \"A method-comparison table exists with method, status, growth, total_abs_flux, active_reactions, and runtime.\", \"must_pass\": true}, {\"id\": \"req_fva_table\", \"type\": \"artifact\", \"description\": \"An FVA table exists with reaction ID, minimum, maximum, width, and rigid/flexible classification.\", \"must_pass\": true}, {\"id\": \"req_figures\", \"type\": \"artifact\", \"description\": \"At least one labelled figure compares methods or FVA variability.\", \"must_pass\": true}, {\"id\": \"req_h1_h2_h3_supported_flags\", \"type\": \"discussion\", \"description\": \"Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.\\n\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The protocol comparison is summarised with a method recommendation — which method is most reproducible and why, referencing flux-norm and FVA evidence. Acceptable inside summary, method_comparison, or a separate writeup field.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"B07\", \"requirements\": \"A reproducible FBA-method benchmark on E. coli that compares standard FBA, pFBA, loopless FBA when available, and FVA near optimum using consistent medium, metrics, runtime logging, and interpretable figures.\", \"judging_note\": \"This is a protocol-quality task. Reward robust implementation, structured outputs, and careful handling of unavailable loopless solvers over biological novelty.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"b07-code\", \"requirements\": \"The code implements method comparison with consistent model state and metrics.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b07-code-model\", \"requirements\": \"Loads E. coli BIGG model, sets medium and biomass objective, and validates reference FBA.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b07-code-methods\", \"requirements\": \"Runs standard FBA, pFBA, and loopless FBA or records a justified loopless-unavailable status.\", \"weight\": 7, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b07-code-fva\", \"requirements\": \"Runs FVA at a documented fraction of optimum and computes interval widths and rigid/flexible labels.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"b07-code-metrics\", \"requirements\": \"Computes total absolute flux, active reaction count, objective value, solver status, and runtime for each method.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b07-exec\", \"requirements\": \"Execution produces complete benchmark artifacts and handles optional method failures explicitly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b07-exec-status\", \"requirements\": \"Standard FBA and pFBA solve optimally; loopless and FVA either solve or have explicit, non-crashing failure records.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b07-exec-physics\", \"requirements\": \"Mass-balance check passes for the loaded model under the benchmark medium; FBA, pFBA, and loopless FBA all return non-negative biomass fluxes; no negative flux norms or ill-conditioned solver status appear without explicit logging. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b07-exec-artifacts\", \"requirements\": \"Writes results.json, method comparison table, FVA table, and method flux tables.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b07-results\", \"requirements\": \"Results compare objective preservation, parsimony, and reaction variability.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"b07-result-h1\", \"requirements\": \"Reports objective differences among methods and checks agreement within solver tolerance where methods succeed. Score 100% if FBA and pFBA biomass objectives agree within 1% relative error and any available loopless FBA also agrees within 1%; 67% if FBA and pFBA agree but loopless comparison is missing or failed with explicit explanation; 33% if only qualitative agreement is stated; 0% otherwise.\", \"weight\": 9, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"b07-result-h2\", \"requirements\": \"Quantifies pFBA parsimony using total flux norm and active reaction count compared with standard FBA. Score 100% if pFBA total absolute flux norm is numerically lower than FBA AND active reaction count is lower under pFBA; 67% if flux norm is lower but active reaction count is not compared; 33% if pFBA parsimony is stated qualitatively without numeric comparison; 0% otherwise.\", \"weight\": 10, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b07-result-h3\", \"requirements\": \"Summarizes FVA width distribution and names central-carbon reactions that are rigid or flexible near optimum. Score 100% if FVA interval widths are reported for all model reactions and ≥5 central-carbon reactions are classified as rigid (interval width < 1 mmol/gDW/h) with subsystem identification; 67% if rigid/flexible classification is reported without central-carbon specifics; 33% if FVA results are reported without classification; 0% otherwise.\", \"weight\": 10, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b07-result-figure\", \"requirements\": \"Produces labelled method-comparison and/or FVA-width figures with units and clear legends.\", \"weight\": 8, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"b07-result-writeup\", \"requirements\": \"The README or results writeup discusses each hypothesis outcome, summarises the method-level comparison (parsimony, variability, runtime), and gives a one-paragraph protocol recommendation stating which method is most suitable for reproducible phenotype prediction and why.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"b07-repro\", \"requirements\": \"The benchmark is reproducible with pinned model, medium, tolerance, solver, and code path.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"b07-repro-model\", \"requirements\": \"The loaded E. coli GSMM is either persisted (models/.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object and reproduce the benchmark medium.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b07-repro-config\", \"requirements\": \"Saves model ID, objective, medium bounds, FVA fraction, active-flux tolerance, and method list.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"b07-repro-version\", \"requirements\": \"Records COBRApy, optlang, solver backend/version, and runtime environment.\", \"weight\": 6, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/biology/manifests/B07.yaml", "rubric_file": "tasks/biology/rubrics/B07.json"} diff --git a/data/ml.jsonl b/data/ml.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b984615bc3f8098d633f6695c173c78078b2cd6d --- /dev/null +++ b/data/ml.jsonl @@ -0,0 +1,25 @@ +{"id": "ML01", "domain": "ml", "title": "Dropout regularization strategies on shallow tabular MLPs", "topic": "Comparing dropout regularization strategies (standard, spatial, variational) for preventing overfitting in shallow MLPs on tabular classification benchmarks", "domains": ["machine-learning", "regularization"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 240, "synthesis": "Dropout-style regularization is a cornerstone of neural-net training for\noverfitting control, but its impact on tabular classification with shallow\nMLPs remains subtle. Standard element-wise dropout is the default; spatial\n(feature-wise) dropout treats correlated input features as a block; MC /\nvariational dropout keeps dropout active at test time and averages over\nsamples. For tabular data with small feature counts and mild non-linearity,\nthese variants can produce equal test-set accuracy yet very different\nprobability calibration — a known gotcha that makes accuracy-only\nevaluation misleading.\n\nA credible CPU-scale study of this topic (a) compares at least three\ndropout variants against a no-dropout control on multiple sklearn\nclassification benchmarks, (b) reports both accuracy and calibration\nmetrics (ECE, NLL, Brier), (c) averages over ≥5 seeds with statistical\ntests, and (d) checks for ablation integrity (that different dropout\nvariants actually produce different output distributions, not just the\nsame network). The research question is: *does the choice of dropout\nvariant matter more for calibration than for accuracy on tabular data?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Variational / MC dropout produces lower Expected Calibration Error (ECE) than standard element-wise dropout on at least 2 of 3 tabular classification benchmarks, averaged over ≥5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Test-set accuracy differs by <2 absolute percentage points between dropout variants on datasets where all methods exceed 95% accuracy — i.e. accuracy is non-discriminative and calibration is needed to distinguish methods.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The no-dropout control is strictly worse than at least one dropout variant in ECE on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the choice of dropout variant (standard / spatial / variational) matter more for probability calibration than for test accuracy on tabular classification with shallow MLPs?\", \"conditions\": [{\"name\": \"no_dropout\", \"description\": \"Shallow MLP (2 hidden layers, 64 units, ReLU) trained with no dropout. Control condition.\"}, {\"name\": \"standard_dropout_p30\", \"description\": \"Same MLP with standard element-wise dropout at p=0.3 between hidden layers.\"}, {\"name\": \"standard_dropout_p50\", \"description\": \"Same MLP with standard element-wise dropout at p=0.5.\"}, {\"name\": \"spatial_dropout_p30\", \"description\": \"Same MLP with feature-blockwise dropout at p=0.3 applied to the input layer.\"}, {\"name\": \"mc_dropout_p30_T20\", \"description\": \"Same MLP with standard p=0.3 dropout left active at test time; prediction is the mean of T=20 stochastic forward passes.\"}], \"baselines\": [\"no_dropout is the no-regularization baseline\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Fraction correctly classified on held-out 20% test split, mean over 5 seeds.\"}, {\"name\": \"ece\", \"direction\": \"minimize\", \"description\": \"Expected Calibration Error (15 bins) on the test split, mean over 5 seeds.\"}, {\"name\": \"nll\", \"direction\": \"minimize\", \"description\": \"Mean per-example negative log-likelihood on the test split.\"}, {\"name\": \"brier\", \"direction\": \"minimize\", \"description\": \"Mean Brier score on the test split.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"digits\", \"source\": \"sklearn.datasets.load_digits\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 240}}", "requirements": "", "rubric": "{\"id\": \"ml01-root\", \"requirements\": \"A credible experiment studying whether dropout-variant choice affects calibration more than accuracy on tabular classification: the main dropout variants are implemented, evaluation covers multiple sklearn benchmarks with reasonable seed coverage, and the writeup ties the numeric findings to the three hypotheses in direction and magnitude.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Partial but well-motivated evidence deserves partial credit; rigid wording or name mismatches should not penalize a substantively correct experiment.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml01-code\", \"requirements\": \"The dropout conditions are implemented in a way that allows a meaningful calibration comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml01-code-variants\", \"requirements\": \"The submission implements the main dropout variants relevant to the hypotheses — typically a no-dropout baseline, a standard element-wise dropout at one or more rates, and at least one stochastic-test-time / MC-style dropout variant — as distinct code paths within a shared MLP architecture.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml01-code-mlp\", \"requirements\": \"The model is a shallow tabular MLP (a small number of hidden layers at modest width) trained with a standard supervised classification objective, appropriate for the sklearn benchmarks used.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml01-code-datasets\", \"requirements\": \"The submission loads multiple tabular sklearn datasets (e.g., from {breast_cancer, wine, digits} or comparable benchmarks) and uses a reasonable train/test split.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml01-code-mc-pass\", \"requirements\": \"The MC-dropout-style condition performs several stochastic forward passes at test time and averages the predicted probabilities, rather than collapsing to a single deterministic forward pass.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml01-exec\", \"requirements\": \"Execution produces accuracy and calibration numbers per condition that are adequate to evaluate the hypotheses.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml01-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact (e.g., results/metrics.json or stage-14/experiment_summary.json) with numeric accuracy and a calibration metric such as ECE, covering the implemented conditions on at least one dataset. Other calibration metrics (NLL, Brier) may substitute or supplement ECE.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml01-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (condition, dataset) cell with some form of dispersion reporting (std, stderr, CI, or min/max across seeds). More seeds are better, but a small-but-honest seed count with reported variance is preferable to a single deterministic run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml01-results\", \"requirements\": \"The results analysis addresses the three hypotheses with quantitative evidence and a clear narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml01-result-h1\", \"requirements\": \"The submission compares calibration (ECE or analogous metric) between an MC/variational-style dropout and a standard dropout across the evaluated datasets, and conveys whether MC-style calibration tends to be better — judge whether the evidence is consistent with H1, refutes it, or is inconclusive, based on the reported numbers.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml01-result-h2\", \"requirements\": \"The submission discusses whether accuracy differences among dropout variants are small on datasets where all methods achieve high accuracy — i.e., whether calibration is the discriminative axis rather than accuracy. Exact percentage-point thresholds are not required; a qualitative comparison grounded in the reported numbers suffices.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml01-result-h3\", \"requirements\": \"The submission compares the no-dropout baseline against at least one dropout variant on calibration and conveys whether the baseline is clearly worse, comparable, or better.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml01-result-writeup\", \"requirements\": \"The README or writeup describes the method and setup, presents the key accuracy and calibration numbers, and conveys per-hypothesis outcomes (supported / refuted / inconclusive) with appropriate caveats on seed count, dataset scope, or calibration-metric choice. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML01.yaml", "rubric_file": "tasks/ml/rubrics/ML01.json"} +{"id": "ML02", "domain": "ml", "title": "Bagging vs boosting vs stacking for noisy non-linear regression", "topic": "Evaluating ensemble methods (bagging, boosting, stacking) for regression on synthetic non-linear datasets with varying noise levels", "domains": ["machine-learning", "ensemble-methods"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "rmse", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Ensemble learning is often presented as a broadly reliable way to improve\npredictive performance, but different ensemble families can react very\ndifferently to observation noise in regression. Bagging primarily reduces\nvariance by averaging many weakly correlated models. Boosting is sequential\nand can aggressively fit residuals, which can help on structured non-linear\nsignals but may overfit high-noise targets if regularization is not tuned.\nStacking combines heterogeneous base learners through a meta-model and may\nbenefit from diversity, but it also introduces extra fitting stages that can\nbe sensitive to data size and leakage handling.\n\nA focused CPU-scale benchmark can probe these tradeoffs using synthetic and\nlightweight sklearn regression datasets where non-linearity and noise are\ncontrollable. By varying noise level explicitly in generated data, the study\ncan test whether relative ranking among bagging, boosting, and stacking is\nstable or changes materially as signal-to-noise degrades. This design also\nencourages careful evaluation with repeated random seeds and test-set metrics\nthat capture both average error and fit quality.\n\nA credible implementation should compare at least one representative for each\nfamily (e.g., RandomForestRegressor for bagging, GradientBoostingRegressor\nfor boosting, StackingRegressor with diverse base estimators for stacking),\ninclude a single-model baseline, and report RMSE-centered conclusions. Since\nthis benchmark is intended for fast autonomous execution, datasets and models\nmust remain small enough to run in minutes on one CPU core while still\nproducing clear numeric contrasts across noise regimes.\n\nThe key outcome is not reproducing a known paper result but determining when\neach ensemble strategy is most robust for noisy non-linear regression in a\nconstrained practical setting.\n\n*How does predictive robustness (RMSE) of bagging, boosting, and stacking change as regression noise increases on small non-linear benchmarks?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On the high-noise synthetic dataset (noise >= 25), bagging (RandomForestRegressor) achieves lower mean RMSE than boosting (GradientBoostingRegressor), averaged over at least 5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On at least 2 of 3 evaluated datasets, at least one ensemble method (bagging, boosting, or stacking) improves RMSE by >= 10% relative to the single DecisionTreeRegressor baseline.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"StackingRegressor attains the best (lowest) mean RMSE on at least 1 of 3 datasets while not being worst on more than 1 dataset, averaged over at least 5 seeds.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How does RMSE performance of bagging, boosting, and stacking vary with increasing noise in non-linear regression tasks under CPU-only constraints?\", \"conditions\": [{\"name\": \"decision_tree_baseline\", \"description\": \"Single DecisionTreeRegressor (max_depth tuned over a small grid) as non-ensemble baseline.\"}, {\"name\": \"bagging_random_forest\", \"description\": \"RandomForestRegressor representing bagging-style averaging across trees.\"}, {\"name\": \"boosting_gbrt\", \"description\": \"GradientBoostingRegressor representing sequential boosting on residuals.\"}, {\"name\": \"stacking_heterogeneous\", \"description\": \"StackingRegressor with base learners {kNN regressor, ridge regression, shallow random forest} and a linear meta-regressor.\"}], \"baselines\": [\"decision_tree_baseline is the single-model baseline\", \"bagging_random_forest serves as a strong classical ensemble reference\"], \"metrics\": [{\"name\": \"rmse\", \"direction\": \"minimize\", \"description\": \"Root Mean Squared Error on held-out test split, averaged over seeds.\"}, {\"name\": \"mae\", \"direction\": \"minimize\", \"description\": \"Mean Absolute Error on held-out test split, averaged over seeds.\"}, {\"name\": \"r2\", \"direction\": \"maximize\", \"description\": \"Coefficient of determination on held-out test split, averaged over seeds.\"}], \"datasets\": [{\"name\": \"friedman1_low_noise\", \"source\": \"sklearn.datasets.make_friedman1 (n_samples=1200, n_features=10, noise=1.0)\"}, {\"name\": \"friedman1_high_noise\", \"source\": \"sklearn.datasets.make_friedman1 (n_samples=1200, n_features=10, noise=30.0)\"}, {\"name\": \"diabetes\", \"source\": \"sklearn.datasets.load_diabetes\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml02-root\", \"requirements\": \"A credible experiment studying bagging vs boosting vs stacking for noisy non-linear regression: the main ensemble families are implemented, execution covers multiple regression datasets with reasonable seed coverage, and the analysis addresses H1/H2/H3 using RMSE-centered evidence.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Condition-name variants (e.g., 'RandomForest' vs 'bagging_random_forest') should not be penalized when the underlying method is clearly the intended one.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml02-code\", \"requirements\": \"The regression ensemble conditions and datasets are implemented in a way that supports a fair comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml02-code-conditions\", \"requirements\": \"The submission implements the main ensemble families relevant to the hypotheses — a bagging-style model (e.g., RandomForestRegressor), a boosting-style model (e.g., GradientBoostingRegressor), and a stacking-style model (e.g., StackingRegressor) — alongside a non-ensemble baseline such as a single DecisionTreeRegressor. Equivalent well-motivated choices are acceptable.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml02-code-datasets\", \"requirements\": \"The submission uses multiple regression datasets that allow a noise-robustness comparison — for example, friedman1 at varying noise levels and/or diabetes from sklearn — with a reasonable train/test split.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml02-code-setup\", \"requirements\": \"The pipeline uses a consistent preprocessing and evaluation setup across conditions, with controlled random-state handling and no obvious test-set leakage into training or stacking meta-features.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml02-exec\", \"requirements\": \"Execution logs the core metrics needed to evaluate the hypotheses.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml02-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact containing numeric RMSE for the implemented (dataset, condition) cells, plus at least one complementary error metric (MAE or R²). Complete coverage is preferred; partial coverage should be scored proportional to how much of the hypothesis-relevant grid is populated.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml02-exec-seeds\", \"requirements\": \"Reported dataset-condition results are aggregated over multiple random seeds with some form of dispersion reporting (std, stderr, CI, or min/max). More seeds are better, but an honest small-seed run with reported variance is preferable to a single deterministic run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml02-results\", \"requirements\": \"Results analysis addresses all three hypotheses with quantitative evidence.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml02-result-h1\", \"requirements\": \"The submission compares bagging (e.g., Random Forest) vs boosting (e.g., GBRT) on RMSE in a high-noise regression regime and conveys whether bagging is clearly better, clearly worse, or comparable — judge directionally against H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml02-result-h2\", \"requirements\": \"The submission conveys whether ensembles deliver a meaningful RMSE improvement over the single-tree baseline on most of the evaluated datasets. Exact percentage thresholds are not required; a clear comparison grounded in the reported numbers suffices.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml02-result-h3\", \"requirements\": \"The submission ranks ensemble and baseline conditions by RMSE across the evaluated datasets and discusses whether stacking is competitive (best or near-best on some datasets, not systematically worst) — judge directionally against H3.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml02-result-writeup\", \"requirements\": \"The README or writeup describes the methods and setup, presents the key RMSE/MAE/R² findings, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML02.yaml", "rubric_file": "tasks/ml/rubrics/ML02.json"} +{"id": "ML03", "domain": "ml", "title": "CPU comparison of Nelder-Mead, Powell, and CMA-ES on non-convex functions", "topic": "Comparing gradient-free optimization algorithms (Nelder-Mead, Powell, CMA-ES) for non-convex benchmark functions using only CPU computation", "domains": ["optimization", "numerical-methods"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "primary_metric", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 480, "synthesis": "Gradient-free optimization methods are widely used when derivatives are\nunavailable, unreliable, or expensive to compute. In low-to-medium\ndimensional non-convex landscapes, direct-search methods such as\nNelder-Mead and Powell are frequently used because they are easy to call\nthrough scipy.optimize. Population-based approaches such as CMA-ES can be\nmore robust to local minima and ill-conditioning, but they introduce extra\nhyperparameters and potentially higher per-iteration cost.\n\nA CPU-bounded benchmark can clarify practical trade-offs by evaluating these\nmethods under a fixed function-evaluation budget on standard synthetic\nobjective functions with known global minima. Rather than asking which method\nis universally best, the relevant question is whether one method reaches\nbetter objective values more reliably within the same budget and how much\nruntime overhead that reliability costs.\n\nA credible experiment should evaluate at least three optimization conditions\n(Nelder-Mead, Powell, CMA-ES) across multiple non-convex test functions,\nusing repeated random starts and common stopping/budget rules. Reporting only\nfinal objective value is incomplete; runtime and success-rate-to-threshold\nshould be included to capture both quality and efficiency. Statistical\nsummaries over seeds are required because single runs are high variance.\n\nThe resulting evidence should produce explicit pass/fail verdicts for each\nhypothesis: whether CMA-ES improves best-found objective value, whether\nPowell offers faster wall-clock convergence than CMA-ES at similar budgets,\nand whether Nelder-Mead underperforms on multimodal landscapes. This keeps\nthe study measurable and feasible within a short single-core runtime window.\n\n*Under equal function-evaluation budgets on CPU, which gradient-free optimizer (Nelder-Mead, Powell, CMA-ES) gives the best trade-off between final objective quality, runtime, and success rate on non-convex benchmark functions?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"CMA-ES achieves lower mean best objective value than both Nelder-Mead and Powell on at least 2 of 3 benchmark functions, averaged over >=10 random starts with the same evaluation budget.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Powell has lower median wall-clock runtime than CMA-ES on at least 2 of 3 benchmark functions while finishing within the same function-evaluation cap.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"On the multimodal Rastrigin function, Nelder-Mead attains a lower success rate (fraction of runs reaching f(x) <= 1e-2) than CMA-ES by at least 0.20 absolute.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Under equal evaluation budgets, how do Nelder-Mead, Powell, and CMA-ES compare on objective quality, runtime, and success probability for non-convex synthetic objectives?\", \"conditions\": [{\"name\": \"nelder_mead\", \"description\": \"scipy.optimize.minimize with method='Nelder-Mead', random initial point per run, maxfev budget enforced.\"}, {\"name\": \"powell\", \"description\": \"scipy.optimize.minimize with method='Powell', same initialization protocol and maxfev cap.\"}, {\"name\": \"cma_es\", \"description\": \"CMA-ES implementation (lightweight numpy/scipy variant) with population updates under the same total function-evaluation budget.\"}, {\"name\": \"random_search_baseline\", \"description\": \"Uniform random search within bounded domain using the same number of objective evaluations as other methods.\"}], \"baselines\": [\"random_search_baseline as a budget-matched non-adaptive baseline\"], \"metrics\": [{\"name\": \"primary_metric\", \"direction\": \"minimize\", \"description\": \"Mean best objective value found at termination (lower is better), aggregated over random starts.\"}, {\"name\": \"median_runtime_sec\", \"direction\": \"minimize\", \"description\": \"Median wall-clock runtime per run in seconds for each (method, function).\"}, {\"name\": \"success_rate_eps\", \"direction\": \"maximize\", \"description\": \"Fraction of runs reaching objective value <= 1e-2 by termination.\"}], \"datasets\": [{\"name\": \"rastrigin_10d\", \"source\": \"synthetic numpy implementation of Rastrigin function in 10 dimensions\"}, {\"name\": \"rosenbrock_10d\", \"source\": \"synthetic numpy implementation of Rosenbrock function in 10 dimensions\"}, {\"name\": \"ackley_10d\", \"source\": \"synthetic numpy implementation of Ackley function in 10 dimensions\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 480}}", "requirements": "", "rubric": "{\"id\": \"ml03-root\", \"requirements\": \"A credible experiment comparing Nelder-Mead, Powell, and CMA-ES on non-convex benchmark functions: the optimizers are implemented under matched evaluation budgets, execution covers multiple benchmark functions with repeated random starts, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. If the submission uses different optimizer names or slightly different benchmark functions but addresses the same scientific question, credit it accordingly.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml03-code\", \"requirements\": \"The optimization methods and benchmark setup are implemented in a way that supports a fair comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml03-code-methods\", \"requirements\": \"The submission implements distinct optimizer code paths for a local-simplex method (e.g., Nelder-Mead), a direction-set method (e.g., Powell), and an evolution-strategy method (e.g., CMA-ES or a well-motivated substitute such as a random-search baseline), rather than aliases of a single optimizer.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml03-code-functions\", \"requirements\": \"The submission defines multiple non-convex benchmark functions (e.g., Rastrigin, Rosenbrock, Ackley at a moderate dimension such as 10D) with bounded domains and deterministic objective evaluation.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml03-code-budget\", \"requirements\": \"A shared function-evaluation budget (maxfev or equivalent) is enforced across optimizers so comparisons are budget-matched.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml03-exec\", \"requirements\": \"Execution uses repeated random starts and produces comparable benchmark metrics.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml03-exec-runs\", \"requirements\": \"Execution uses multiple random starts per (optimizer, function) cell across the evaluated benchmark functions. More starts are better for reducing variance, but an honest small-repetition run with reported dispersion is preferable to a single start.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml03-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact with numeric best-objective-value (the primary optimization metric), a wall-clock runtime measure (e.g., median runtime), and a success-rate-style metric (fraction of runs reaching a threshold f(x) ≤ ε) per optimizer and function.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml03-results\", \"requirements\": \"The reported results address all three hypotheses and include a clear narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml03-result-h1\", \"requirements\": \"The submission compares mean best-objective-values across the optimizers for each function and conveys whether CMA-ES (or its stand-in) tends to outperform the local methods on the multimodal functions — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml03-result-h2\", \"requirements\": \"The submission compares runtime between Powell and CMA-ES across the evaluated functions and conveys whether Powell tends to finish meaningfully faster under the matched budget.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml03-result-h3\", \"requirements\": \"The submission compares success-rate on a hard multimodal function (e.g., Rastrigin) between a local method (e.g., Nelder-Mead) and CMA-ES and conveys whether CMA-ES has a clearly higher success rate.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml03-result-writeup\", \"requirements\": \"The README or writeup describes the benchmark setup and metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as budget size, dimensionality, CMA-ES implementation simplifications, and seed variance. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML03.yaml", "rubric_file": "tasks/ml/rubrics/ML03.json"} +{"id": "ML04", "domain": "ml", "title": "Effect of standard, min-max, and robust scaling on KNN classification", "topic": "Analyzing the effect of feature scaling methods (standard, min-max, robust) on k-nearest neighbors classification performance across different data distributions", "domains": ["machine-learning", "data-preprocessing"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 180, "synthesis": "K-nearest neighbors (KNN) is highly sensitive to feature scale because\ndistance calculations implicitly weight dimensions by their numeric ranges.\nIn practice, preprocessing decisions such as StandardScaler, MinMaxScaler,\nor RobustScaler can alter neighborhood structure enough to change\nclassification performance as much as model hyperparameters do. Despite this,\nscaling choice is often treated as a default rather than as an experimental\nfactor.\n\nThe core methodological question is not whether scaling helps versus no\nscaling (it usually does), but whether specific scaling families are better\nmatched to particular data distributions. Standard scaling assumes roughly\nGaussian-like behavior, min-max scaling compresses to bounded ranges and can\nbe strongly affected by extrema, and robust scaling centers/scales by median\nand IQR to reduce outlier influence.\n\nA CPU-friendly, credible benchmark should compare these scalers with a fixed\nKNN classifier across multiple datasets that differ in outlier prevalence and\nmarginal feature distributions, while averaging over multiple train/test\nsplits. Because KNN is lightweight on small-to-medium sklearn datasets, the\nstudy can include both predictive quality and stability metrics without\nexceeding strict runtime constraints.\n\nThe experiment should report test accuracy as the primary outcome, plus\nmacro-F1 and split-to-split variability, and include at least one outlier-\nheavy synthetic dataset to stress robust scaling behavior. The goal is to\nproduce falsifiable conclusions about when scaler choice materially changes\nKNN outcomes.\n\n*How does the choice among standard, min-max, and robust feature scaling change KNN classification performance and stability across datasets with different distribution and outlier characteristics?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"RobustScaler + KNN achieves higher mean test accuracy than StandardScaler + KNN on the outlier-heavy synthetic dataset by at least 0.03 absolute accuracy, averaged over ≥5 random splits.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Across the evaluated datasets, at least one real dataset shows an absolute test-accuracy gap of ≥0.02 between the best and worst of {StandardScaler, MinMaxScaler, RobustScaler}, indicating scaler choice materially affects KNN performance.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"No_scaling baseline is not the top-accuracy condition on at least 2 of 3 datasets when compared against the three scaling methods with the same KNN hyperparameters.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How does scaler choice (standard, min-max, robust) affect KNN classification accuracy and stability across datasets with different feature distributions and outlier profiles?\", \"conditions\": [{\"name\": \"no_scaling_knn\", \"description\": \"KNeighborsClassifier with fixed k and distance metric on raw features; control condition.\"}, {\"name\": \"standard_scaler_knn\", \"description\": \"Pipeline(StandardScaler, KNeighborsClassifier) with the same KNN hyperparameters as control.\"}, {\"name\": \"minmax_scaler_knn\", \"description\": \"Pipeline(MinMaxScaler, KNeighborsClassifier) with identical KNN settings.\"}, {\"name\": \"robust_scaler_knn\", \"description\": \"Pipeline(RobustScaler, KNeighborsClassifier) with identical KNN settings.\"}], \"baselines\": [\"no_scaling_knn is the preprocessing-free baseline\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Mean held-out accuracy over at least 5 random stratified splits per dataset.\"}, {\"name\": \"macro_f1\", \"direction\": \"maximize\", \"description\": \"Macro-averaged F1 on held-out data, averaged over splits.\"}, {\"name\": \"accuracy_std\", \"direction\": \"minimize\", \"description\": \"Standard deviation of test accuracy across random splits as a stability indicator.\"}], \"datasets\": [{\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"synthetic_outlier_classification\", \"source\": \"sklearn.datasets.make_classification with injected feature outliers on a subset of samples\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 180}}", "requirements": "", "rubric": "{\"id\": \"ml04-root\", \"requirements\": \"A credible experiment studying how feature scaling (standard, min-max, robust, or none) affects KNN classification on datasets with different distributions: scaling conditions are implemented consistently, execution covers multiple datasets and random splits, and conclusions address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. If the submission uses alternative but well-motivated scalers or datasets that test the same scientific question, credit it accordingly.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml04-code\", \"requirements\": \"The scaling conditions and KNN setup are implemented in a way that supports a fair comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml04-code-conditions\", \"requirements\": \"The submission implements the relevant scaling conditions — typically no-scaling, standard-scaler, min-max-scaler, and robust-scaler — as distinct code paths sharing the same KNN configuration.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml04-code-datasets\", \"requirements\": \"The submission uses multiple datasets including at least one real sklearn dataset and at least one synthetic/outlier-heavy dataset, with a reasonable train/test split.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml04-code-consistency\", \"requirements\": \"The implementation keeps KNN settings identical across scaling conditions and fits preprocessing only on training data within each split.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml04-exec\", \"requirements\": \"Execution produces benchmark metrics adequate to evaluate the hypotheses.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml04-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact with numeric test accuracy and an additional metric such as macro-F1 for each evaluated (condition, dataset) cell.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml04-exec-splits\", \"requirements\": \"Reported metrics are aggregated over multiple random splits or seeds per (condition, dataset) with some dispersion measure (std or CI). More splits are better, but an honest small-split run with variance reported is preferable to a single split.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml04-results\", \"requirements\": \"The analysis evaluates all three hypotheses and presents interpretable findings.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml04-result-h1\", \"requirements\": \"The submission compares robust-scaling vs standard-scaling + KNN on the outlier-heavy dataset and conveys whether robust-scaling shows a meaningful accuracy advantage. Exact percentage-point thresholds are not required.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml04-result-h2\", \"requirements\": \"The submission discusses whether scaler choice materially affects KNN accuracy on at least one real dataset — i.e., whether the best-vs-worst-scaler gap is non-trivial and of practical significance.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml04-result-h3\", \"requirements\": \"The submission compares the no-scaling baseline against the scaled conditions and conveys whether no-scaling tends to be clearly suboptimal across the evaluated datasets.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml04-result-writeup\", \"requirements\": \"The README or writeup describes setup and datasets, reports the key metric values per condition, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as dataset scope, split count, or KNN-hyperparameter sensitivity. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML04.yaml", "rubric_file": "tasks/ml/rubrics/ML04.json"} +{"id": "ML05", "domain": "ml", "title": "Dimensionality reduction methods for preserving cluster structure in synthetic high-dimensional data", "topic": "Investigating dimensionality reduction techniques (PCA, t-SNE, UMAP) for preserving cluster structure in high-dimensional synthetic datasets", "domains": ["machine-learning", "unsupervised-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "silhouette_score", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 600, "synthesis": "Dimensionality reduction is frequently used before visualization and\nclustering, but different methods optimize different objectives and can\ndistort neighborhood and global geometry in distinct ways. PCA is linear and\nemphasizes variance preservation, while t-SNE and UMAP are nonlinear manifold\nmethods designed to preserve local structure. In practice, users often infer\ncluster quality from 2D embeddings without checking whether separability in\nthe embedding reflects true high-dimensional structure.\n\nA CPU-scale investigation can probe this mismatch by generating synthetic\ndatasets where ground-truth cluster labels are known and controllable. By\nvarying cluster overlap, anisotropy, and manifold geometry (e.g., Gaussian\nblobs, anisotropic transforms, and noisy moons embedded in high dimensions),\none can evaluate whether each reducer preserves cluster structure under\ndifferent regimes instead of relying on a single toy example.\n\nA credible experiment should compare PCA, t-SNE, and UMAP under a common\npipeline: reduce to 2 dimensions, then score structure using metrics such as\nsilhouette score and adjusted Rand index after a fixed clustering backend\n(e.g., KMeans with true k). Runtime should also be tracked because nonlinear\nmethods may yield better structure at substantially higher computational cost,\nwhich matters in constrained environments.\n\nThe goal is not to claim a universally best reducer, but to quantify tradeoffs\nbetween structure preservation and efficiency in settings where intrinsic\ngeometry differs. This produces actionable guidance for method choice based on\ndata regime rather than defaults.\n\n*Which dimensionality reduction method (PCA, t-SNE, or UMAP) best preserves cluster structure across distinct synthetic high-dimensional regimes when evaluated by silhouette score, ARI, and runtime?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On nonlinear manifold data (noisy two-moons embedded into 50D), at least one nonlinear reducer (t-SNE or UMAP) achieves silhouette_score at least 0.10 higher than PCA after 2D reduction and KMeans clustering, averaged over >=5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On linearly separable Gaussian blobs in 50D, PCA achieves silhouette_score within 0.05 of the best nonlinear method (t-SNE or UMAP), averaged over >=5 seeds.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"PCA has the lowest mean wall-clock reduction time and is at least 3x faster than both t-SNE and UMAP on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which reducer best balances cluster-structure preservation and compute cost across linear and nonlinear synthetic high-dimensional datasets?\", \"conditions\": [{\"name\": \"pca_2d\", \"description\": \"PCA with n_components=2 using sklearn.decomposition.PCA.\"}, {\"name\": \"tsne_2d\", \"description\": \"t-SNE with n_components=2, perplexity in [20, 40], learning_rate='auto', init='pca'.\"}, {\"name\": \"umap_2d\", \"description\": \"UMAP-like reducer: use umap.UMAP(n_components=2, n_neighbors in [15, 30], min_dist in [0.0, 0.3]) if available; if unavailable, use sklearn.manifold.SpectralEmbedding(n_components=2) as a documented fallback proxy.\"}, {\"name\": \"identity_no_reduction\", \"description\": \"Baseline that applies KMeans directly in the original high-dimensional space (no dimensionality reduction).\"}], \"baselines\": [\"identity_no_reduction as no-DR clustering baseline\", \"pca_2d as linear DR baseline\"], \"metrics\": [{\"name\": \"silhouette_score\", \"direction\": \"maximize\", \"description\": \"Silhouette score computed on the 2D embedding (or original space for identity baseline) using predicted KMeans labels; primary metric.\"}, {\"name\": \"ari\", \"direction\": \"maximize\", \"description\": \"Adjusted Rand Index between KMeans labels and known synthetic ground-truth labels.\"}, {\"name\": \"fit_transform_time_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock time for dimensionality reduction fit_transform only (seconds).\"}], \"datasets\": [{\"name\": \"gaussian_blobs_50d\", \"source\": \"sklearn.datasets.make_blobs with 4 centers, n_features=50, cluster_std=1.2\"}, {\"name\": \"anisotropic_blobs_50d\", \"source\": \"make_blobs in 10D expanded/projection to 50D then linear anisotropic transform\"}, {\"name\": \"embedded_moons_50d\", \"source\": \"sklearn.datasets.make_moons(noise=0.08) embedded into 50D by random linear projection plus Gaussian noise\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 600}}", "requirements": "", "rubric": "{\"id\": \"ml05-root\", \"requirements\": \"A credible experiment studying how linear (PCA) vs non-linear (t-SNE / UMAP-like) dimensionality reduction preserves cluster structure on synthetic high-dimensional datasets: methods share a consistent evaluation pipeline, execution covers multiple datasets with repeated seeds, and conclusions address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. UMAP may be unavailable in the environment — a documented fallback (e.g., Isomap, LLE) that tests the same scientific question should be credited rather than penalized.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml05-code\", \"requirements\": \"The dimensionality-reduction and clustering conditions are implemented in a way that supports a fair comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml05-code-conditions\", \"requirements\": \"The submission implements the main reduction conditions relevant to the hypotheses — a linear method (PCA), at least one nonlinear method (t-SNE, UMAP, or a documented substitute such as Isomap), and preferably a no-reduction identity baseline — as distinct code paths.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml05-code-data\", \"requirements\": \"The submission generates multiple synthetic high-dimensional datasets that stress different geometries (e.g., isotropic blobs, anisotropic blobs, embedded non-linear manifolds such as moons) with reproducible seeds and retained ground-truth labels.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml05-code-pipeline\", \"requirements\": \"A consistent post-reduction clustering backend (e.g., KMeans with k matching the true cluster count) and a shared metric-computation pipeline are applied uniformly across conditions.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml05-exec\", \"requirements\": \"Execution outputs quantitative cluster-quality and runtime metrics.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml05-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact with numeric cluster-quality scores (silhouette, ARI, or equivalents) and a wall-clock reduction-time measure per (dataset, condition) cell.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml05-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (dataset, condition) with some dispersion measure. More seeds are better, but an honest small-seed run with variance reported is preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml05-results\", \"requirements\": \"Results are analyzed against the hypotheses with interpretable evidence.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml05-result-h1\", \"requirements\": \"The submission compares nonlinear methods vs PCA on the nonlinear-manifold dataset using a cluster-quality metric (silhouette or ARI) and conveys whether the nonlinear methods produce meaningfully better cluster separation — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml05-result-h2\", \"requirements\": \"The submission discusses PCA vs the best nonlinear method on the linearly-separable blobs dataset and conveys whether PCA is competitive (comparable quality) in that regime.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml05-result-h3\", \"requirements\": \"The submission reports runtime rankings across methods and conveys whether PCA is markedly faster than the nonlinear methods on most datasets.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml05-result-writeup\", \"requirements\": \"The README or writeup describes setup, reports the key metric numbers, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as synthetic-only scope, hyperparameter sensitivity, or substitutions for unavailable methods. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML05.yaml", "rubric_file": "tasks/ml/rubrics/ML05.json"} +{"id": "ML06", "domain": "ml", "title": "Adaptive learning-rate schedules for logistic-regression convergence on binary classification", "topic": "Comparing adaptive learning rate schedules (cosine annealing, step decay, exponential decay, warm restarts) for logistic regression convergence on binary classification", "domains": ["optimization", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "convergence_epochs", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Logistic regression is often treated as a solved baseline, yet its practical\ntraining behavior still depends strongly on optimization details. In\nfirst-order methods, the learning-rate schedule can dominate time-to-quality:\na constant step size may converge slowly or oscillate, while adaptive\nschedules can accelerate early progress or stabilize late optimization.\nBecause logistic regression has a smooth convex objective, it offers a clean\nsetting to compare schedules without confounds from deep-network\nnon-convexity.\n\nThis topic studies four schedule families in a unified mini-batch gradient\ndescent implementation: step decay, exponential decay, cosine annealing, and\ncosine warm restarts. The focus is not raw final accuracy alone, but\noptimization efficiency measured by epochs needed to reach a predefined\nvalidation-loss threshold, plus robustness across datasets and random seeds.\nA fixed-learning-rate baseline is required so any claimed speedup is\nattributable to schedule design.\n\nA credible CPU-scale experiment should run on multiple binary classification\ndatasets from sklearn, with feature standardization, identical model\nparameterization, and synchronized stopping criteria. Reporting should include\nconvergence epochs, final validation log loss, and test ROC-AUC so that faster\nconvergence is not achieved at the expense of generalization. Since warm\nrestarts can re-increase the learning rate, trajectory logging is important to\nverify that restart behavior is actually implemented.\n\nThe key decision criterion is whether adaptive schedules reduce optimization\neffort in a consistent, measurable way. The study should conclude with clear\nper-hypothesis verdicts and practical limitations (dataset size, threshold\nchoice, and sensitivity to initial learning rate).\n\n*Do adaptive learning-rate schedules (especially cosine variants) reduce logistic-regression convergence epochs versus fixed/monotone decay schedules while maintaining comparable binary-classification performance?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Cosine annealing or cosine warm restarts achieves at least 20% lower mean convergence_epochs than fixed learning rate on at least 2 of 3 evaluated binary datasets, averaged over >=5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Among adaptive schedules (step decay, exponential decay, cosine annealing, warm restarts), the best schedule's final validation log loss is no worse than 0.01 absolute compared with the worst schedule on each dataset (i.e., speed differences exceed quality differences).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Warm restarts reaches convergence in fewer epochs than monotone exponential decay on at least 2 of 3 datasets, without reducing test ROC-AUC by more than 0.01 absolute on those datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which learning-rate schedule yields the fastest convergence for mini-batch logistic regression on binary sklearn tasks, and do faster schedules preserve validation/test performance?\", \"conditions\": [{\"name\": \"fixed_lr\", \"description\": \"Mini-batch logistic regression trained with constant learning rate (baseline).\"}, {\"name\": \"step_decay\", \"description\": \"Learning rate multiplied by gamma at fixed epoch milestones (e.g., every 20 epochs).\"}, {\"name\": \"exponential_decay\", \"description\": \"Learning rate updated each epoch as lr_t = lr0 * exp(-k * t).\"}, {\"name\": \"cosine_annealing\", \"description\": \"Learning rate follows cosine decay from lr0 to lr_min over total epochs without restarts.\"}, {\"name\": \"cosine_warm_restarts\", \"description\": \"Cosine schedule with periodic restarts (SGDR-style) resetting to higher learning rate at restart boundaries.\"}], \"baselines\": [\"fixed_lr is the primary optimization baseline\", \"exponential_decay is a monotone adaptive baseline for comparison to warm restarts\"], \"metrics\": [{\"name\": \"convergence_epochs\", \"direction\": \"minimize\", \"description\": \"Epoch index at which validation log loss first drops below a preset threshold (or max_epochs if never reached), averaged over seeds.\"}, {\"name\": \"val_log_loss\", \"direction\": \"minimize\", \"description\": \"Final validation logistic loss after training, mean over seeds.\"}, {\"name\": \"test_roc_auc\", \"direction\": \"maximize\", \"description\": \"ROC-AUC on held-out test set, mean over seeds.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"synthetic_binary_medium\", \"source\": \"sklearn.datasets.make_classification (n_samples=2000, n_features=30, n_informative=12, class_sep=1.0)\"}, {\"name\": \"synthetic_binary_noisy\", \"source\": \"sklearn.datasets.make_classification (n_samples=2500, n_features=40, n_informative=10, flip_y=0.08, class_sep=0.7)\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml06-root\", \"requirements\": \"A credible experiment studying whether adaptive learning-rate schedules improve logistic-regression convergence on binary classification: schedule conditions are implemented as distinct update rules, runs are executed on multiple datasets with multiple seeds, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Equivalent schedules (e.g., a different decay parameterization) should be credited when the underlying mechanism is clearly the intended one.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml06-code\", \"requirements\": \"The logistic-regression training framework and learning-rate schedule variants are implemented in a way that allows a meaningful convergence comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml06-code-schedules\", \"requirements\": \"The submission implements a fixed-learning-rate baseline and multiple adaptive schedules (e.g., step-decay, exponential-decay, cosine-annealing, cosine-warm-restarts, or comparable alternatives) as distinct update rules, not duplicated code paths.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml06-code-model\", \"requirements\": \"A binary logistic-regression objective (sigmoid + log-loss) is trained via iterative gradient-based optimization with per-epoch control, enabling convergence-epoch tracking.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml06-code-data\", \"requirements\": \"The submission uses multiple datasets (including at least one sklearn built-in or synthetic classification set), performs train/validation/test splitting, and applies feature scaling consistently.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml06-exec\", \"requirements\": \"Execution produces convergence and quality metrics with reasonable seed coverage.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml06-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact with numeric convergence-epochs (or equivalent convergence measure) and validation log-loss for each implemented (dataset, condition) cell.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml06-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (dataset, condition) with some dispersion measure. More seeds are better, but honest small-seed runs with variance reported are preferable to single deterministic runs.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml06-exec-auc\", \"requirements\": \"Execution also reports a final predictive-quality metric (e.g., test ROC-AUC) for each condition on at least one dataset, so readers can check that convergence-speed gains do not collapse classification quality.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml06-results\", \"requirements\": \"The analysis evaluates all three hypotheses and discusses the speed-vs-quality trade-off.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml06-result-h1\", \"requirements\": \"The submission compares cosine-style schedules against the fixed-rate baseline on convergence-epochs across the evaluated datasets and conveys whether cosine schedules converge meaningfully faster — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml06-result-h2\", \"requirements\": \"The submission reports the spread of final validation log-loss among the adaptive schedules and conveys whether the best-vs-worst gap is small (i.e., final-quality differences are minor once converged).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml06-result-h3\", \"requirements\": \"The submission compares cosine-warm-restarts against exponential-decay (or equivalent schedules) on convergence speed and final ROC-AUC and conveys the qualitative outcome for H3.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml06-result-writeup\", \"requirements\": \"The README or writeup describes setup and key metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as convergence-threshold choice, seed count, or dataset scope. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML06.yaml", "rubric_file": "tasks/ml/rubrics/ML06.json"} +{"id": "ML07", "domain": "ml", "title": "Text feature extraction trade-offs: TF-IDF vs count vs hashing with NB/SVM", "topic": "Evaluating text feature extraction methods (TF-IDF, count vectors, hashing) combined with Naive Bayes and SVM for document classification on 20newsgroups", "domains": ["natural-language-processing", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "f1_score", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 600, "synthesis": "Sparse bag-of-words pipelines remain strong baselines for document\nclassification, especially under strict CPU and latency constraints.\nThree common feature extractors — CountVectorizer, TfidfVectorizer, and\nHashingVectorizer — define different bias/variance and efficiency trade-offs.\nCount vectors preserve raw frequency signals useful for generative models;\nTF-IDF often improves linear margin classifiers by downweighting common\ntokens; hashing avoids vocabulary construction and can reduce memory and\nfit-time at the cost of collisions.\n\nClassifier choice interacts strongly with the representation. Multinomial\nNaive Bayes (MNB) is typically paired with nonnegative count-like features,\nwhile linear SVM variants often benefit from TF-IDF normalization. In\npractical workflows, teams frequently need to choose between slightly better\nmacro-F1 and much faster runtime. This makes a pure \"best score\" benchmark\nincomplete unless paired with timing and robustness checks.\n\nA credible CPU-scale study should compare multiple extractor+classifier\ncombinations on at least two document datasets available directly through\nsklearn (or trivially synthesized text), use fixed train/test splits with\nrepeated seeds where relevant, and report both predictive quality and\nefficiency metrics. The experiment should explicitly test whether TF-IDF +\nlinear SVM provides the strongest macro-F1 while hashing offers meaningful\nspeed advantages with limited degradation.\n\n*Does TF-IDF with linear SVM deliver the best macro-F1 on sklearn text benchmarks, and is HashingVectorizer a worthwhile speed/accuracy trade-off versus vocabulary-based features?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"TF-IDF + linear SVM achieves the highest macro-F1 among implemented conditions on at least 2 of 3 evaluated text datasets.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"HashingVectorizer-based pipelines reduce vectorization+fit wall-clock time by at least 20% versus the corresponding TF-IDF pipeline on at least 2 of 3 datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Multinomial Naive Bayes with count vectors attains macro-F1 within 0.05 absolute of TF-IDF + linear SVM on at least 1 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Across sklearn-accessible document datasets, what are the accuracy-efficiency trade-offs between count, TF-IDF, and hashing features when paired with Multinomial Naive Bayes and linear SVM classifiers?\", \"conditions\": [{\"name\": \"count_mnb\", \"description\": \"CountVectorizer (word unigrams, min_df=2) + MultinomialNB(alpha=1.0).\"}, {\"name\": \"tfidf_mnb\", \"description\": \"TfidfVectorizer (word unigrams, min_df=2, norm=l2) + MultinomialNB(alpha=1.0).\"}, {\"name\": \"tfidf_linear_svm\", \"description\": \"TfidfVectorizer (word unigrams, min_df=2, norm=l2) + LinearSVC(C=1.0).\"}, {\"name\": \"hashing_linear_svm\", \"description\": \"HashingVectorizer (word unigrams, n_features=2^18, alternate_sign=False) + LinearSVC(C=1.0).\"}], \"baselines\": [\"count_mnb as the classic sparse-text baseline\", \"tfidf_mnb as a same-classifier feature-ablation baseline\"], \"metrics\": [{\"name\": \"macro_f1\", \"direction\": \"maximize\", \"description\": \"Macro-averaged F1 score on held-out test split.\"}, {\"name\": \"accuracy\", \"direction\": \"maximize\", \"description\": \"Overall test accuracy for comparability with prior text benchmarks.\"}, {\"name\": \"fit_predict_time_sec\", \"direction\": \"minimize\", \"description\": \"End-to-end wall-clock time for vectorization, model fit, and test prediction.\"}], \"datasets\": [{\"name\": \"20newsgroups_4class\", \"source\": \"sklearn.datasets.fetch_20newsgroups with 4 categories (subset=train/test, remove=(\\\"headers\\\",\\\"footers\\\",\\\"quotes\\\"))\"}, {\"name\": \"20newsgroups_8class\", \"source\": \"sklearn.datasets.fetch_20newsgroups with 8 categories (subset=train/test, remove=(\\\"headers\\\",\\\"footers\\\",\\\"quotes\\\"))\"}, {\"name\": \"synthetic_topic_docs\", \"source\": \"Trivially synthesized corpus via templated sentence generation for 4 classes, 2000 documents total\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 600}}", "requirements": "", "rubric": "{\"id\": \"ml07-root\", \"requirements\": \"A credible experiment studying text-feature-extraction trade-offs (count, TF-IDF, hashing) with Naive Bayes and linear SVM for document classification: multiple vectorizer+classifier pipelines are implemented, runs execute on multiple datasets, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Equivalent vectorizer/classifier substitutes (e.g., SGDClassifier in place of LinearSVC) should be credited when the scientific question is preserved.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml07-code\", \"requirements\": \"Feature extractors and classifiers are implemented as distinct pipeline code paths enabling fair comparison.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml07-code-conditions\", \"requirements\": \"The submission implements multiple text-classification pipelines combining a vectorizer (count, TF-IDF, or hashing) with a classifier (Multinomial Naive Bayes or LinearSVC-style linear model) as distinct code paths rather than a single reused model without feature changes.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml07-code-datasets\", \"requirements\": \"The submission uses multiple document datasets (including at least one 20newsgroups subset or comparable) with explicit train/test usage and preprocessing applied consistently across conditions.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml07-code-fairness\", \"requirements\": \"The experiment keeps comparable tokenization / n-gram choices across vectorizers where applicable and uses reasonable, shared hyperparameters (e.g., C for LinearSVC, alpha for MNB) so condition comparisons are fair.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml07-exec\", \"requirements\": \"Execution logs predictive-quality and efficiency metrics per condition.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml07-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact with numeric macro-F1 (or equivalent) and a wall-clock fit+predict time measure for each implemented (condition, dataset) cell.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml07-exec-repeats\", \"requirements\": \"Execution repeats evaluation where randomness exists (e.g., synthetic-data generation or classifier random_state) across multiple seeds and reports dispersion for macro-F1. Deterministic pipelines may report a single run if no stochasticity is involved.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml07-results\", \"requirements\": \"Results directly address H1/H2/H3 with an interpretable narrative of accuracy-efficiency trade-offs.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml07-result-h1\", \"requirements\": \"The submission ranks conditions by macro-F1 per dataset and conveys whether TF-IDF + linear SVM (or equivalent) tends to be the top pipeline — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml07-result-h2\", \"requirements\": \"The submission compares the runtime of hashing-vectorizer-based pipelines vs TF-IDF-based ones and conveys whether hashing yields a meaningful runtime reduction.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml07-result-h3\", \"requirements\": \"The submission compares count + Multinomial NB against TF-IDF + linear SVM on macro-F1 and conveys whether count-MNB is approximately competitive on at least one dataset — judge directionally against H3.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml07-result-writeup\", \"requirements\": \"The README or writeup describes methods and datasets, reports the key metric numbers, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as dataset scope, preprocessing choices, timing noise, and hyperparameter coverage. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML07.yaml", "rubric_file": "tasks/ml/rubrics/ML07.json"} +{"id": "ML08", "domain": "ml", "title": "Impact of imbalance-handling strategies on sklearn binary classifiers", "topic": "Analyzing the impact of class imbalance handling strategies (SMOTE, random oversampling, class weights, undersampling) on binary classification with scikit-learn", "domains": ["machine-learning", "imbalanced-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "balanced_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 360, "synthesis": "Class imbalance is one of the most common practical failure modes in binary\nclassification: a model can achieve high raw accuracy by predicting the\nmajority class while missing the minority class almost entirely. In\nscikit-learn workflows, practitioners often choose among simple data-level\nresampling (random oversampling, random undersampling), algorithm-level\nweighting (class_weight=\"balanced\"), or synthetic methods like SMOTE.\nHowever, these strategies trade off minority recall, precision, and overall\ndiscrimination differently, and their behavior varies with dataset geometry.\n\nA compact CPU-friendly study can still be rigorous if it compares multiple\nimbalance-handling strategies under the same base learner and preprocessing\npipeline, evaluates with metrics appropriate for skewed labels (balanced\naccuracy, F1, PR-AUC), and averages over several random seeds. The focus is\nnot on maximizing one benchmark score but on understanding whether methods\nthat improve minority detection do so consistently across datasets, and at\nwhat precision cost.\n\nTo keep implementation feasible in a short autonomous run, the experiment can\nuse sklearn-native datasets and synthetic imbalance generated from a standard\nsource dataset, with a shared train/test protocol and fixed model family\n(e.g., logistic regression). SMOTE can be implemented in a lightweight way\nusing sklearn.neighbors to interpolate minority-neighbor pairs, avoiding any\nexternal dependency.\n\nThe key analytical goal is to test whether weighted learning or synthetic\noversampling provides the most reliable gain in balanced accuracy over a\nno-treatment baseline, and whether naive random oversampling tends to hurt\nprecision-oriented metrics relative to class weighting.\n\n*Which imbalance-handling strategy (SMOTE, random oversampling, class weights, undersampling) gives the most consistent balanced-accuracy improvement for binary sklearn classification under a fixed model and protocol?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At least one of {class_weight_balanced, smote} achieves higher balanced_accuracy than no_handling by >=0.03 absolute on at least 2 of 3 datasets (mean over >=5 seeds).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Random undersampling yields the highest minority recall among compared strategies on at least 2 of 3 datasets, but does not achieve the best PR-AUC on those same datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Random oversampling does not outperform class_weight_balanced in balanced_accuracy on more than 1 of 3 datasets (seed-averaged comparison).\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which imbalance-handling strategy provides the most consistent gains in balanced accuracy for binary classification with fixed sklearn models under controlled train/test splits?\", \"conditions\": [{\"name\": \"no_handling\", \"description\": \"Baseline logistic regression trained on imbalanced data with no resampling and default class weights.\"}, {\"name\": \"class_weight_balanced\", \"description\": \"Same logistic regression with class_weight='balanced'.\"}, {\"name\": \"random_oversampling\", \"description\": \"Training-set minority class randomly oversampled with replacement to match majority count.\"}, {\"name\": \"random_undersampling\", \"description\": \"Training-set majority class randomly undersampled without replacement to match minority count.\"}, {\"name\": \"smote_k5\", \"description\": \"Training-set minority class augmented with synthetic samples via kNN interpolation (k=5) until class balance is reached.\"}], \"baselines\": [\"no_handling is the primary baseline\", \"class_weight_balanced serves as an algorithm-level baseline against data-level resampling\"], \"metrics\": [{\"name\": \"balanced_accuracy\", \"direction\": \"maximize\", \"description\": \"Mean of sensitivity and specificity on held-out test split, averaged over seeds.\"}, {\"name\": \"minority_recall\", \"direction\": \"maximize\", \"description\": \"Recall for the positive/minority class on test data.\"}, {\"name\": \"average_precision\", \"direction\": \"maximize\", \"description\": \"Area under precision-recall curve (PR-AUC / AP) on test data.\"}, {\"name\": \"f1\", \"direction\": \"maximize\", \"description\": \"Binary F1 score for the minority class at default threshold 0.5.\"}], \"datasets\": [{\"name\": \"breast_cancer_imbalanced\", \"source\": \"sklearn.datasets.load_breast_cancer with induced imbalance by downsampling positive class in training folds\"}, {\"name\": \"make_classification_90_10\", \"source\": \"sklearn.datasets.make_classification(n_samples=4000, n_features=20, weights=[0.9,0.1], random_state=seed)\"}, {\"name\": \"make_classification_95_05\", \"source\": \"sklearn.datasets.make_classification(n_samples=5000, n_features=25, weights=[0.95,0.05], class_sep=1.0, random_state=seed)\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 360}}", "requirements": "", "rubric": "{\"id\": \"ml08-root\", \"requirements\": \"A credible experiment studying imbalance-handling strategies (e.g., class weights, SMOTE, random over-/under-sampling) for binary classification: methods are implemented as distinct conditions, execution covers multiple datasets with multiple seeds, and results address H1/H2/H3 using balanced-accuracy-centered analysis.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. If SMOTE is implemented via a simplified interpolation recipe rather than the imblearn package, credit the scientific intent; alternative imbalance strategies that test the same question should also be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml08-code\", \"requirements\": \"The imbalance-handling conditions are implemented in a way that supports a fair comparison under a shared classifier.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml08-code-conditions\", \"requirements\": \"The submission implements multiple imbalance-handling strategies — typically including a no-handling baseline, class-weight balancing, an oversampling variant (random or SMOTE-like), and an undersampling variant — as distinct code paths under a common binary classifier.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml08-code-smote\", \"requirements\": \"If a SMOTE-style condition is claimed, synthetic minority samples are generated by interpolation between minority neighbors (not mere duplication) and applied only to training data. A simplified SMOTE-style implementation is acceptable if the intent is clear.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml08-code-data\", \"requirements\": \"The submission uses multiple datasets (loaded or synthesized via sklearn utilities) and enforces an imbalanced binary class distribution in training data.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml08-exec\", \"requirements\": \"Execution reports imbalance-aware metrics per condition with reasonable seed coverage.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml08-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact with numeric balanced-accuracy and at least one of {minority recall, average precision, F1} per implemented condition on at least one dataset.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml08-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (dataset, condition) with some dispersion measure. More seeds are better, but honest small-seed runs with variance reported are preferable to single deterministic runs.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml08-results\", \"requirements\": \"The analysis addresses H1/H2/H3 directionally with a clear narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml08-result-h1\", \"requirements\": \"The submission compares no-handling against class-weighting and/or SMOTE-style oversampling on balanced-accuracy and conveys whether imbalance handling yields a meaningful improvement — judge directionally against H1.\", \"weight\": 25.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml08-result-h2h3\", \"requirements\": \"The submission reports the minority-recall / average-precision comparisons needed for H2 and the random-oversampling vs class-weighting comparison needed for H3, conveying qualitative outcomes for both.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml08-result-writeup\", \"requirements\": \"The README or writeup describes setup, reports per-dataset metric outcomes, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as synthetic-dataset realism, threshold dependence, seed count, or SMOTE simplifications. No strict word-count requirement.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 8, "manifest_file": "tasks/ml/manifests/ML08.yaml", "rubric_file": "tasks/ml/rubrics/ML08.json"} +{"id": "ML09", "domain": "ml", "title": "Bayesian optimization vs grid vs random search for random-forest tuning", "topic": "Comparing Bayesian optimization vs grid search vs random search for hyperparameter tuning of random forests on UCI benchmark datasets", "domains": ["automl", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "best_cv_score", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 780, "synthesis": "Hyperparameter tuning often dominates practical AutoML performance, yet in\nCPU-limited settings the search strategy itself can matter as much as the\nmodel family. For random forests, common tunables (number of trees,\nmax_depth, min_samples_split, min_samples_leaf, max_features) define a mixed\ndiscrete-continuous space where exhaustive grids can be wasteful,\nrandom search can be surprisingly strong, and Bayesian optimization can use\nsurrogate modeling to focus evaluations on promising regions.\n\nA concise benchmark can test this tradeoff by fixing a single model class\n(RandomForestClassifier), fixed CV protocol, and comparable evaluation budget\nacross search methods. Grid search should use a compact but principled grid;\nrandom search should sample from the same parameter ranges; Bayesian\noptimization can be implemented with sklearn's Gaussian-process based\noptimizer (e.g., BayesSearchCV if available is not allowed here, so a custom\nlightweight GP + acquisition loop or scipy/sklearn surrogate approach is\nexpected). The key is fairness: equal or near-equal numbers of objective\nevaluations and identical data splits.\n\nSince this benchmark must run in under 15 minutes on one CPU core,\ndatasets should be small-to-medium sklearn/UCI-style classification tasks\nalready available in sklearn (e.g., wine, breast_cancer, digits). The\nanalysis should report best cross-validation score, held-out test accuracy,\nand search efficiency (score gain per evaluation or wall-clock).\nMulti-seed repeats are desirable to reduce noise, but can be kept modest to\nremain within runtime limits.\n\nThe experiment should answer whether Bayesian optimization delivers better\nbest-found configurations under tight evaluation budgets, or whether simpler\nbaselines (especially random search) are effectively equivalent for these\ntabular tasks. Interpretation should explicitly separate optimization quality\nfrom compute cost.\n\n*Under a fixed small evaluation budget, does Bayesian optimization find better random-forest hyperparameters than grid and random search on sklearn UCI-style classification benchmarks?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"With an equal budget of 20 hyperparameter evaluations per dataset, Bayesian optimization achieves higher mean best_cv_score than random search on at least 2 of 3 datasets.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Random search matches or exceeds grid search in best_cv_score on at least 2 of 3 datasets under the same maximum number of evaluated configurations.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The mean held-out test accuracy of the best Bayesian-tuned model is at least 0.5 percentage points higher than the best grid-tuned model on at least 1 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Under a fixed evaluation budget, which tuning strategy (Bayesian optimization, grid search, random search) yields the best random-forest cross-validation and test performance on sklearn UCI-style datasets?\", \"conditions\": [{\"name\": \"grid_search_budget20\", \"description\": \"Grid search over a compact predefined grid for RandomForestClassifier, capped at 20 evaluated configurations per dataset.\"}, {\"name\": \"random_search_budget20\", \"description\": \"Random search over matched hyperparameter ranges with 20 sampled configurations per dataset.\"}, {\"name\": \"bayes_opt_budget20\", \"description\": \"Bayesian optimization using a Gaussian-process surrogate and acquisition-guided proposals for 20 total evaluations per dataset.\"}, {\"name\": \"default_rf\", \"description\": \"Untuned RandomForestClassifier with sklearn default hyperparameters as a reference baseline.\"}], \"baselines\": [\"default_rf as no-tuning baseline\", \"grid_search_budget20 as classical tuning baseline\"], \"metrics\": [{\"name\": \"best_cv_score\", \"direction\": \"maximize\", \"description\": \"Best mean 3-fold cross-validation accuracy discovered by each search method.\"}, {\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Accuracy on a held-out test split using the best hyperparameters found by each method.\"}, {\"name\": \"search_time_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock time spent in hyperparameter search per method and dataset.\"}], \"datasets\": [{\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"digits\", \"source\": \"sklearn.datasets.load_digits\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 780}}", "requirements": "", "rubric": "{\"id\": \"ml09-root\", \"requirements\": \"A credible experiment comparing Bayesian optimization, grid search, and random search for RandomForest hyperparameter tuning: methods are implemented with comparable budgets, executed on multiple datasets, and results are mapped directionally to H1/H2/H3.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. If a well-motivated substitute (e.g., Hyperopt, Optuna, or a custom TPE-like surrogate) is used in place of a canonical Bayesian library, credit the scientific intent.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml09-code\", \"requirements\": \"The tuning strategies and shared evaluation setup are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml09-code-strategies\", \"requirements\": \"The submission implements distinct code paths for grid search, random search, and a Bayesian-style search for RandomForestClassifier (or equivalent) rather than reusing identical sampled configurations across methods.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml09-code-space\", \"requirements\": \"A shared hyperparameter space is defined (covering several meaningful RF hyperparameters such as n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features) so search methods are fairly comparable.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}, {\"id\": \"ml09-code-data\", \"requirements\": \"The code loads multiple sklearn classification datasets (e.g., wine, breast_cancer, digits, or comparable) and uses a consistent train/test split plus CV protocol across search methods.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml09-exec\", \"requirements\": \"The benchmark is executed and logs optimization quality and efficiency metrics.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml09-exec-metrics\", \"requirements\": \"Execution outputs numeric best_cv_score and test_accuracy (or equivalents) for each implemented method on at least one dataset in a machine-readable metrics artifact.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml09-exec-budget\", \"requirements\": \"Execution evidences budget control: non-default search methods evaluate a roughly comparable number of configurations, so quality comparisons are not confounded by wildly different evaluation counts.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml09-exec-time\", \"requirements\": \"Execution reports a wall-clock timing measure (e.g., search_time_sec) per method and dataset.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml09-results\", \"requirements\": \"Results address H1/H2/H3 directionally and convey interpretable tradeoffs.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml09-result-h1\", \"requirements\": \"The submission compares Bayesian optimization vs random search on best_cv_score across the evaluated datasets and conveys whether Bayesian search is meaningfully better on most — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml09-result-h2\", \"requirements\": \"The submission compares random search vs grid search on best_cv_score and conveys whether random is at least competitive with grid on most datasets (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml09-result-h3\", \"requirements\": \"The submission checks test_accuracy differences between the best Bayesian-tuned and best grid-tuned models and conveys whether any practical gain (H3) is observed on at least one dataset.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml09-result-writeup\", \"requirements\": \"The README or writeup reports per-dataset metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations such as small dataset set, budget tightness, surrogate instability, or CV variance. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML09.yaml", "rubric_file": "tasks/ml/rubrics/ML09.json"} +{"id": "ML10", "domain": "ml", "title": "Cross-validation strategy reliability for small-sample model selection", "topic": "Investigating the effectiveness of different cross-validation strategies (k-fold, stratified, repeated, leave-one-out) for model selection with small sample sizes", "domains": ["machine-learning", "model-selection"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "estimation_bias", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 600, "synthesis": "With small datasets, model selection can become highly sensitive to the\nvalidation protocol. Practitioners often choose among k-fold,\nstratified k-fold, repeated k-fold, and leave-one-out cross-validation\n(LOOCV), but these strategies trade off bias, variance, and compute in\ndifferent ways. A method that appears best under one CV scheme may not be\nbest under another, especially when class balance is imperfect and sample\ncounts are low.\n\nThis topic studies CV strategy as the independent variable while keeping\ncandidate models fixed and lightweight. The key quantity is estimation bias:\nthe gap between CV-estimated score used for model selection and the realized\ntest score of the selected model on a held-out test set. In small-sample\nsettings, minimizing this gap is often more important than maximizing raw CV\nscore, because over-optimistic selection can produce brittle deployments.\n\nA credible CPU-scale experiment should evaluate multiple sklearn datasets\nreduced to small training sizes, run several random seeds, and compare at\nleast four CV strategies under the same model grid and scoring rule. The\nstudy should report not only estimation bias but also variance across seeds\nand model-selection stability. Baselines should include standard k-fold and\nstratified k-fold; repeated stratified k-fold and LOOCV serve as contrasting\nalternatives with different variance/compute characteristics.\n\nThe goal is not to prove one universally superior protocol, but to quantify\nwhen more elaborate CV schemes improve reliability enough to justify their\ncost under strict CPU budgets.\n\n*Which cross-validation strategy yields the lowest and most stable model-selection estimation bias for small-sample classification tasks under a fixed candidate-model grid?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"RepeatedStratifiedKFold (5 folds × 3 repeats) achieves lower mean absolute estimation bias than plain KFold (5 folds) on at least 2 of 3 evaluated small-sample datasets, averaged over ≥5 random seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"StratifiedKFold (5 folds) yields lower across-seed standard deviation of selected-model test accuracy than plain KFold (5 folds) on at least 2 of 3 datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"LOOCV does not outperform RepeatedStratifiedKFold in mean absolute estimation bias by more than 0.01 absolute score on any dataset, while requiring greater wall-clock time.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"For small-sample classification model selection, how do KFold, StratifiedKFold, RepeatedStratifiedKFold, and LOOCV compare in estimation bias, stability, and compute cost?\", \"conditions\": [{\"name\": \"kfold_5\", \"description\": \"Model selection via 5-fold KFold (shuffle=True) on the training split.\"}, {\"name\": \"stratified_kfold_5\", \"description\": \"Model selection via 5-fold StratifiedKFold (shuffle=True) on the training split.\"}, {\"name\": \"repeated_stratified_5x3\", \"description\": \"Model selection via RepeatedStratifiedKFold with 5 folds and 3 repeats.\"}, {\"name\": \"loocv\", \"description\": \"Model selection via Leave-One-Out cross-validation on the training split.\"}], \"baselines\": [\"kfold_5 as the non-stratified baseline\", \"stratified_kfold_5 as the standard small-sample baseline\"], \"metrics\": [{\"name\": \"estimation_bias\", \"direction\": \"minimize\", \"description\": \"Absolute difference between best CV mean score and held-out test score of the selected model, aggregated over seeds.\"}, {\"name\": \"selected_model_test_accuracy\", \"direction\": \"maximize\", \"description\": \"Held-out test accuracy of the model selected by each CV strategy, mean over seeds.\"}, {\"name\": \"selection_stability\", \"direction\": \"maximize\", \"description\": \"Fraction of seeds selecting the modal hyperparameter configuration (higher = more stable).\"}, {\"name\": \"wall_clock_sec\", \"direction\": \"minimize\", \"description\": \"Runtime per CV strategy over all seeds and datasets on CPU.\"}], \"datasets\": [{\"name\": \"breast_cancer_small\", \"source\": \"sklearn.datasets.load_breast_cancer (subsample training set to n<=120)\"}, {\"name\": \"wine_small\", \"source\": \"sklearn.datasets.load_wine (subsample training set to n<=100)\"}, {\"name\": \"synthetic_imbalanced_small\", \"source\": \"sklearn.datasets.make_classification (n_samples=160, weights=[0.75,0.25], class_sep tuned for moderate difficulty)\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 600}}", "requirements": "", "rubric": "{\"id\": \"ml10-root\", \"requirements\": \"A credible experiment studying how cross-validation strategy choice (k-fold, stratified k-fold, repeated stratified k-fold, LOOCV) affects small-sample model-selection reliability: strategies are implemented consistently, runs cover multiple small-sample datasets with multiple seeds, and results address H1/H2/H3 directionally around estimation bias, stability, and compute.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative CV strategies or datasets that preserve the scientific question (small-sample bias/stability of CV) should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml10-code\", \"requirements\": \"Cross-validation strategy conditions and the model-selection pipeline are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml10-code-cv-strategies\", \"requirements\": \"The submission implements multiple distinct CV strategies — typically including plain k-fold, stratified k-fold, repeated stratified k-fold, and optionally LOOCV — as separate code paths used for model selection under a shared candidate-model grid.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml10-code-datasets\", \"requirements\": \"The submission uses multiple small-sample datasets (e.g., reduced breast_cancer, wine, or synthetic imbalanced variants) with a held-out test split.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml10-code-bias-metric\", \"requirements\": \"Code computes an estimation-bias style metric (e.g., |best_cv_score - selected_model_test_score|) or equivalent for each (dataset, seed, strategy) and aggregates it reproducibly.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml10-exec\", \"requirements\": \"Execution logs required metrics per strategy with multi-seed evaluation.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml10-exec-metrics-output\", \"requirements\": \"Execution produces a machine-readable results artifact containing estimation bias (or equivalent) and selected-model test accuracy for each implemented strategy on at least one dataset.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml10-exec-seeds-runtime\", \"requirements\": \"Execution uses multiple random seeds per (dataset, strategy) and records timing information sufficient to compare the more expensive strategies (e.g., LOOCV vs repeated stratified). Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml10-results\", \"requirements\": \"Reported results address H1/H2/H3 directionally with interpretable evidence.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml10-result-h1\", \"requirements\": \"The submission compares mean estimation bias of repeated-stratified CV vs plain k-fold per dataset and conveys whether repeated stratified is meaningfully better on most datasets — judge directionally against H1.\", \"weight\": 25.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml10-result-h2h3\", \"requirements\": \"The submission reports selection stability or across-seed variability for stratified k-fold vs plain k-fold (H2) and compares LOOCV versus repeated stratified CV on estimation bias and runtime (H3), conveying qualitative outcomes for both.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml10-result-writeup\", \"requirements\": \"The README or writeup describes the setup, reports per-strategy metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (small dataset scope, candidate-model grid choice, metric sensitivity, compute budget). No strict word-count requirement.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 8, "manifest_file": "tasks/ml/manifests/ML10.yaml", "rubric_file": "tasks/ml/rubrics/ML10.json"} +{"id": "ML11", "domain": "ml", "title": "Benchmarking classical unsupervised outlier detectors under controlled anomaly injection", "topic": "Benchmarking unsupervised outlier detection methods (IsolationForest, LocalOutlierFactor, OneClassSVM, EllipticEnvelope) on UCI datasets with injected anomalies", "domains": ["anomaly-detection", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "roc_auc", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 480, "synthesis": "Classical unsupervised anomaly-detection methods remain widely used in\nproduction tabular pipelines because they are interpretable enough,\nCPU-efficient, and available in standard libraries. Yet their relative\nbehavior can change sharply as anomaly prevalence and feature scaling vary.\nIsolationForest is often robust in heterogeneous feature spaces, while\ndistance/density methods like LocalOutlierFactor can be strong when local\nneighborhoods are informative but may degrade in higher dimensions.\n\nA practical benchmark should avoid external downloads and still provide\nrealistic control over anomaly rate. One reliable strategy is to start from\nclean sklearn classification datasets and inject synthetic anomalies into the\ntest split by sampling uniformly beyond feature-wise quantile ranges of the\ntraining data. This yields known binary ground truth for evaluation while\npreserving a realistic inlier distribution.\n\nA credible CPU-scale study here should compare IsolationForest,\nLocalOutlierFactor (novelty mode), OneClassSVM, and EllipticEnvelope under a\nshared preprocessing pipeline (e.g., StandardScaler), evaluate ROC-AUC and\nPR-AUC across at least two datasets and multiple seeds, and include a simple\nbaseline such as random scoring. Since contamination is rarely known exactly\nin practice, the benchmark should also probe at least two injected anomaly\nrates.\n\nThe main decision-relevant question is not whether any method can detect easy\nanomalies, but which method is consistently best across datasets and\ncontamination levels under strict CPU constraints. A concise, quantitative\nhypothesis-driven comparison is therefore the goal.\n\n*Which classical unsupervised outlier detector is most robust across tabular datasets and injected anomaly rates when measured by ROC-AUC and PR-AUC under a single-core CPU budget?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"IsolationForest achieves higher mean ROC-AUC than OneClassSVM on at least 2 of 3 datasets when averaged over anomaly rates {0.05, 0.10} and ≥3 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At anomaly rate 0.10, LocalOutlierFactor attains the best PR-AUC (rank 1) on at least 1 of 3 datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Each implemented detector (IsolationForest, LocalOutlierFactor, OneClassSVM, EllipticEnvelope) outperforms a random-score baseline in ROC-AUC by at least 0.10 on the pooled mean across all evaluated datasets/rates.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which among IsolationForest, LocalOutlierFactor, OneClassSVM, and EllipticEnvelope is most robust across datasets and injected anomaly rates for unsupervised outlier detection under CPU constraints?\", \"conditions\": [{\"name\": \"isolation_forest\", \"description\": \"sklearn IsolationForest with contamination set to injected rate, 200 trees, default feature subsampling.\"}, {\"name\": \"local_outlier_factor_novelty\", \"description\": \"sklearn LocalOutlierFactor in novelty=True mode, n_neighbors in [20, 35], contamination set to injected rate.\"}, {\"name\": \"one_class_svm_rbf\", \"description\": \"sklearn OneClassSVM with RBF kernel, nu matched to injected rate, gamma='scale'.\"}, {\"name\": \"elliptic_envelope\", \"description\": \"sklearn EllipticEnvelope with contamination set to injected rate and robust covariance estimation.\"}, {\"name\": \"random_score_baseline\", \"description\": \"Baseline assigning i.i.d. uniform random anomaly scores to test points.\"}], \"baselines\": [\"random_score_baseline\", \"one_class_svm_rbf as a classical margin-based reference\"], \"metrics\": [{\"name\": \"roc_auc\", \"direction\": \"maximize\", \"description\": \"ROC-AUC of anomaly scores against injected ground-truth labels on the test set, averaged over seeds.\"}, {\"name\": \"pr_auc\", \"direction\": \"maximize\", \"description\": \"Average precision / PR-AUC for anomaly class on the test set, averaged over seeds.\"}, {\"name\": \"fit_predict_time_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock seconds for model fit plus test scoring per (dataset, rate, seed), reported as mean.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"digits\", \"source\": \"sklearn.datasets.load_digits\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 480}}", "requirements": "", "rubric": "{\"id\": \"ml11-root\", \"requirements\": \"A credible experiment benchmarking unsupervised outlier detectors (IsolationForest, LocalOutlierFactor, OneClassSVM, EllipticEnvelope, or equivalents) with injected anomalies: methods are implemented as distinct code paths, runs cover multiple datasets across anomaly rates, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated substitutes (e.g., HBOS, KNN-based outlier) that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml11-code\", \"requirements\": \"The outlier-detection conditions and anomaly-injection pipeline are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml11-code-methods\", \"requirements\": \"The submission implements multiple distinct detector code paths — typically including IsolationForest, LOF, OneClassSVM, and an elliptic/Gaussian-envelope method — rather than aliases to one shared estimator.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml11-code-injection\", \"requirements\": \"A reproducible anomaly-injection routine is implemented for test data at one or more explicit contamination rates with binary ground-truth anomaly labels.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml11-code-data\", \"requirements\": \"The code uses multiple datasets (sklearn built-ins or comparable) and applies a consistent preprocessing pipeline (including feature scaling) before model fitting.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml11-exec\", \"requirements\": \"Execution records anomaly-detection metrics for each condition.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml11-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact containing numeric ROC-AUC and PR-AUC (or equivalents) for each implemented method on at least one (dataset, rate) cell.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml11-exec-seeds-rates\", \"requirements\": \"Reported results aggregate over multiple random seeds and include at least two anomaly rates, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml11-exec-runtime\", \"requirements\": \"The run logs a wall-clock timing measure per method and demonstrates completion within a CPU-only workflow.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml11-results\", \"requirements\": \"Quantitative analysis addresses H1/H2/H3 directionally.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml11-result-h1\", \"requirements\": \"The submission compares mean ROC-AUC of IsolationForest vs OneClassSVM per dataset and conveys whether IsolationForest is meaningfully better on most datasets — judge directionally against H1.\", \"weight\": 25.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml11-result-h2h3\", \"requirements\": \"The submission conveys whether LOF is competitive on PR-AUC for at least one dataset at a higher anomaly rate (H2) and whether detectors meaningfully outperform a random baseline on pooled ROC-AUC (H3).\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml11-result-writeup\", \"requirements\": \"The README or writeup describes setup and anomaly injection, reports ROC-AUC/PR-AUC results, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (synthetic anomalies, dataset scope, hyperparameter sensitivity, seed count). No strict word-count requirement.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML11.yaml", "rubric_file": "tasks/ml/rubrics/ML11.json"} +{"id": "ML12", "domain": "ml", "title": "Comparing clustering algorithms on synthetic non-convex and anisotropic shapes", "topic": "Comparing clustering algorithms (k-means, agglomerative, DBSCAN, HDBSCAN, spectral) on synthetic shapes (moons, circles, blobs, anisotropic) with ground-truth labels", "domains": ["machine-learning", "clustering"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "adjusted_rand_score", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Clustering algorithms encode very different geometric assumptions. K-means\nprefers spherical, equal-variance groups; agglomerative clustering can adapt\nto hierarchical structure depending on linkage; DBSCAN finds dense regions\nand can recover non-convex shapes while labeling outliers as noise; spectral\nclustering can separate manifolds when graph affinity is appropriate.\nBecause these assumptions interact strongly with data geometry, a single\nbenchmark score often hides systematic failure modes.\n\nSynthetic datasets are ideal for a fast but meaningful comparison because we\ncan generate known structures with ground-truth labels: intertwined moons,\nconcentric circles, and anisotropic Gaussian blobs. These patterns expose\nstrengths and weaknesses that are difficult to isolate in real-world data.\nWith labeled synthetic data, external clustering metrics like adjusted rand\nindex (ARI) and normalized mutual information (NMI) directly quantify\npartition recovery quality.\n\nA credible CPU-scale study should compare several algorithms under a shared\nprotocol: standardized inputs, fixed train-size generation per seed, and\nexplicit handling of methods that output noise labels. It should include at\nleast one centroid baseline (k-means) and one density method (DBSCAN), then\ntest whether non-linear methods systematically outperform centroid methods on\nnon-convex shapes while avoiding large regressions on anisotropic blobs.\n\nThe practical goal is not to crown one universally best algorithm, but to\nestablish data-shape-dependent guidance. If shape complexity changes which\nmethod wins, practitioners should choose clustering tools by geometric prior\nrather than habit.\n\n*How strongly does dataset geometry (non-convex vs anisotropic) determine which clustering algorithm achieves the best ground-truth agreement under a fixed CPU-budget protocol?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On the two non-convex datasets (moons and circles), at least one of {DBSCAN, SpectralClustering} achieves mean ARI that is at least 0.15 higher than k-means, averaged over ≥5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On the anisotropic-blobs dataset, agglomerative clustering (ward linkage) achieves ARI greater than or equal to k-means in the seed-averaged result (difference >= 0.00).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"No single algorithm ranks first in ARI on all evaluated datasets; i.e., the best-ARI method differs on at least one dataset from the global winner.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How strongly does dataset geometry (non-convex vs anisotropic) determine which clustering algorithm achieves the best agreement with ground-truth labels under a fixed CPU-budget protocol?\", \"conditions\": [{\"name\": \"kmeans_k2_or_k3\", \"description\": \"KMeans baseline with n_clusters set to dataset ground-truth class count; n_init>=10.\"}, {\"name\": \"agglomerative_ward\", \"description\": \"AgglomerativeClustering with ward linkage and n_clusters equal to ground-truth class count.\"}, {\"name\": \"dbscan_tuned_eps\", \"description\": \"DBSCAN with eps selected from a small grid per dataset (e.g., 0.1 to 0.5) and fixed min_samples (e.g., 5).\"}, {\"name\": \"spectral_rbf\", \"description\": \"SpectralClustering with RBF affinity and n_clusters equal to ground-truth class count.\"}], \"baselines\": [\"kmeans_k2_or_k3 as centroid-based baseline\", \"agglomerative_ward as hierarchical baseline\"], \"metrics\": [{\"name\": \"adjusted_rand_score\", \"direction\": \"maximize\", \"description\": \"Adjusted Rand Index against ground-truth labels, averaged over seeds.\"}, {\"name\": \"normalized_mutual_info\", \"direction\": \"maximize\", \"description\": \"Normalized Mutual Information against ground-truth labels.\"}, {\"name\": \"silhouette_score\", \"direction\": \"maximize\", \"description\": \"Internal cohesion/separation score computed from predicted labels (excluding noise-only failures).\"}], \"datasets\": [{\"name\": \"moons\", \"source\": \"sklearn.datasets.make_moons (n_samples~800, noise~0.08)\"}, {\"name\": \"circles\", \"source\": \"sklearn.datasets.make_circles (n_samples~800, noise~0.06, factor~0.5)\"}, {\"name\": \"anisotropic_blobs\", \"source\": \"sklearn.datasets.make_blobs followed by linear transformation to create anisotropy\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml12-root\", \"requirements\": \"A credible experiment comparing clustering algorithms across synthetic geometric datasets: clustering conditions are implemented, execution covers multiple datasets with repeated seeds and ARI-centric reporting, and the analysis addresses H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated algorithm substitutes (e.g., HDBSCAN for DBSCAN) that test the same question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml12-code\", \"requirements\": \"The clustering conditions and dataset generators are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml12-code-algos\", \"requirements\": \"The submission implements multiple distinct clustering conditions — typically k-means, agglomerative, DBSCAN, and/or spectral — via sklearn APIs with separate code paths and method-specific hyperparameters.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml12-code-data\", \"requirements\": \"The submission generates multiple synthetic datasets with varied geometry (e.g., moons, circles, anisotropic blobs), preserves ground-truth labels, and standardizes features before clustering where appropriate.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml12-code-setup\", \"requirements\": \"The experiment framework includes seed control and a consistent per-dataset protocol for cluster-count-dependent methods.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml12-exec\", \"requirements\": \"Execution produces clustering metrics for each condition.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml12-exec-metrics\", \"requirements\": \"Execution outputs a metrics artifact containing numeric adjusted_rand_score (or equivalent) for each implemented condition on at least one dataset.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml12-exec-repeats\", \"requirements\": \"Reported results are aggregated over multiple random seeds per (dataset, condition) with a dispersion estimate. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml12-exec-tuning\", \"requirements\": \"For DBSCAN (or equivalent density-based method), the run documents a reasonable choice of eps/min_samples (either via a small search or a justified fixed value), logged in the artifacts.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml12-results\", \"requirements\": \"Results address H1/H2/H3 directionally with interpretable evidence.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml12-result-h1\", \"requirements\": \"The submission compares ARI of density-based / spectral methods against k-means on nonlinear-geometry datasets and conveys whether they produce meaningfully better cluster assignments — judge directionally against H1.\", \"weight\": 25.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml12-result-h2h3\", \"requirements\": \"The submission conveys dataset-wise ARI outcomes — whether agglomerative-ward is at least competitive with k-means on anisotropic blobs (H2) and whether any single method wins across all datasets (H3).\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml12-result-writeup\", \"requirements\": \"The README or writeup describes setup, key ARI/NMI findings per dataset, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (synthetic-only scope, hyperparameter sensitivity, seed count, metric dependence). No strict word-count requirement.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML12.yaml", "rubric_file": "tasks/ml/rubrics/ML12.json"} +{"id": "ML13", "domain": "ml", "title": "Evaluating kernel choices for Gaussian Process regression on synthetic 1-D and 5-D functions", "topic": "Evaluating kernel choices (RBF, polynomial, Matern-3/2, Matern-5/2) for Gaussian Process regression on synthetic 1-D and 5-D functions with homoscedastic noise", "domains": ["machine-learning", "kernel-methods"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_nll", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 360, "synthesis": "Gaussian Process (GP) regression performance depends strongly on the kernel,\nwhich encodes assumptions about function smoothness and local structure.\nPractitioners often default to the RBF kernel because it is easy to optimize\nand widely available, but this choice can underperform when the target\nfunction has roughness patterns that are better matched by Matern families or\nwhen polynomial trends dominate part of the signal. Since kernel\nmisspecification can be subtle, a short benchmark should compare predictive\nuncertainty quality as well as point error.\n\nA CPU-scale study can be done entirely with sklearn GaussianProcessRegressor\non synthetic datasets where the data-generating process is controlled. In 1-D,\na mildly nonstationary smooth function with additive Gaussian noise can expose\ndifferences in extrapolation and uncertainty tails. In 5-D, a mixed\nsinusoidal-plus-quadratic target can stress anisotropy and interactions while\nremaining fast enough for repeated fitting under multiple random seeds.\n\nTo make claims measurable, the experiment should evaluate at least four kernel\nchoices (RBF, polynomial, Matern-3/2, Matern-5/2) under the same train/test\nsplits, optimizer restarts, and noise model assumptions. Because the topic's\nkey metric is negative log-likelihood, uncertainty calibration quality should\nbe primary; RMSE and R^2 provide supporting evidence for point prediction.\nSeed averaging is important because synthetic splits and optimizer\ninitialization can change marginal likelihood optimization outcomes.\n\nThe core question is whether smoother kernels (RBF, Matern-5/2) consistently\noutperform rougher or mismatched kernels (Matern-3/2, polynomial) on test NLL\nacross both low- and moderate-dimensional synthetic tasks, and whether ranking\nby NLL aligns with ranking by RMSE.\n\n*Which GP kernel among RBF, polynomial, Matern-3/2, and Matern-5/2 yields the best uncertainty-aware generalization (lowest test NLL) on synthetic 1-D and 5-D noisy regression functions?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Across the two synthetic datasets, at least one of {RBF, Matern-5/2} achieves the lowest mean test NLL on each dataset (averaged over >=5 seeds).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The polynomial kernel has mean test NLL at least 10% worse than the best kernel on at least 1 of the 2 datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Kernel ranking by mean test NLL and by mean RMSE is not identical on at least 1 dataset, indicating uncertainty quality and point error disagree.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do RBF, polynomial, Matern-3/2, and Matern-5/2 kernels compare for Gaussian Process regression on synthetic 1-D and 5-D noisy functions when judged primarily by test NLL?\", \"conditions\": [{\"name\": \"gp_rbf\", \"description\": \"GaussianProcessRegressor with ConstantKernel * RBF + WhiteKernel, hyperparameters optimized by log-marginal-likelihood.\"}, {\"name\": \"gp_poly_deg2\", \"description\": \"GaussianProcessRegressor with ConstantKernel * DotProduct^2 (implemented via polynomial feature mapping + DotProduct kernel) + WhiteKernel as a polynomial-trend proxy.\"}, {\"name\": \"gp_matern32\", \"description\": \"GaussianProcessRegressor with ConstantKernel * Matern(nu=1.5) + WhiteKernel.\"}, {\"name\": \"gp_matern52\", \"description\": \"GaussianProcessRegressor with ConstantKernel * Matern(nu=2.5) + WhiteKernel.\"}], \"baselines\": [\"gp_rbf as the common default-kernel baseline\", \"gp_poly_deg2 as a mismatched-trend baseline\"], \"metrics\": [{\"name\": \"test_nll\", \"direction\": \"minimize\", \"description\": \"Mean negative log predictive density on held-out test data using GP predictive mean and variance, averaged over seeds.\"}, {\"name\": \"rmse\", \"direction\": \"minimize\", \"description\": \"Root mean squared error on test targets, averaged over seeds.\"}, {\"name\": \"r2\", \"direction\": \"maximize\", \"description\": \"Coefficient of determination on test targets, averaged over seeds.\"}], \"datasets\": [{\"name\": \"synthetic_1d_sinmix\", \"source\": \"Generated with numpy: x in [-3,3], y = sin(2x) + 0.3x + epsilon, epsilon~N(0, 0.15^2).\"}, {\"name\": \"synthetic_5d_mixed\", \"source\": \"Generated with numpy: X in [-2,2]^5, y = sin(x1) + 0.5*x2^2 - 0.7*x3 + 0.3*x4*x5 + epsilon, epsilon~N(0, 0.2^2).\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 360}}", "requirements": "", "rubric": "{\"id\": \"ml13-root\", \"requirements\": \"A credible experiment studying kernel-choice effects (RBF, polynomial, Matern-3/2, Matern-5/2, or equivalents) for Gaussian Process regression on synthetic noisy functions: conditions are implemented, execution covers multiple datasets with repeated seeds, and results address H1/H2/H3 directionally using test NLL as the primary metric.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative kernels or dataset dimensions that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml13-code\", \"requirements\": \"The GP kernel conditions and synthetic datasets are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml13-code-kernels\", \"requirements\": \"The submission implements multiple kernel conditions — typically including RBF, a polynomial kernel, and one or more Matern kernels — as distinct GP configurations with comparable noise handling.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml13-code-datasets\", \"requirements\": \"The submission generates synthetic regression datasets with explicit noise (both a low-dimensional and a higher-dimensional case preferred) and creates train/test splits.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml13-code-setup\", \"requirements\": \"Experimental setup controls are consistent across kernels (same split protocol, seed handling, and optimizer restart policy), enabling fair kernel comparison.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml13-exec\", \"requirements\": \"Execution logs primary and secondary metrics for each kernel and dataset.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml13-exec-metrics\", \"requirements\": \"Execution produces a metrics artifact containing numeric test NLL and RMSE (or equivalents) for every implemented (kernel, dataset) pair.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml13-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (kernel, dataset) cell, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml13-results\", \"requirements\": \"Results analysis addresses H1/H2/H3 directionally with quantitative comparisons.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml13-result-h1\", \"requirements\": \"The submission reports per-dataset mean test-NLL ranking and conveys whether smooth kernels (RBF, Matern-5/2) tend to be best — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml13-result-h2\", \"requirements\": \"The submission compares the polynomial-kernel NLL against the best kernel and conveys whether polynomial is meaningfully worse on at least one dataset (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml13-result-h3\", \"requirements\": \"The submission compares kernel rankings by test NLL and RMSE on each dataset and conveys whether rankings can differ between these two metrics (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml13-result-writeup\", \"requirements\": \"The README or writeup describes implementation choices, dataset generation, metric tables, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (synthetic-only scope, kernel parameterization, seed/compute constraints). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML13.yaml", "rubric_file": "tasks/ml/rubrics/ML13.json"} +{"id": "ML14", "domain": "ml", "title": "Comparing split-conformal, Mondrian conformal, and CQR-style intervals on heteroscedastic regression", "topic": "Comparing conformal prediction procedures (split-conformal, Mondrian, CQR) for achieving target coverage on heteroscedastic regression tasks", "domains": ["machine-learning", "uncertainty-quantification"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "coverage_gap", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Conformal prediction provides finite-sample coverage guarantees with minimal\nassumptions, making it attractive for uncertainty quantification in practical\nregression. However, standard split-conformal intervals are often globally\ncalibrated and can be inefficient when noise is heteroscedastic: they may\nover-cover low-noise regions and under-cover high-noise regions. This\nmotivates conditional variants such as Mondrian conformal (group-conditional\ncalibration) and CQR-style approaches that adapt interval width through\nquantile modeling before conformal correction.\n\nIn small, CPU-only settings, one can still run a meaningful comparison by\nusing sklearn-compatible regressors and synthetic data where heteroscedastic\nstructure is controlled. A rigorous setup should evaluate not only marginal\ncoverage but also efficiency (mean interval width) and conditional behavior\n(coverage gap across strata of predicted difficulty or input regions). These\ndiagnostics reveal when methods achieve nominal coverage by producing overly\nwide intervals versus genuinely adapting to varying noise.\n\nA credible benchmark should include at least one homoscedastic control and at\nleast one heteroscedastic dataset, then compare split-conformal, a Mondrian\nvariant (binning by an auxiliary score such as |x| or predicted scale), and a\nCQR-style method based on lower/upper quantile regressors plus conformal\nadjustment. Repeated random splits (multiple seeds) are needed because\nconformal procedures can vary with calibration sample composition.\n\nThe key question is whether adaptive procedures can reduce conditional\nmiscoverage while maintaining near-target marginal coverage and reasonable\ninterval width under strict runtime constraints.\n\n*Do Mondrian and CQR-style conformal procedures achieve lower conditional coverage error than vanilla split-conformal at comparable marginal coverage on heteroscedastic regression tasks?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On heteroscedastic datasets, at least one adaptive method (Mondrian or CQR-style) attains a lower absolute coverage gap to the 90% target than split-conformal by at least 0.02 on at least 2 of 3 datasets, averaged over ≥5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At matched target 90% coverage, CQR-style conformal yields mean interval width no larger than split-conformal on at least 2 of 3 datasets (difference ≤ 0.00).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Mondrian conformal reduces worst-bin conditional miscoverage (max over 4 bins of |bin_coverage-0.90|) by at least 0.03 versus split-conformal on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Do adaptive conformal procedures (Mondrian, CQR-style) improve coverage quality and efficiency over split-conformal for heteroscedastic regression under a fixed 90% target coverage?\", \"conditions\": [{\"name\": \"split_conformal_rf\", \"description\": \"Vanilla split-conformal regression using RandomForestRegressor point predictor and absolute residual conformity scores on a held-out calibration split.\"}, {\"name\": \"mondrian_conformal_rf_4bins\", \"description\": \"Mondrian split-conformal with the same base predictor, calibration and test points partitioned into 4 bins by an auxiliary score (e.g., fitted value or |x0| proxy), with per-bin quantiles.\"}, {\"name\": \"cqr_gbr\", \"description\": \"CQR-style method using GradientBoostingRegressor quantile models (alpha=0.05, 0.95) and split-conformal correction on calibration residuals of quantile bands.\"}, {\"name\": \"naive_quantile_no_conformal\", \"description\": \"Non-conformal baseline using raw quantile regression interval [q0.05, q0.95] without conformal adjustment.\"}], \"baselines\": [\"split_conformal_rf is the primary conformal baseline\", \"naive_quantile_no_conformal is the non-conformal baseline\"], \"metrics\": [{\"name\": \"coverage_gap\", \"direction\": \"minimize\", \"description\": \"Absolute difference between empirical marginal coverage and target 0.90 on the test split, averaged over seeds.\"}, {\"name\": \"mean_interval_width\", \"direction\": \"minimize\", \"description\": \"Average prediction interval width on test samples, averaged over seeds.\"}, {\"name\": \"worst_bin_miscoverage\", \"direction\": \"minimize\", \"description\": \"Maximum across 4 bins of absolute deviation between bin coverage and 0.90, measuring conditional coverage disparity.\"}], \"datasets\": [{\"name\": \"hetero_sine\", \"source\": \"synthetic: y = sin(2πx) + (0.1 + 0.5|x|)ε, x~Uniform(-1,1), n≈2000\"}, {\"name\": \"hetero_friedman1\", \"source\": \"synthetic: sklearn.datasets.make_friedman1 with multiplicative noise scale depending on x0\"}, {\"name\": \"diabetes\", \"source\": \"sklearn.datasets.load_diabetes (tabular regression benchmark)\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml14-root\", \"requirements\": \"A credible experiment comparing split-conformal, Mondrian conformal, CQR-style, and optionally naive-quantile prediction intervals for ~90% target coverage on heteroscedastic regression: methods are implemented, executed on multiple datasets with multiple seeds, and results address H1/H2/H3 directionally with coverage-gap-focused analysis.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative conformal variants (e.g., jackknife+, locally adaptive) that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml14-code\", \"requirements\": \"The conformal and baseline interval-construction conditions are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml14-code-methods\", \"requirements\": \"The submission implements multiple distinct conditions — typically including split conformal, a Mondrian/group-adaptive variant, and a CQR-style or naive-quantile baseline — as separate code paths, not a single shared interval formula.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml14-code-conformal-splits\", \"requirements\": \"Conformal methods use explicit train/calibration/test separation (or equivalent cross-fit logic) so calibration quantiles are computed on data not used to fit base regressors.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml14-code-datasets\", \"requirements\": \"The submission uses multiple datasets (including at least one heteroscedastic synthetic dataset) and prepares regression targets correctly.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml14-exec\", \"requirements\": \"Execution outputs interval-quality metrics for each condition.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml14-exec-metrics\", \"requirements\": \"Execution produces a metrics artifact containing numeric coverage gap and mean interval width (or equivalents) for each implemented condition on at least one dataset.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml14-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (condition, dataset) cell with a dispersion estimate. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml14-exec-conditional\", \"requirements\": \"Execution computes a conditional-coverage diagnostic (e.g., worst-bin miscoverage across several bins) for the conformal methods.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml14-results\", \"requirements\": \"Quantitative analysis addresses H1/H2/H3 directionally and discusses trade-offs between coverage, conditional validity, and interval efficiency.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml14-result-h1\", \"requirements\": \"The submission compares coverage gap of adaptive methods (Mondrian, CQR-style) against plain split conformal per dataset and conveys whether adaptive methods are meaningfully closer to the nominal coverage — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml14-result-h2\", \"requirements\": \"The submission evaluates whether CQR-style intervals achieve mean interval width comparable to or narrower than plain split conformal on most datasets and conveys an H2 outcome.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml14-result-h3\", \"requirements\": \"The submission compares worst-bin miscoverage for Mondrian conformal vs plain split conformal and conveys whether Mondrian yields a meaningful improvement in conditional coverage (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml14-result-writeup\", \"requirements\": \"The README or writeup describes methods and datasets, reports key metric values (coverage gap, interval width, worst-bin miscoverage), conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (finite seeds, binning choice, synthetic-to-real transfer). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML14.yaml", "rubric_file": "tasks/ml/rubrics/ML14.json"} +{"id": "ML15", "domain": "ml", "title": "Filter-based vs embedded L1 feature selection with injected noise features", "topic": "Investigating filter-based feature selection (mutual_info_classif, chi2, f_classif) vs embedded L1-logistic selection on UCI classification datasets with injected irrelevant features", "domains": ["machine-learning", "feature-selection"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Feature selection on tabular classification problems is often presented as a\nchoice between simple univariate filters and embedded sparse models.\nUnivariate filters such as chi-squared, ANOVA F-score, and mutual\ninformation are computationally cheap and easy to apply in a pipeline, but\nthey evaluate each feature independently and can miss interactions. Embedded\nL1-regularized logistic regression performs selection during model fitting,\npotentially yielding feature subsets that better align with predictive\nstructure when many irrelevant variables are present.\n\nA practical stress test is to inject synthetic irrelevant features into\notherwise standard benchmark datasets. This setup creates controlled feature\ndilution while preserving original labels and base difficulty. Under such\ndilution, methods that can ignore noise should maintain accuracy and avoid\nselecting many synthetic features. Conversely, methods sensitive to spurious\nunivariate associations may degrade as noise dimension increases.\n\nA credible CPU-only study should compare at least three filter selectors\n(mutual_info_classif, chi2, f_classif) and an embedded L1 selector in a\nshared downstream classifier pipeline, evaluate on multiple sklearn\nclassification datasets, and report both predictive performance and selection\nquality. Including multiple random seeds is important because both noise\ninjection and train/test splits can change apparent selector rankings.\n\nThe key aim is not to reproduce a single published table, but to determine\nwhether embedded sparsity offers robustness advantages over filters as\nirrelevant dimensions grow, and whether that robustness generalizes across\ndatasets with different feature scales and class structures.\n\n*Does embedded L1-logistic feature selection retain predictive performance and reject injected irrelevant features more reliably than univariate filter methods under controlled feature-noise injection?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"With 5x injected irrelevant features (relative to original feature count), L1-logistic embedded selection achieves higher mean test_accuracy than each filter method (mutual_info_classif, chi2, f_classif) on at least 2 of 3 datasets, averaged over >=5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At fixed selected-feature budget k=20 (or all features if p<20), L1-logistic selects a lower fraction of injected irrelevant features than the average of the three filter methods on all evaluated datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"From no-injection to 5x-injection settings, mean test_accuracy drop for L1-logistic is <= 3 percentage points on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does embedded L1-logistic feature selection outperform univariate filter methods in robustness to injected irrelevant features on sklearn tabular classification datasets?\", \"conditions\": [{\"name\": \"filter_mutual_info_k20\", \"description\": \"SelectKBest(mutual_info_classif, k=20 or p if p<20) followed by LogisticRegression classifier.\"}, {\"name\": \"filter_chi2_k20\", \"description\": \"MinMax scaling to nonnegative domain, then SelectKBest(chi2, k=20 or p if p<20) followed by LogisticRegression classifier.\"}, {\"name\": \"filter_f_classif_k20\", \"description\": \"SelectKBest(f_classif, k=20 or p if p<20) followed by LogisticRegression classifier.\"}, {\"name\": \"embedded_l1_logistic\", \"description\": \"L1-penalized LogisticRegression (saga/liblinear) used as embedded selector; keep top-20 by absolute coefficient magnitude (or nonzero if <=20), retrain LogisticRegression on selected subset.\"}], \"baselines\": [\"filter_f_classif_k20 as a standard univariate linear-statistic baseline\", \"filter_mutual_info_k20 as a nonlinear dependency baseline\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Held-out test accuracy averaged over >=5 seeds for each dataset and injection level.\"}, {\"name\": \"noise_feature_selection_rate\", \"direction\": \"minimize\", \"description\": \"Fraction of selected features that come from injected irrelevant columns.\"}, {\"name\": \"accuracy_drop_0x_to_5x_pp\", \"direction\": \"minimize\", \"description\": \"Absolute percentage-point drop in test_accuracy from 0x to 5x injection.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"digits\", \"source\": \"sklearn.datasets.load_digits\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml15-root\", \"requirements\": \"A credible experiment studying filter-based feature selection (mutual information, chi-square, ANOVA F) versus embedded L1-logistic selection under injected irrelevant features: conditions are implemented, runs cover multiple datasets with multiple seeds and injection levels, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative filter methods or embedded selectors (e.g., tree-based importance) that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml15-code\", \"requirements\": \"Feature-selection conditions and the noise-injection pipeline are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml15-code-conditions\", \"requirements\": \"The submission implements multiple distinct selection conditions — typically including mutual information, chi-square, ANOVA F, and L1-logistic (or a comparable embedded method) — rather than reusing one selector for all conditions.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml15-code-injection\", \"requirements\": \"The pipeline injects synthetic irrelevant features at one or more defined levels (including a no-injection baseline and a higher-injection case) and tracks which columns are injected so a noise-selection rate can be computed.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml15-code-datasets\", \"requirements\": \"The submission uses multiple datasets (sklearn built-ins or comparable) and a reasonable train/test split.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml15-exec\", \"requirements\": \"Execution produces metrics across selectors and injection levels.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml15-exec-metrics\", \"requirements\": \"Execution outputs a structured metrics artifact containing numeric test accuracy and a noise-selection-rate style measure for each implemented condition on at least one dataset with at least two injection levels.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml15-exec-seeds\", \"requirements\": \"Reported metrics are aggregated over multiple random seeds per (dataset, condition, injection level) cell with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml15-results\", \"requirements\": \"Results address H1/H2/H3 directionally with a clear narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml15-result-h1\", \"requirements\": \"The submission compares mean test accuracy at the higher injection level between the embedded L1-logistic method and each filter method per dataset and conveys whether L1 tends to be better — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml15-result-h2\", \"requirements\": \"The submission reports the noise-selection rate for each method and conveys whether the embedded method selects fewer irrelevant features than the filter-method average on most datasets (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml15-result-h3\", \"requirements\": \"The submission reports the accuracy drop from low-injection to high-injection for the embedded method and conveys whether the drop is small on most datasets (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml15-result-writeup\", \"requirements\": \"The README or writeup describes the experimental setup, reports key metric tables, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as synthetic-noise realism, k-choice, classifier dependence, and seed/dataset scope. No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML15.yaml", "rubric_file": "tasks/ml/rubrics/ML15.json"} +{"id": "ML16", "domain": "ml", "title": "Bandit algorithm robustness under stationary and drifting reward regimes", "topic": "Comparing multi-armed bandit algorithms (epsilon-greedy, UCB1, Thompson sampling, Exp3) on synthetic Bernoulli and Gaussian arms under stationary and drift regimes", "domains": ["reinforcement-learning", "bandits"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "cumulative_regret", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Multi-armed bandit algorithms encode different assumptions about reward\ngeneration and non-stationarity. Epsilon-greedy is simple and adaptable with\npersistent exploration; UCB1 is optimism-driven and often strong in\nstationary stochastic settings; Thompson sampling can be highly sample\nefficient when model assumptions are matched; Exp3 is designed for\nadversarial settings and may trade off stochastic efficiency for robustness.\nIn small CPU-constrained studies, these differences are often discussed\nqualitatively but not tested with matched synthetic environments.\n\nA meaningful benchmark should evaluate both Bernoulli and Gaussian reward\narms, because posterior/model assumptions and noise scale can materially\nchange outcomes. It should also include stationary and drifting regimes:\nmethods tuned for fixed means can degrade when the best arm changes over\ntime. Regret, not just final reward, is the right primary metric because it\ncaptures online learning efficiency throughout the horizon.\n\nA credible experiment here implements the four algorithms with consistent\naction/reward interfaces, runs repeated simulations over multiple seeds, and\nreports cumulative regret trajectories and endpoint statistics. To keep the\nstudy CPU-friendly, horizons and arm counts should be modest (e.g., 5-10 arms,\n500-2000 steps), with vectorized numpy updates where possible.\n\nThe key analytical value is comparative: identify where UCB1/Thompson excel\nin stationary stochastic settings, and whether epsilon-greedy or Exp3 is more\nresilient under drift. The objective is not reproducing a single paper, but\ntesting algorithm-environment fit under controlled synthetic shifts.\n\n*How do epsilon-greedy, UCB1, Thompson sampling, and Exp3 trade off cumulative regret across Bernoulli vs Gaussian arms and stationary vs drifting reward regimes?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"In stationary Bernoulli environments, either UCB1 or Thompson sampling achieves at least 10% lower mean cumulative regret than epsilon-greedy at horizon T on at least 2 of 3 datasets (averaged over >=20 seeds).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"In drifting environments, epsilon-greedy or Exp3 achieves lower mean cumulative regret than UCB1 on at least 2 of 3 datasets at horizon T (averaged over >=20 seeds).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Across all evaluated datasets, no single algorithm is best (lowest mean cumulative regret) on every dataset, indicating environment-dependent performance.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do epsilon-greedy, UCB1, Thompson sampling, and Exp3 compare in cumulative regret across stationary versus drifting synthetic bandit tasks with Bernoulli and Gaussian rewards?\", \"conditions\": [{\"name\": \"epsilon_greedy_eps0.1\", \"description\": \"Epsilon-greedy with constant epsilon=0.1 and sample-mean value estimates.\"}, {\"name\": \"ucb1\", \"description\": \"UCB1 with exploration bonus sqrt(2 log t / n_a).\"}, {\"name\": \"thompson_sampling\", \"description\": \"Thompson sampling using Beta-Bernoulli updates for Bernoulli arms and Gaussian posterior sampling (known variance assumption) for Gaussian arms.\"}, {\"name\": \"exp3_gamma0.07\", \"description\": \"Exp3 with gamma=0.07 and importance-weighted reward estimates.\"}], \"baselines\": [\"epsilon_greedy_eps0.1 as simple stochastic baseline\", \"ucb1 as canonical optimism baseline\"], \"metrics\": [{\"name\": \"cumulative_regret\", \"direction\": \"minimize\", \"description\": \"Mean cumulative regret at final horizon T relative to oracle best arm per round, averaged over seeds.\"}, {\"name\": \"instantaneous_regret_auc\", \"direction\": \"minimize\", \"description\": \"Area under per-round regret curve over time (lower is better).\"}, {\"name\": \"best_arm_selection_rate\", \"direction\": \"maximize\", \"description\": \"Fraction of rounds selecting the current optimal arm (for drift: time-varying optimum).\"}], \"datasets\": [{\"name\": \"bernoulli_stationary_k10\", \"source\": \"synthetic: 10 Bernoulli arms with fixed means sampled in [0.05, 0.95] and sorted gap >=0.05\"}, {\"name\": \"bernoulli_drift_k10\", \"source\": \"synthetic: 10 Bernoulli arms with piecewise-constant means; best arm switches at predefined changepoints\"}, {\"name\": \"gaussian_drift_k8\", \"source\": \"synthetic: 8 Gaussian arms (sigma=1) with linearly drifting means and periodic rank reversals\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml16-root\", \"requirements\": \"A credible experiment comparing bandit algorithms (epsilon-greedy, UCB1, Thompson sampling, Exp3, or equivalents) under stationary and drifting synthetic regimes: algorithms are implemented with a common interface, experiments cover multiple environments with repeated seeds, and results address H1/H2/H3 directionally using cumulative regret.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated algorithm variants (e.g., UCB-V, linear Thompson) should be credited when they test the same scientific question.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml16-code\", \"requirements\": \"The bandit algorithms and synthetic environments are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml16-code-algos\", \"requirements\": \"The submission implements multiple distinct algorithm code paths — typically including epsilon-greedy, UCB1, Thompson sampling, and/or Exp3 — with per-round action selection and update logic that are not identical wrappers.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml16-code-envs\", \"requirements\": \"The submission defines multiple synthetic bandit environments including at least one stationary and one drifting regime, with reproducible seed control and oracle best-arm rewards per round for regret computation.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml16-code-thompson-exp3\", \"requirements\": \"If Thompson sampling and/or Exp3 are included, implementation uses sampling-based posterior decisions (Thompson) and probability-weighted action selection with importance-weighted updates (Exp3), rather than greedy mean selection.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml16-exec\", \"requirements\": \"Execution produces regret metrics across algorithms and datasets.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml16-exec-runs\", \"requirements\": \"Execution runs multiple seeds per (algorithm, dataset) cell for multiple environments and logs final cumulative-regret values with mean and dispersion. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml16-exec-artifacts\", \"requirements\": \"A machine-readable results artifact is produced containing dataset-wise metrics for each implemented algorithm, including cumulative regret.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml16-results\", \"requirements\": \"Findings address H1/H2/H3 directionally and summarize implications.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml16-result-h1\", \"requirements\": \"The submission compares stationary-regime cumulative regret between epsilon-greedy and {UCB1, Thompson} and conveys whether the principled algorithms are meaningfully better — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml16-result-h2\", \"requirements\": \"The submission evaluates drifting-regime cumulative regret and conveys whether more exploratory algorithms (epsilon-greedy or Exp3) outperform UCB1 on most drifting datasets (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml16-result-h3\", \"requirements\": \"The submission conveys whether any single algorithm dominates across all environments or whether winners are mixed (H3), with supporting tables or summaries.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml16-result-writeup\", \"requirements\": \"The README or writeup describes setup, reports key cumulative-regret results per environment, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (horizon length, hyperparameter sensitivity, synthetic-only scope). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML16.yaml", "rubric_file": "tasks/ml/rubrics/ML16.json"} +{"id": "ML17", "domain": "ml", "title": "Topic-model comparison on small 20newsgroups subsets: LDA vs NMF vs LSA", "topic": "Comparing topic models (LDA, NMF, LSA) on a small subset of 20newsgroups by topic coherence (c_v) and document-cluster adjusted rand score", "domains": ["natural-language-processing", "topic-modeling"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "coherence_cv", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 720, "synthesis": "Topic modeling methods are often compared using either intrinsic quality\nmeasures (such as topic coherence) or extrinsic utility (such as how well\ndocument representations align with known labels), but the two views can\ndisagree. Latent Dirichlet Allocation (LDA), Non-negative Matrix\nFactorization (NMF), and Latent Semantic Analysis (LSA/SVD) each induce\ndifferent assumptions over term-document structure and may therefore rank\ndifferently depending on metric choice.\n\nOn CPU-constrained benchmarks, a practical study should use a compact text\ncorpus and a controlled preprocessing pipeline so that runtime stays short\nwhile still yielding meaningful distinctions. A small subset of\n20newsgroups is ideal because it has accessible labels for external\nclustering evaluation and enough lexical diversity for coherence analysis.\nUsing multiple subset difficulties (well-separated vs more confusable\ncategories) helps test robustness of conclusions.\n\nA credible experiment compares LDA, NMF, and LSA under matched topic counts\nand vectorization settings, then reports both c_v-like coherence and\nadjusted rand index (ARI) from document-cluster assignments against true\nnewsgroup labels. Because exact c_v implementations are not native in\nsklearn, an explicit approximation based on sliding-window co-occurrence\nand normalized PMI should be documented and applied consistently across\nmethods. Repeated runs for stochastic models are needed to avoid\nover-interpreting single-seed noise.\n\nThe core question is whether better intrinsic coherence implies better\nlabel alignment on small real-world corpora, and which model offers the\nbest trade-off under strict CPU budgets.\n\n*Do LDA, NMF, and LSA produce different rankings on coherence versus ARI on small 20newsgroups subsets, and does NMF provide the strongest coherence-structure trade-off under CPU limits?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"NMF achieves higher mean coherence_cv than LSA on at least 2 of 3 evaluated 20newsgroups subsets when using the same number of topics and preprocessing pipeline.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Across the three methods (LDA, NMF, LSA), the method with the highest coherence_cv is also the highest-ARI method on no more than 1 of the 3 subsets, indicating weak metric agreement.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"LDA achieves mean ARI at least 0.03 higher than LSA on at least 2 of 3 subsets when document clusters are obtained by argmax topic assignment.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do LDA, NMF, and LSA compare on coherence_cv versus document-cluster ARI over small 20newsgroups subsets, and do intrinsic/extrinsic rankings diverge?\", \"conditions\": [{\"name\": \"lda_k4\", \"description\": \"LatentDirichletAllocation with n_components=4, max_iter=20, learning_method='batch', CountVectorizer features, repeated over 3 seeds.\"}, {\"name\": \"nmf_k4\", \"description\": \"NMF with n_components=4, init='nndsvda', solver='cd', max_iter=300 on TF-IDF features, repeated over 3 seeds.\"}, {\"name\": \"lsa_k4\", \"description\": \"TruncatedSVD (LSA) with n_components=4 on TF-IDF features; document-topic embeddings clustered via argmax after non-negative shift or via KMeans(k=4) with fixed seed.\"}, {\"name\": \"nmf_k6_sensitivity\", \"description\": \"NMF sensitivity condition with n_components=6 to test topic-count robustness for coherence and ARI trends.\"}], \"baselines\": [\"lsa_k4 as a linear-algebra baseline without probabilistic topic assumptions\", \"lda_k4 as a probabilistic-topic baseline\"], \"metrics\": [{\"name\": \"coherence_cv\", \"direction\": \"maximize\", \"description\": \"Approximate c_v coherence computed from top words per topic using sliding-window co-occurrence and NPMI aggregation, averaged across topics and seeds.\"}, {\"name\": \"ari\", \"direction\": \"maximize\", \"description\": \"Adjusted Rand Index between predicted document clusters (topic argmax or equivalent) and true newsgroup labels.\"}, {\"name\": \"runtime_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock training + inference time per condition/dataset cell on single CPU core.\"}], \"datasets\": [{\"name\": \"20ng_easy4\", \"source\": \"sklearn.datasets.fetch_20newsgroups with categories=['sci.space','rec.autos','comp.graphics','talk.politics.misc']\"}, {\"name\": \"20ng_related4\", \"source\": \"sklearn.datasets.fetch_20newsgroups with categories=['comp.graphics','comp.os.ms-windows.misc','comp.sys.ibm.pc.hardware','comp.sys.mac.hardware']\"}, {\"name\": \"20ng_science4\", \"source\": \"sklearn.datasets.fetch_20newsgroups with categories=['sci.space','sci.med','sci.electronics','sci.crypt']\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 720}}", "requirements": "", "rubric": "{\"id\": \"ml17-root\", \"requirements\": \"A credible experiment comparing LDA, NMF, and LSA topic models on small 20newsgroups subsets: conditions are implemented, execution reports coherence and ARI on multiple subsets with repeated seeds, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative topic-model implementations or coherence approximations that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml17-code\", \"requirements\": \"Topic-model conditions and dataset pipeline are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml17-code-models\", \"requirements\": \"The submission implements LDA, NMF, and LSA (or equivalents such as a truncated SVD for LSA) as distinct modeling code paths with a matched topic-count setting.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml17-code-data\", \"requirements\": \"The submission loads multiple 20newsgroups subsets (or comparable document corpora) with consistent text preprocessing/vectorization documented in code.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml17-code-metrics-impl\", \"requirements\": \"The code computes a coherence-style metric (e.g., c_v or a documented approximation) and ARI from document-cluster assignments for each method.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml17-exec\", \"requirements\": \"Execution produces comparable numeric outputs for all implemented methods.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml17-exec-runs\", \"requirements\": \"Execution evaluates the core methods on multiple datasets and writes per-method, per-dataset coherence and ARI values to a machine-readable artifact.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml17-exec-seeds-runtime\", \"requirements\": \"Execution repeats stochastic methods with multiple seeds per dataset, reports dispersion, and logs a wall-clock timing measure per condition. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml17-results\", \"requirements\": \"Quantitative analysis addresses H1/H2/H3 directionally.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml17-result-h1\", \"requirements\": \"The submission compares NMF vs LSA on coherence for each evaluated dataset and conveys whether NMF tends to yield better coherence — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml17-result-h2\", \"requirements\": \"The submission checks ranking agreement between coherence and ARI across methods per dataset and conveys whether coherence-ranking and ARI-ranking tend to diverge (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml17-result-h3\", \"requirements\": \"The submission reports LDA vs LSA ARI deltas per dataset and conveys whether LDA tends to achieve meaningfully higher document-cluster ARI (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml17-result-writeup\", \"requirements\": \"The README or writeup describes methods and preprocessing, reports coherence/ARI/runtime results, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (subset choice, coherence approximation validity, seed count, clustering-assignment assumptions). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML17.yaml", "rubric_file": "tasks/ml/rubrics/ML17.json"} +{"id": "ML18", "domain": "ml", "title": "Post-hoc calibration methods for sklearn classifiers on tabular benchmarks", "topic": "Evaluating probability calibration methods (Platt scaling, isotonic regression, temperature scaling) applied to scikit-learn classifiers (RandomForest, GBM, SVM-RBF) on UCI benchmarks", "domains": ["machine-learning", "calibration"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "ece", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 480, "synthesis": "Many widely used scikit-learn classifiers optimize discrimination rather\nthan probability quality. Random forests, gradient boosting models, and\nRBF-kernel SVMs can achieve strong accuracy while output probabilities are\nmiscalibrated, especially on small or moderately imbalanced tabular data.\nThis matters in decision settings where thresholds, risk ranking, or cost-\nsensitive actions depend on reliable confidence estimates.\n\nPost-hoc calibration methods are attractive because they can be layered onto\nexisting models without retraining the base learner. Platt scaling fits a\nsigmoid map, isotonic regression fits a non-parametric monotone map, and\ntemperature scaling applies a single-parameter logit rescaling (implemented\nfor binary tasks via optimization on held-out logits/probabilities). These\nmethods differ in flexibility and overfitting risk, so their relative value\nmay depend on classifier family and dataset size.\n\nA credible CPU-scale study should compare uncalibrated outputs versus at\nleast three calibrators across multiple sklearn tabular datasets, using a\nproper train/calibration/test protocol and repeated seeds. Because this is a\ncalibration-centric question, expected calibration error (ECE) and log loss\nshould be primary metrics, with accuracy used as a guardrail to ensure that\ncalibration does not degrade classification utility.\n\nThe goal is not to reproduce a single paper result but to test whether any\none calibrator is consistently superior across heterogeneous models and\ndatasets under tight compute constraints. The key uncertainty is whether\nmethod ranking is stable enough to justify a default choice.\n\n*Which post-hoc calibration method (Platt, isotonic, temperature) most reliably improves ECE across RF/GBM/SVM-RBF on small tabular sklearn benchmarks without materially harming accuracy?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At least one post-hoc calibrator (Platt, isotonic, or temperature) reduces ECE by at least 10% relative to the uncalibrated model for at least 2 of 3 classifiers on at least 2 of 3 datasets, averaged over >=3 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Isotonic regression achieves lower mean ECE than Platt scaling on at least 2 of 3 datasets when calibration-set size is >=150 samples.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Applying post-hoc calibration changes test accuracy by no more than 1.0 absolute percentage point (mean over seeds) for each classifier-dataset pair.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which post-hoc calibration method (Platt, isotonic, temperature) most consistently improves probability calibration for RF/GBM/SVM-RBF on sklearn tabular datasets while preserving accuracy?\", \"conditions\": [{\"name\": \"uncalibrated\", \"description\": \"Base classifier probabilities/decision scores used directly with no calibration.\"}, {\"name\": \"platt_scaling\", \"description\": \"Sigmoid calibration fit on a held-out calibration split (CalibratedClassifierCV with method='sigmoid' or equivalent).\"}, {\"name\": \"isotonic_regression\", \"description\": \"Isotonic calibration fit on a held-out calibration split (CalibratedClassifierCV with method='isotonic' or equivalent).\"}, {\"name\": \"temperature_scaling\", \"description\": \"Single temperature parameter optimized on calibration split to minimize NLL; applied to logits/scores before probability mapping.\"}], \"baselines\": [\"uncalibrated outputs are the primary baseline\", \"platt_scaling serves as a classic parametric calibration baseline\"], \"metrics\": [{\"name\": \"ece\", \"direction\": \"minimize\", \"description\": \"Expected Calibration Error (10-15 bins) on the held-out test split, averaged over seeds.\"}, {\"name\": \"log_loss\", \"direction\": \"minimize\", \"description\": \"Negative log-likelihood on test probabilities.\"}, {\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Classification accuracy on held-out test split to verify discrimination is preserved.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"digits_binary\", \"source\": \"sklearn.datasets.load_digits with target transformed to binary (e.g., digit<5 vs >=5)\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 480}}", "requirements": "", "rubric": "{\"id\": \"ml18-root\", \"requirements\": \"A credible experiment studying post-hoc probability calibration methods (Platt, isotonic, temperature, or equivalents) for sklearn classifiers: calibration conditions are implemented, execution covers multiple datasets/classifiers with repeated seeds, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated calibrator variants or alternative base classifiers that preserve the scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml18-code\", \"requirements\": \"Calibration methods and classifier setup are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml18-code-calibrators\", \"requirements\": \"The submission implements multiple calibration conditions — typically including uncalibrated, Platt, isotonic, and temperature scaling — as distinct code paths applied post-hoc to the same base model outputs.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml18-code-models\", \"requirements\": \"The experiment includes multiple target classifiers (e.g., RandomForest, gradient boosting, SVM-RBF, or equivalents) with consistent train/calibration/test handling.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml18-code-datasets\", \"requirements\": \"The submission uses multiple datasets (sklearn built-ins or comparable) and an explicit calibration split separate from train and test.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml18-exec\", \"requirements\": \"Execution reports calibration-focused metrics across conditions.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml18-exec-metrics\", \"requirements\": \"Execution outputs numeric ECE, log loss, and test accuracy (or equivalents) for each implemented condition on at least one dataset-classifier pair in a machine-readable artifact.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml18-exec-seeds\", \"requirements\": \"Metrics are aggregated over multiple random seeds per evaluated cell with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml18-results\", \"requirements\": \"Results quantitatively address H1/H2/H3 directionally with a clear narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml18-result-h1\", \"requirements\": \"The submission computes relative ECE change versus uncalibrated and conveys whether at least one calibrator yields a meaningful ECE reduction across most classifier/dataset pairs — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml18-result-h2\", \"requirements\": \"The submission compares isotonic vs Platt mean ECE per dataset and conveys the relative calibration quality plus any calibration-set size considerations (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml18-result-h3\", \"requirements\": \"The submission reports accuracy deltas between calibrated and uncalibrated outputs per classifier-dataset pair and conveys whether accuracy changes are small (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml18-result-writeup\", \"requirements\": \"The README or report conveys per-hypothesis outcomes (supported / refuted / inconclusive), cites key ECE/log-loss/accuracy numbers, and discusses limitations (dataset scope, binning sensitivity, seed count, calibration split size). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML18.yaml", "rubric_file": "tasks/ml/rubrics/ML18.json"} +{"id": "ML19", "domain": "ml", "title": "Comparing graph- and pseudo-label-based semi-supervised learning on small tabular datasets", "topic": "Comparing semi-supervised learning strategies (LabelPropagation, LabelSpreading, SelfTrainingClassifier) on small UCI datasets with 10-20% labeled data", "domains": ["machine-learning", "semi-supervised-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "Semi-supervised learning is attractive when labels are scarce but unlabeled\nexamples are cheap. In sklearn, LabelPropagation and LabelSpreading provide\ngraph-based transductive approaches, while SelfTrainingClassifier offers a\npseudo-labeling wrapper around a standard supervised base learner. These\nmethods rely on different assumptions: graph smoothness over neighborhoods\nversus confidence-thresholded iterative self-labeling. On small tabular\ndatasets, their relative behavior can change quickly as the labeled fraction\ndrops from moderate (20%) to very low (10%).\n\nA useful CPU-scale study should compare these methods under the same splits,\nfeature preprocessing, and random seeds, with explicit control of labeled\nfraction. Because the key claim of semi-supervision is label efficiency, the\nexperiment should include a supervised-only baseline trained on the same\nlabeled subset and evaluated on a fixed held-out test set. This makes gains\nattributable to unlabeled-data usage rather than favorable splits.\n\nThe study should report not only test accuracy but also balanced accuracy and\nmacro-F1, since small tabular datasets can have class imbalance and accuracy\nalone may hide minority-class degradation. Repeating runs across several seeds\nis necessary because which points are labeled strongly affects outcomes at low\nlabel rates.\n\nPractically, the benchmark should stay lightweight: sklearn datasets only,\nno downloads, and model choices that complete within minutes on a single CPU\ncore. The focus is not SOTA performance but robust relative comparisons across\nlabel regimes and datasets.\n\n*How do LabelPropagation, LabelSpreading, and SelfTrainingClassifier compare in label-efficiency versus a supervised-only baseline when only 10–20% of training labels are available on small sklearn tabular datasets?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At 10% labeled data, at least one semi-supervised method (LabelPropagation, LabelSpreading, or SelfTrainingClassifier) achieves test accuracy at least 3 percentage points higher than the supervised-only baseline on at least 2 of 3 datasets, averaged over ≥5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Across datasets at 10% labeled data, LabelSpreading with RBF kernel attains mean test accuracy greater than or equal to LabelPropagation with RBF kernel in at least 2 of 3 datasets (seed-averaged).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"For SelfTrainingClassifier, increasing labeled fraction from 10% to 20% improves mean test accuracy by at least 1 absolute percentage point on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do graph-based (LabelPropagation/LabelSpreading) and pseudo-labeling (SelfTrainingClassifier) methods compare to supervised-only training under 10% and 20% labeled-data regimes on small tabular benchmarks?\", \"conditions\": [{\"name\": \"supervised_logreg_labeled_only\", \"description\": \"LogisticRegression trained only on the labeled subset of the training split; unlabeled points ignored.\"}, {\"name\": \"label_propagation_rbf\", \"description\": \"LabelPropagation with RBF kernel; unlabeled training labels set to -1 and transductive predictions used for test inference.\"}, {\"name\": \"label_spreading_rbf\", \"description\": \"LabelSpreading with RBF kernel under the same masked-label protocol.\"}, {\"name\": \"self_training_logreg\", \"description\": \"SelfTrainingClassifier wrapping LogisticRegression with confidence threshold (e.g., 0.8), fit on mixed labeled/unlabeled training data.\"}], \"baselines\": [\"supervised_logreg_labeled_only is the primary baseline\", \"10% labeled regime serves as a lower-label baseline versus 20% within each method\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Classification accuracy on held-out test split, averaged over seeds.\"}, {\"name\": \"balanced_accuracy\", \"direction\": \"maximize\", \"description\": \"Balanced accuracy on held-out test split, averaged over seeds.\"}, {\"name\": \"macro_f1\", \"direction\": \"maximize\", \"description\": \"Macro-averaged F1 score on held-out test split, averaged over seeds.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"digits\", \"source\": \"sklearn.datasets.load_digits\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml19-root\", \"requirements\": \"A credible experiment comparing semi-supervised methods (LabelPropagation, LabelSpreading, SelfTrainingClassifier, or equivalents) against a supervised-only baseline under limited-label regimes: methods are implemented correctly, runs cover multiple datasets with repeated seeds, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated alternative semi-supervised methods that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml19-code\", \"requirements\": \"Semi-supervised and baseline conditions are implemented correctly with explicit label masking.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml19-code-methods\", \"requirements\": \"The submission implements multiple distinct conditions — typically including at least one graph-based method (LabelPropagation or LabelSpreading), a self-training method, and a supervised-only baseline — as separate code paths.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml19-code-masking\", \"requirements\": \"Label masking for semi-supervised runs is explicit and correct: only a defined labeled fraction retains class labels and the remaining training labels are set to the unlabeled marker.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml19-code-datasets\", \"requirements\": \"The submission uses multiple datasets (sklearn built-ins or comparable), with a held-out test split and consistent preprocessing across conditions.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml19-exec\", \"requirements\": \"Execution covers multiple label fractions and produces benchmark metrics.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml19-exec-metrics\", \"requirements\": \"Execution outputs numeric test accuracy and at least one additional metric (e.g., balanced accuracy or macro-F1) for each implemented (condition, dataset, label-fraction) cell in a machine-readable artifact.\", \"weight\": 16.6667, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml19-exec-seeds\", \"requirements\": \"Each reported cell is aggregated over multiple random seeds with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml19-results\", \"requirements\": \"Results address H1/H2/H3 directionally with quantitative comparisons.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml19-result-h1\", \"requirements\": \"The submission compares each semi-supervised method versus the supervised-only baseline at a low labeled fraction and conveys whether any method provides a meaningful gain on most datasets — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml19-result-h2\", \"requirements\": \"The submission reports a direct LabelSpreading vs LabelPropagation comparison per dataset and conveys whether LabelSpreading is at least competitive on most datasets (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml19-result-h3\", \"requirements\": \"The submission reports self-training accuracy at both low and higher labeled fractions and conveys whether the gain with more labels is meaningful (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml19-result-writeup\", \"requirements\": \"The README or writeup describes setup and methods, reports per-dataset metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (dataset scope, seed count variance, sensitivity to masking/hyperparameters). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML19.yaml", "rubric_file": "tasks/ml/rubrics/ML19.json"} +{"id": "ML20", "domain": "ml", "title": "Classical forecaster robustness on synthetic seasonal time series", "topic": "Benchmarking classical time-series forecasters (AR, ARIMA, SARIMAX, ETS, Theta) on synthetic seasonal series with varying noise, trend, and seasonality amplitudes", "domains": ["time-series", "forecasting"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "smape", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 720, "synthesis": "Classical univariate forecasting methods remain widely used because they are\ninterpretable and lightweight, yet their comparative behavior can flip under\ndifferent data-generating regimes. AR models often perform well when dynamics\nare mostly autoregressive and weakly seasonal; ARIMA can absorb trend and\ndifferencing structure; SARIMAX can explicitly represent seasonal lag effects;\nETS captures error-trend-seasonal decomposition; and Theta is often a strong\nlow-variance baseline. In small CPU-constrained settings, practitioners need\npractical guidance about which method is robust to changing signal-to-noise\nand seasonality strength.\n\nA synthetic benchmark is appropriate here because we can precisely control\ntrend slope, seasonal amplitude, and observation noise while keeping compute\nmodest. By generating multiple families of monthly-like series with known\nperiodicity (e.g., period 12), we can evaluate whether model rankings are\nstable or regime-dependent. This avoids overfitting conclusions to one\nreal-world dataset and enables direct stress testing under high-noise versus\nhigh-seasonality conditions.\n\nA credible study should compare at least four of {AR, ARIMA, SARIMAX, ETS,\nTheta} on 2–3 synthetic dataset families, using rolling-origin or holdout\nforecasts with a fixed horizon. It should report scale-robust error metrics\n(especially sMAPE), include at least one baseline (e.g., seasonal naive), and\naggregate over multiple random seeds. The analysis should explicitly test\nwhether one method is consistently best overall or whether the best choice\ndepends on data regime.\n\nThe key outcome is actionable selection logic: if seasonal structure is strong\nand noise is low, do seasonal/state-space methods dominate; and if noise is\nhigh, do simpler methods become competitive? These are concrete decisions an\nautonomous forecasting pipeline can apply when only limited data and CPU are\navailable.\n\n*How do AR, ARIMA, SARIMAX, ETS, and Theta compare in sMAPE robustness across synthetic seasonal series with controlled trend, seasonality amplitude, and noise levels?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"SARIMAX or ETS achieves the lowest mean sMAPE on at least 2 of 3 synthetic dataset families when seasonality amplitude is high (amplitude >= 8) and noise is low (sigma <= 1.0), averaged over >=5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Under high-noise settings (sigma >= 3.0), the sMAPE gap between the best advanced method (ARIMA/SARIMAX/ETS/Theta) and a seasonal_naive baseline is <= 5 percentage points on at least 2 of 3 dataset families.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"No single method among {AR, ARIMA, SARIMAX, ETS, Theta} ranks first in mean sMAPE on all evaluated dataset families, indicating regime-dependent winners.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which classical forecasting method is most robust in sMAPE across controlled synthetic regimes of trend, seasonality amplitude, and noise?\", \"conditions\": [{\"name\": \"ar_lag12\", \"description\": \"Autoregressive model with lag order selected from {3,6,12} by AIC on training split.\"}, {\"name\": \"arima_auto_small\", \"description\": \"Non-seasonal ARIMA with (p,d,q) searched over small grid p,q in [0,2], d in [0,1], selected by AIC.\"}, {\"name\": \"sarimax_seasonal12\", \"description\": \"Seasonal ARIMA/SARIMAX with seasonal period 12 and small grid over (p,d,q)x(P,D,Q), selected by AIC.\"}, {\"name\": \"ets_additive\", \"description\": \"Exponential smoothing with additive trend/seasonality (period 12), damped trend optional by AIC.\"}, {\"name\": \"theta\", \"description\": \"ThetaModel forecast with default theta decomposition for univariate series.\"}], \"baselines\": [\"seasonal_naive (forecast y_t = y_{t-12}) as a simple seasonal baseline\", \"naive_last (random walk) as a non-seasonal baseline\"], \"metrics\": [{\"name\": \"smape\", \"direction\": \"minimize\", \"description\": \"Symmetric Mean Absolute Percentage Error on forecast horizon, averaged over seeds and series instances.\"}, {\"name\": \"mae\", \"direction\": \"minimize\", \"description\": \"Mean Absolute Error on forecast horizon.\"}, {\"name\": \"fit_time_sec\", \"direction\": \"minimize\", \"description\": \"Per-method wall-clock fit+forecast runtime in seconds.\"}], \"datasets\": [{\"name\": \"syn_low_noise_high_season\", \"source\": \"Synthetic monthly series: length 180, period 12, trend slope in [0.0,0.2], season amplitude in [8,12], gaussian noise sigma in [0.5,1.0].\"}, {\"name\": \"syn_medium_noise_medium_season\", \"source\": \"Synthetic monthly series: length 180, period 12, trend slope in [0.1,0.4], season amplitude in [4,8], gaussian noise sigma in [1.5,2.5].\"}, {\"name\": \"syn_high_noise_low_season\", \"source\": \"Synthetic monthly series: length 180, period 12, trend slope in [0.0,0.3], season amplitude in [1,4], gaussian noise sigma in [3.0,4.0].\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 720}}", "requirements": "", "rubric": "{\"id\": \"ml20-root\", \"requirements\": \"A credible experiment benchmarking classical forecasters (AR, ARIMA, SARIMAX, ETS, Theta, or equivalents) on synthetic seasonal series: model conditions are implemented, execution covers multiple synthetic regimes with repeated seeds, and results address H1/H2/H3 directionally using sMAPE-centered analysis.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative forecaster implementations or substitutes that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml20-code\", \"requirements\": \"The forecasting conditions and synthetic data generation pipeline are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml20-code-methods\", \"requirements\": \"The submission implements multiple distinct forecasting methods — typically AR/ARIMA, a seasonal model (SARIMAX or ETS), and Theta or equivalents — as separate code paths, and includes at least one explicit naive/seasonal-naive baseline.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml20-code-synthdata\", \"requirements\": \"The submission generates multiple synthetic seasonal dataset families with controllable seasonality, trend, and noise, each with a train/test split suitable for forecasting.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml20-code-setup\", \"requirements\": \"Forecasting setup uses a fixed holdout horizon (or rolling-origin equivalent) and computes comparable forecasts for every (method, dataset, seed) cell.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml20-exec\", \"requirements\": \"Benchmark execution logs required metrics with repeated trials.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml20-exec-metrics\", \"requirements\": \"Execution produces a structured metrics artifact containing numeric sMAPE and MAE (or equivalents) for each implemented method on at least one dataset family.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml20-exec-seeds\", \"requirements\": \"Each reported method-dataset metric is aggregated over multiple random seeds with dispersion statistics. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml20-exec-runtime\", \"requirements\": \"The run logs per-method wall-clock timing and completes within a CPU-only budget.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml20-results\", \"requirements\": \"Results analysis addresses H1/H2/H3 directionally with quantitative comparisons.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml20-result-h1\", \"requirements\": \"The submission isolates high-seasonality low-noise results and conveys whether SARIMAX or ETS tends to attain the best mean sMAPE — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml20-result-h2\", \"requirements\": \"The submission compares the best advanced method against seasonal-naive under high-noise settings and conveys whether the sMAPE gap collapses to a small margin (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml20-result-h3\", \"requirements\": \"The submission ranks methods by mean sMAPE per dataset family and conveys whether no single method dominates across all families (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml20-result-writeup\", \"requirements\": \"The README or writeup reports key metric tables, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (synthetic realism, grid-search scope, seed count, horizon sensitivity). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML20.yaml", "rubric_file": "tasks/ml/rubrics/ML20.json"} +{"id": "ML21", "domain": "ml", "title": "PC vs GES vs NOTEARS-linear for recovering small Gaussian linear-SEM DAGs", "topic": "Comparing causal structure learning algorithms (PC, GES, NOTEARS-linear) on small synthetic linear-SEM DAGs of 5-15 nodes with Gaussian noise", "domains": ["causal-inference", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "structural_hamming_distance", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 720, "synthesis": "Causal structure learning methods often claim asymptotic guarantees, but in\npractical settings researchers usually face small graphs, finite samples, and\nimperfect hyperparameter choices. For Gaussian linear structural equation\nmodels (SEMs), three widely discussed paradigms are constraint-based (PC),\nscore-based (GES), and continuous optimization (NOTEARS-linear). Even when\ndata are generated from the model class these methods assume, finite-sample\nbehavior can differ sharply in edge recovery and orientation errors.\n\nA compact CPU-scale benchmark can isolate these differences by generating\nsynthetic DAGs with known ground truth over 5-15 nodes, sampling linear-Gaussian\nobservations, and evaluating structural recovery against the true adjacency.\nThis avoids data-download overhead and lets the study vary sample size and\ngraph density in a controlled way. The key metric is Structural Hamming\nDistance (SHD), which penalizes missing, extra, and wrongly oriented edges.\n\nA credible study should implement all three algorithms with transparent\nassumptions: PC via partial-correlation CI tests, GES via greedy score search\n(e.g., BIC), and NOTEARS-linear via a differentiable acyclicity penalty and\nL1 regularization. Multiple random seeds and at least two synthetic regimes\n(sparser/easier vs denser/harder) are needed to reduce variance and avoid\nover-interpreting one-off runs.\n\nBecause these algorithms expose different tuning knobs (significance level,\nregularization strength, score penalties), the comparison should include one\nclear default setting per method plus modest tuning on validation seeds. The\nresulting analysis should report SHD and at least one precision/recall-style\nedge metric, then map outcomes back to explicit hypotheses about relative\nranking and sample-efficiency.\n\n*On small Gaussian linear-SEM DAGs, which of PC, GES, and NOTEARS-linear most reliably minimizes structural recovery error under realistic finite-sample budgets?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At n_samples=1000, NOTEARS-linear achieves lower mean SHD than PC on at least 2 of 3 synthetic DAG regimes (averaged over >=5 random DAG/data seeds per regime).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At n_samples=300, GES achieves mean SHD <= PC in all evaluated regimes, and strictly lower SHD in at least 1 regime.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The best method at n_samples=1000 reduces mean SHD by at least 20% relative to the worst method in at least 2 of 3 regimes.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"For 5-15 node Gaussian linear-SEM DAGs, how do PC, GES, and NOTEARS-linear compare in SHD and edge recovery across sparse/medium/dense synthetic regimes and finite sample sizes?\", \"conditions\": [{\"name\": \"pc_partial_corr\", \"description\": \"Constraint-based PC using Gaussian partial-correlation conditional independence tests with significance level alpha (default 0.01, optional small grid).\"}, {\"name\": \"ges_bic\", \"description\": \"Score-based greedy equivalence search using Gaussian/BIC score with forward-backward edge operations.\"}, {\"name\": \"notears_linear_l1\", \"description\": \"Continuous optimization NOTEARS-linear with squared loss, acyclicity constraint, and L1 sparsity penalty tuned on a small lambda grid.\"}, {\"name\": \"pc_alpha_tuned\", \"description\": \"PC variant selecting alpha from {0.005, 0.01, 0.05} by lowest validation-seed SHD, then evaluated on held-out seeds.\"}], \"baselines\": [\"pc_partial_corr is the classical constraint-based baseline\", \"ges_bic is the classical score-based baseline\"], \"metrics\": [{\"name\": \"shd\", \"direction\": \"minimize\", \"description\": \"Structural Hamming Distance between learned and true DAG adjacency (lower is better), averaged over seeds.\"}, {\"name\": \"edge_f1\", \"direction\": \"maximize\", \"description\": \"F1 score on directed edge existence/orientation against ground truth adjacency.\"}, {\"name\": \"runtime_sec\", \"direction\": \"minimize\", \"description\": \"Per-run wall-clock time in seconds to assess CPU practicality.\"}], \"datasets\": [{\"name\": \"synth_linear_sem_sparse\", \"source\": \"Synthetic 8-node DAGs generated via Erdos-Renyi with expected degree ~1.5; linear weights sampled from [-2,-0.5] U [0.5,2], Gaussian noise.\"}, {\"name\": \"synth_linear_sem_medium\", \"source\": \"Synthetic 12-node DAGs with expected degree ~2.5 using same linear-Gaussian SEM generator.\"}, {\"name\": \"synth_linear_sem_dense\", \"source\": \"Synthetic 15-node DAGs with expected degree ~3.5 using same linear-Gaussian SEM generator.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 720}}", "requirements": "", "rubric": "{\"id\": \"ml21-root\", \"requirements\": \"A credible experiment comparing causal structure learning methods (PC, GES, NOTEARS-linear, or equivalents) on small synthetic Gaussian linear-SEM DAGs: methods are implemented, runs cover multiple synthetic regimes with multiple seeds, and results address H1/H2/H3 directionally using SHD-centered evidence.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative structure-learning methods or linear-SEM variants that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml21-code\", \"requirements\": \"Core causal structure learning conditions and synthetic data generation are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml21-code-methods\", \"requirements\": \"The submission implements distinct code paths for multiple structure learning methods — typically PC, GES, and NOTEARS-linear or equivalents — rather than reusing one estimator under different names.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml21-code-synth\", \"requirements\": \"A reproducible synthetic linear-Gaussian SEM DAG generator is implemented with moderate node counts, including ground-truth adjacency export and sample generation.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml21-code-hparams\", \"requirements\": \"At least one tunable hyperparameter is exposed and used for the constraint-based method (e.g., PC's alpha) and the continuous-optimization method (e.g., NOTEARS' sparsity weight), with documented default or grid values.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml21-exec\", \"requirements\": \"The benchmark produces comparable metrics across methods/regimes.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml21-exec-coverage\", \"requirements\": \"Execution covers multiple synthetic regimes and reports results for the primary methods at one or more sample sizes.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml21-exec-metrics\", \"requirements\": \"A machine-readable results artifact includes numeric SHD for each (method, regime) and at least one additional metric such as edge-F1 or runtime.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml21-exec-seeds\", \"requirements\": \"Each reported cell is averaged over multiple random seeds with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml21-results\", \"requirements\": \"Results analysis addresses H1/H2/H3 directionally.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml21-result-h1\", \"requirements\": \"The submission compares NOTEARS-linear vs PC at a reasonable sample size and conveys whether NOTEARS achieves meaningfully lower SHD on most regimes — judge directionally against H1.\", \"weight\": 25.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml21-result-h2h3\", \"requirements\": \"The submission conveys qualitative verdicts for H2 and H3 using SHD aggregates, including whether larger sample sizes or denser-graph regimes yield meaningful SHD improvements.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml21-result-writeup\", \"requirements\": \"The README or writeup describes setup, reports key SHD/edge-F1/runtime findings, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (synthetic-only scope, small-node regime, hyperparameter sensitivity, finite seeds). No strict word-count requirement.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML21.yaml", "rubric_file": "tasks/ml/rubrics/ML21.json"} +{"id": "ML22", "domain": "ml", "title": "Active learning query strategies for logistic regression on small tabular pools", "topic": "Evaluating active learning query strategies (uncertainty sampling, margin sampling, query-by-committee, expected-error reduction) for logistic regression on small UCI pools", "domains": ["machine-learning", "active-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "accuracy_at_budget", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 600, "synthesis": "Pool-based active learning is attractive when labels are expensive but a\nmodest unlabeled pool is available. For linear probabilistic models such as\nlogistic regression, classic query policies include uncertainty sampling,\nmargin sampling, query-by-committee (QBC), and expected-error reduction\nstyle lookahead approximations. These methods are often taught as broadly\nuseful, but on small tabular datasets their gains can be inconsistent due to\nmodel misspecification, class imbalance, and high variance from tiny initial\nlabeled sets.\n\nA useful CPU-bounded benchmark should compare these query strategies under a\nfixed annotation budget and shared learner, rather than mixing in model\narchitecture changes. The core signal is label efficiency: how quickly test\nperformance improves as labels are acquired. Because final accuracy alone can\nhide early-budget differences, the study should include both\naccuracy-at-budget and a budget-curve summary metric such as area under the\nlearning curve.\n\nThe experiment should therefore run multiple seeds, use at least two\nsklearn-resident tabular classification datasets, and enforce identical\ninitialization, batch size, and stopping budget across strategies. A random\nquery baseline is essential to determine whether sophisticated policies\nprovide real value beyond chance selection. A passive full-data reference is\nalso useful for contextualizing attainable performance ceilings under the\nsame logistic-regression family.\n\nPractical constraints matter: expected-error reduction can be expensive if\nnaively recomputing retrains for every candidate. A tractable variant can\nevaluate a capped candidate subset per round and use one-step hypothetical\nlabel outcomes, preserving the spirit of expected future loss minimization\nwhile staying within single-core runtime limits.\n\n*Do uncertainty, margin, QBC, and approximate expected-error reduction deliver better label efficiency than random sampling for logistic regression on small tabular pools under a fixed annotation budget?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At 30% labeling budget, at least 2 of {uncertainty_sampling, margin_sampling, qbc, expected_error_reduction} achieve higher mean test accuracy than random_sampling by ≥1.5 percentage points, averaged over ≥5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"expected_error_reduction attains the highest mean area_under_learning_curve (AULC) on at least 1 of 3 datasets, and its AULC is not lower than random_sampling on any evaluated dataset.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"qbc outperforms uncertainty_sampling in mean test accuracy at early budget (10% labels) on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which active learning query strategy provides the best label efficiency for logistic regression on small tabular pool-based classification tasks under fixed budgets?\", \"conditions\": [{\"name\": \"random_sampling\", \"description\": \"Baseline pool-based active learning that queries unlabeled points uniformly at random each round.\"}, {\"name\": \"uncertainty_sampling\", \"description\": \"Queries samples with highest predictive entropy from current logistic regression model.\"}, {\"name\": \"margin_sampling\", \"description\": \"Queries samples with smallest top-2 class probability margin (binary: |p-0.5|).\"}, {\"name\": \"qbc\", \"description\": \"Query-by-committee with 5 bootstrapped logistic regression committee members; selects by vote entropy/disagreement.\"}, {\"name\": \"expected_error_reduction\", \"description\": \"Approximate one-step expected-error reduction over a capped candidate subset per round using hypothetical labels and expected future log-loss reduction.\"}], \"baselines\": [\"random_sampling is the primary active-learning baseline\", \"passive_full_data logistic regression reference trained once on all labels for context\"], \"metrics\": [{\"name\": \"accuracy_at_budget\", \"direction\": \"maximize\", \"description\": \"Test accuracy at 30% labeled budget, averaged over seeds.\"}, {\"name\": \"aulc\", \"direction\": \"maximize\", \"description\": \"Area under the test-accuracy-vs-labeled-fraction curve from initial seed set to 30% budget.\"}, {\"name\": \"early_accuracy_10pct\", \"direction\": \"maximize\", \"description\": \"Test accuracy when 10% of pool labels have been acquired.\"}], \"datasets\": [{\"name\": \"breast_cancer\", \"source\": \"sklearn.datasets.load_breast_cancer\"}, {\"name\": \"wine\", \"source\": \"sklearn.datasets.load_wine\"}, {\"name\": \"digits\", \"source\": \"sklearn.datasets.load_digits\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 600}}", "requirements": "", "rubric": "{\"id\": \"ml22-root\", \"requirements\": \"A credible experiment studying active-learning query strategies (random, uncertainty, margin, QBC, expected error reduction, or equivalents) for logistic regression: strategies are implemented as distinct code paths, execution covers multiple datasets with repeated seeds under a fixed budget, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative acquisition functions or base classifiers that preserve the scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml22-code\", \"requirements\": \"Active-learning strategies and shared logistic-regression pipeline are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml22-code-strategies\", \"requirements\": \"The submission implements multiple distinct query conditions — typically random plus several of {uncertainty, margin, QBC, expected error reduction} — with genuinely different acquisition logic.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml22-code-loop\", \"requirements\": \"There is a pool-based active-learning loop with an initial labeled seed set, iterative querying, model retraining/update, and stopping at a defined label budget.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml22-code-datasets\", \"requirements\": \"The submission uses multiple datasets (sklearn built-ins or comparable) with consistent train/pool/test handling for all strategies.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml22-exec\", \"requirements\": \"Execution emits budget-aware performance metrics.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml22-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact containing numeric accuracy-at-budget and area-under-learning-curve (or equivalents) for each implemented strategy on at least one dataset.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml22-exec-seeds\", \"requirements\": \"Each reported (strategy, dataset) result is aggregated over multiple random seeds, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml22-exec-budget-curve\", \"requirements\": \"The run logs performance across multiple budget checkpoints so early-budget accuracy and AULC are computable from recorded traces.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml22-results\", \"requirements\": \"Quantitative analysis addresses H1/H2/H3 directionally.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml22-result-h1\", \"requirements\": \"The submission compares non-random strategies to random sampling at the final budget and conveys whether informative strategies meaningfully outperform random — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml22-result-h2\", \"requirements\": \"The submission reports per-dataset AULC rankings and conveys whether expected-error-reduction (or a comparable principled strategy) is at least competitive, never falling below random (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml22-result-h3\", \"requirements\": \"The submission compares QBC vs uncertainty sampling at an early-budget checkpoint per dataset and conveys a qualitative verdict (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml22-result-writeup\", \"requirements\": \"The README or report describes setup, reports key numeric outcomes, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (runtime approximations, seed count, dataset scope, strategy sensitivity). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML22.yaml", "rubric_file": "tasks/ml/rubrics/ML22.json"} +{"id": "ML23", "domain": "ml", "title": "Pointwise vs pairwise vs listwise learning-to-rank on synthetic query-document benchmarks", "topic": "Comparing learning-to-rank approaches (pointwise linear, pairwise RankNet-lite, listwise ListMLE-lite) on synthetic query-document pairs with known relevance grades", "domains": ["information-retrieval", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "ndcg_at_10", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 480, "synthesis": "Learning-to-rank (LTR) objectives are often grouped into pointwise, pairwise,\nand listwise families, each optimizing a different surrogate of retrieval\nquality. Pointwise methods reduce ranking to regression/classification on\nindividual query-document pairs; pairwise methods optimize relative order\nbetween document pairs; listwise methods directly shape permutations or top-k\nemphasis. In production IR systems, metric alignment (especially with NDCG)\nis often cited as a reason to prefer pairwise/listwise objectives, but this\nclaim is difficult to test cleanly on public collections with many confounds.\n\nA compact synthetic benchmark with known latent relevance structure allows a\ncontrolled comparison. If query-specific utility functions generate graded\nrelevance labels (0-4), we can produce query groups with explicit train/test\nsplits and evaluate whether objectives aligned with ranking structure obtain\nbetter NDCG@10 than a strong linear pointwise baseline. Because the data are\nsynthetic, we can vary noise and query heterogeneity while keeping runtime low\nenough for single-core CPU execution.\n\nA credible study should implement three distinct training paradigms in the\nsame feature space: (1) pointwise linear regression on grades, (2) a\nRankNet-lite pairwise logistic objective over within-query document pairs,\nand (3) a ListMLE-lite listwise objective using per-query permutations sorted\nby true grades. Evaluation should report NDCG@10 as primary, plus at least one\nsecondary ranking metric and a sanity metric such as training loss. Results\nshould be averaged across multiple seeds and at least two synthetic dataset\nregimes.\n\nThe key scientific goal is not reproducing a specific paper, but testing\nwhether increasing objective-level ranking awareness yields measurable gains\nin top-k ranking quality under controlled conditions and limited compute.\n\n*Do pairwise and listwise lightweight objectives consistently outperform a pointwise linear baseline on NDCG@10 across synthetic query-document datasets with graded relevance?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"ListMLE-lite achieves higher mean NDCG@10 than pointwise linear regression on at least 2 of 3 synthetic datasets, with an absolute improvement of at least 0.03 on each winning dataset (averaged over >=3 seeds).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At least one of {RankNet-lite, ListMLE-lite} outperforms pointwise linear regression in mean NDCG@10 on all evaluated datasets.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The best ranking-aware method (max of RankNet-lite and ListMLE-lite per dataset) improves mean MAP@10 over pointwise linear by at least 0.02 on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Do pairwise/listwise lightweight ranking objectives provide consistent top-k retrieval gains over a pointwise linear baseline on synthetic grouped query-document data?\", \"conditions\": [{\"name\": \"pointwise_linear\", \"description\": \"Linear model trained pointwise on query-document feature vectors to predict graded relevance via squared error; ranking by predicted score within each query.\"}, {\"name\": \"ranknet_lite\", \"description\": \"Pairwise logistic ranking objective on within-query document pairs using a linear scorer; optimized with mini-batch gradient descent in numpy.\"}, {\"name\": \"listmle_lite\", \"description\": \"Listwise ListMLE-style objective with a linear scorer, optimizing likelihood of ground-truth relevance-sorted document permutations per query.\"}, {\"name\": \"pointwise_ridge_tuned\", \"description\": \"Pointwise linear baseline with L2 regularization tuned over a small grid to ensure a competitive non-ranking-aware baseline.\"}], \"baselines\": [\"pointwise_linear is the primary baseline\", \"pointwise_ridge_tuned is a strengthened linear baseline\"], \"metrics\": [{\"name\": \"ndcg_at_10\", \"direction\": \"maximize\", \"description\": \"Mean NDCG@10 across test queries; primary metric.\"}, {\"name\": \"map_at_10\", \"direction\": \"maximize\", \"description\": \"Mean Average Precision@10 across test queries using relevance>=1 as relevant.\"}, {\"name\": \"pairwise_accuracy\", \"direction\": \"maximize\", \"description\": \"Fraction of correctly ordered document pairs within test queries (based on graded relevance order).\"}], \"datasets\": [{\"name\": \"synth_ltr_easy\", \"source\": \"Synthetic generator: 80 queries x 25 docs/query, 20 dense features, low noise, relevance grades 0-4 from latent linear utility.\"}, {\"name\": \"synth_ltr_noisy\", \"source\": \"Synthetic generator: 100 queries x 30 docs/query, 25 features, higher Gaussian noise and feature corruption, grades 0-4.\"}, {\"name\": \"synth_ltr_sparse\", \"source\": \"Synthetic generator: 90 queries x 20 docs/query, 40 features with sparsity mask, query-specific weight drift, grades 0-4.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 480}}", "requirements": "", "rubric": "{\"id\": \"ml23-root\", \"requirements\": \"A credible experiment comparing pointwise, pairwise (RankNet-style), and listwise (ListMLE-style) ranking approaches on synthetic query-document datasets: methods are implemented as distinct objectives, execution reports ranking metrics on multiple datasets with multiple seeds, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated ranking-objective substitutes (e.g., LambdaRank-style, softrank) that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml23-code\", \"requirements\": \"The ranking methods and synthetic grouped datasets are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml23-code-methods\", \"requirements\": \"The submission implements multiple distinct conditions — typically a pointwise baseline plus at least one pairwise and one listwise method — with genuinely different objective computations, not merely different hyperparameters of one loss.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml23-code-data\", \"requirements\": \"A synthetic query-document generator is implemented with grouped queries, per-query document lists, and graded relevance labels, and multiple dataset regimes are instantiated.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml23-code-setup\", \"requirements\": \"The experimental setup includes query-level train/test splitting (no leakage of documents from the same query across splits) and a shared scoring-function interface across methods.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml23-exec\", \"requirements\": \"Execution logs ranking metrics per method and dataset.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml23-exec-metrics\", \"requirements\": \"Execution produces a metrics artifact containing numeric NDCG@k and MAP@k (or equivalents) for each implemented condition on at least one dataset.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml23-exec-seeds\", \"requirements\": \"Reported metrics are averaged over multiple random seeds per (condition, dataset) cell with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml23-exec-tuning\", \"requirements\": \"At least one non-trivial hyperparameter search or documented default is executed (e.g., learning rate/regularization) and the chosen value is logged.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml23-results\", \"requirements\": \"Results analysis addresses H1/H2/H3 directionally.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml23-result-h1\", \"requirements\": \"The submission compares the listwise method vs the pointwise baseline in mean NDCG@k per dataset and conveys whether listwise meaningfully improves ranking quality — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml23-result-h2h3\", \"requirements\": \"The submission conveys whether at least one ranking-aware method beats the pointwise baseline on NDCG across all datasets (H2) and whether the best ranking-aware method yields a meaningful MAP improvement on most datasets (H3).\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml23-result-writeup\", \"requirements\": \"The README or writeup describes methods, datasets, and metric outcomes; conveys per-hypothesis outcomes (supported / refuted / inconclusive); and discusses limitations (synthetic-data realism, scorer capacity, seed count, metric sensitivity). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/ml/manifests/ML23.yaml", "rubric_file": "tasks/ml/rubrics/ML23.json"} +{"id": "ML24", "domain": "ml", "title": "Online binary classification under concept drift: SGD, PA, NB, and FTRL", "topic": "Benchmarking online learning algorithms (SGD logistic, Passive-Aggressive, online Naive Bayes, Follow-the-Regularized-Leader) on streaming binary classification with concept drift", "domains": ["online-learning", "machine-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "prequential_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 420, "synthesis": "In streaming classification, data arrive sequentially and model updates must\nbe cheap and immediate. Under concept drift, the relationship between\nfeatures and labels changes over time, so static train/test evaluation can\nbe misleading. Prequential (test-then-train) evaluation better reflects\ndeployment: each incoming point is first predicted, then used for an update.\nThis setting creates practical trade-offs among stability, adaptability, and\ncomputational cost.\n\nSeveral lightweight online learners in sklearn-style workflows represent\ndifferent adaptation biases. SGD logistic regression can track drift via\ngradient updates but may require careful learning-rate choices. Passive-\nAggressive updates can react strongly to mistakes and often adapt quickly to\nabrupt shifts. Online Naive Bayes is extremely cheap and robust but may lag\nwhen feature dependence structure changes. A simple FTRL-style proximal\nlogistic update (implemented with diagonal accumulators) offers adaptive\nper-feature learning rates and implicit regularization.\n\nA credible CPU-only benchmark should compare these methods on at least one\nsynthetic abrupt-drift stream and one real sklearn dataset converted to a\nstream with induced drift (e.g., blockwise class-prior or feature transform\nshift). It should report prequential accuracy as the primary metric, plus at\nleast one drift-sensitive secondary metric such as post-drift recovery and\ncumulative log loss. Multi-seed runs are important because stream order and\ndrift-point randomness can materially change outcomes.\n\nThe study should also verify that drift actually hurts models by comparing a\nno-drift control stream against drifted streams, and should discuss which\nalgorithms recover fastest after drift while maintaining overall accuracy.\nThis makes the benchmark more diagnostic than a single aggregate score.\n\n*Which lightweight online learner provides the best trade-off between overall prequential accuracy and adaptation speed after concept drift in binary streams?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On drifted streams, Passive-Aggressive or FTRL achieves higher prequential_accuracy than online Naive Bayes by at least 0.02 absolute on at least 2 of 3 datasets, averaged over >=5 seeds.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"For at least 2 of 3 drifted datasets, the best post-drift recovery accuracy in a fixed window of 200 samples after each drift point is achieved by either Passive-Aggressive or FTRL.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"For SGD logistic and Passive-Aggressive, prequential_accuracy on a drifted version of each stream is at least 0.03 lower than on its matched no-drift control, on at least 2 of 3 datasets.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Which online method among SGD logistic, Passive-Aggressive, online Naive Bayes, and FTRL-style logistic best balances prequential accuracy and post-drift recovery on binary data streams with concept drift?\", \"conditions\": [{\"name\": \"sgd_logistic\", \"description\": \"SGDClassifier with log_loss and partial_fit in prequential test-then-train loop.\"}, {\"name\": \"passive_aggressive\", \"description\": \"PassiveAggressiveClassifier with partial_fit in the same loop.\"}, {\"name\": \"online_naive_bayes\", \"description\": \"GaussianNB updated incrementally via partial_fit.\"}, {\"name\": \"ftrl_prox_logistic\", \"description\": \"Custom numpy FTRL-proximal logistic regression with diagonal accumulator and L1/L2 regularization.\"}], \"baselines\": [\"online_naive_bayes as a simple low-cost baseline\", \"sgd_logistic as a standard linear online-learning baseline\"], \"metrics\": [{\"name\": \"prequential_accuracy\", \"direction\": \"maximize\", \"description\": \"Mean test-then-train accuracy over the full stream, averaged over 5 seeds.\"}, {\"name\": \"post_drift_recovery_accuracy\", \"direction\": \"maximize\", \"description\": \"Accuracy in the first 200 samples after each drift point, averaged across drift events and seeds.\"}, {\"name\": \"prequential_log_loss\", \"direction\": \"minimize\", \"description\": \"Cumulative mean log loss computed prequentially when probabilistic outputs are available (or clipped score-to-prob mapping).\"}], \"datasets\": [{\"name\": \"synthetic_abrupt_drift\", \"source\": \"numpy synthesis (piecewise make_classification with coefficient/sign flips at fixed indices)\"}, {\"name\": \"breast_cancer_stream\", \"source\": \"sklearn.datasets.load_breast_cancer transformed into a repeated/shuffled stream with blockwise feature scaling shift\"}, {\"name\": \"spambase_like_synthetic\", \"source\": \"numpy synthesis with sparse informative features and class-prior shift across blocks\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 420}}", "requirements": "", "rubric": "{\"id\": \"ml24-root\", \"requirements\": \"A credible experiment studying online learners (SGD logistic, Passive-Aggressive, online Naive Bayes, FTRL-style logistic, or equivalents) under concept drift: streaming prequential setup is implemented, execution covers multiple datasets with multiple seeds, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated online-learner substitutes or drift-stream variants that test the same scientific question should be credited.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml24-code\", \"requirements\": \"Online-learning conditions and drifted streaming setup are implemented correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml24-code-methods\", \"requirements\": \"The submission implements multiple distinct online methods, each as a real incremental update path (e.g., partial_fit or equivalent), not batch retraining.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml24-code-streaming\", \"requirements\": \"A prequential test-then-train loop is implemented: each sample (or mini-batch) is predicted before being used for model update, with ordered stream processing.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"ml24-code-datasets\", \"requirements\": \"The submission uses multiple streams including at least one drifted stream and one no-drift or matched-control stream, generated from sklearn datasets or numpy synthesis.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml24-exec\", \"requirements\": \"Execution reports prequential metrics for each condition.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml24-exec-metrics\", \"requirements\": \"Execution produces a metrics artifact containing numeric prequential accuracy (or equivalent) for each implemented method on at least one dataset.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml24-exec-recovery\", \"requirements\": \"Execution includes a post-drift recovery measure (e.g., fixed-window post-drift accuracy) with a documented computation referencing known drift points.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml24-exec-seeds\", \"requirements\": \"Each reported (dataset, method) metric is averaged over multiple random seeds, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.\", \"weight\": 6.25, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Hyperparameter Tuning\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml24-results\", \"requirements\": \"Quantitative analysis addresses H1/H2/H3 directionally.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml24-result-h1\", \"requirements\": \"The submission compares prequential accuracy of margin-based online methods (Passive-Aggressive, FTRL) against online Naive Bayes across drifted datasets and conveys whether margin-based methods are meaningfully better — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml24-result-h2\", \"requirements\": \"The submission reports post-drift recovery comparisons and conveys whether Passive-Aggressive or FTRL tends to lead on most datasets (H2).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml24-result-h3\", \"requirements\": \"The submission compares drifted vs no-drift control performance for the implemented methods and conveys whether drift produces a meaningful accuracy degradation (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml24-result-writeup\", \"requirements\": \"The README or writeup describes setup, key metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (stream realism, drift design choices, seed count, metric assumptions). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 10, "manifest_file": "tasks/ml/manifests/ML24.yaml", "rubric_file": "tasks/ml/rubrics/ML24.json"} +{"id": "ML25", "domain": "ml", "title": "Reservoir computing vs MLP vs GP for short-horizon Lorenz-63 forecasting", "topic": "Testing whether a random-feature echo state network (reservoir computing) predicts Lorenz-63 short-horizon trajectories more accurately than a matched-parameter MLP and a Gaussian Process at small training-data sizes (N=200-2000 points)", "domains": ["dynamical-systems", "machine-learning", "reservoir-computing"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "valid_prediction_time", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 720, "synthesis": "Reservoir computing (a.k.a. echo state networks, ESN) is a lightweight\napproach for learning dynamical systems: a fixed random recurrent network\nprojects the input into a high-dimensional reservoir state, and only a\nlinear readout layer is trained. For chaotic systems, ESNs have repeatedly\nbeen reported to match or outperform trainable recurrent nets on\nshort-horizon trajectory prediction, despite training only a ridge\nregression on the readout.\n\nA credible CPU-scale study of this claim must compare (i) a reservoir\nnetwork with a random sparse internal matrix and fixed spectral radius,\n(ii) a parameter-matched fully-connected MLP that reads a fixed window of\npast states, and (iii) a Gaussian Process regressor with an RBF kernel on\nthe same windowed input. The target signal is Lorenz-63 integrated at\ndt=0.02 with the classical (σ=10, ρ=28, β=8/3) parameters. Training-set\nsizes N ∈ {200, 500, 1000, 2000} are small enough that the standard\nreservoir-computing folklore — \"ESNs win when data is scarce\" — can\nactually be tested rather than assumed.\n\nThe key dynamics-aware metric is the *valid prediction time* (VPT):\nthe first time step at which the normalized prediction error exceeds a\nthreshold (commonly 0.4 of the attractor standard deviation). Unlike\nper-step RMSE, VPT tracks how long a model's forecast remains useful on\nthe chaotic attractor. Reporting both RMSE and VPT reveals whether any\nobserved \"superiority\" is numerical or dynamical.\n\nThe research question is: *does a small reservoir (N_res ≤ 500 units) beat\na matched-parameter MLP and a Gaussian Process on valid prediction time\nfor Lorenz-63, and at which training-set size does the gap appear?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"An echo-state network with spectral radius ≈ 0.9 and ≤ 500 reservoir units achieves a longer mean Valid Prediction Time (VPT ≥ 1.2×) than a parameter-matched MLP on Lorenz-63 at training-set sizes N ≤ 500.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The Gaussian Process regressor is competitive with (VPT within ±20% of) the ESN at N=200 but is decisively beaten (ESN VPT at least 2× the GP's) at N=2000, reflecting the GP's O(N^3) training bottleneck and the ESN's ability to exploit more data without re-inverting a Gram matrix.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Per-step RMSE at one-step-ahead prediction is NOT a reliable proxy for VPT: at least one condition exists where model A has lower one-step RMSE than model B but a shorter VPT, showing that per-step error understates the divergence on chaotic attractors.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"On Lorenz-63 short-horizon forecasting, does a small echo-state network achieve a longer valid prediction time than a parameter-matched MLP and a Gaussian Process regressor, and how does the ranking depend on training-set size?\", \"conditions\": [{\"name\": \"esn_N200\", \"description\": \"Echo-state network with 300 reservoir units, spectral radius 0.9, input scaling 1.0, leak rate 0.3, ridge regression readout (alpha=1e-5). Trained on N=200 consecutive Lorenz-63 samples.\"}, {\"name\": \"esn_N500\", \"description\": \"Same ESN configuration, N=500 training samples.\"}, {\"name\": \"esn_N1000\", \"description\": \"Same ESN configuration, N=1000 training samples.\"}, {\"name\": \"esn_N2000\", \"description\": \"Same ESN configuration, N=2000 training samples.\"}, {\"name\": \"mlp_N{200,500,1000,2000}\", \"description\": \"Fully-connected MLP with 2 hidden layers whose total parameter count is within ±10% of the ESN readout parameter count. Reads a 5-step window of past (x,y,z) states as input. Adam optimizer, early stopping on a 10% validation split.\"}, {\"name\": \"gp_N{200,500,1000,2000}\", \"description\": \"Gaussian Process regressor with an RBF kernel and WhiteNoise term, hyperparameters optimised by marginal likelihood maximisation. Reads the same 5-step window as the MLP. At N=2000 the GP may time out or run out of memory; record the failure.\"}], \"baselines\": [\"Persistence baseline: predicted next state = previous state. Establishes a floor for VPT.\", \"Linear autoregressive baseline: one-step-ahead linear regression on the 5-step window. Establishes whether the non-linear methods' gains are real.\"], \"metrics\": [{\"name\": \"valid_prediction_time\", \"direction\": \"maximize\", \"description\": \"First forecast step (in units of integration dt) at which the normalized L2 error exceeds 0.4 × attractor std. Averaged over 10 random initial conditions on an independent test trajectory.\"}, {\"name\": \"one_step_rmse\", \"direction\": \"minimize\", \"description\": \"Root-mean-square error of one-step-ahead prediction on the test trajectory.\"}, {\"name\": \"train_wall_clock_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock time to fit the model, including hyperparameter optimisation where applicable.\"}, {\"name\": \"param_count\", \"direction\": \"report\", \"description\": \"Number of trainable parameters. Must be reported to demonstrate that the MLP is parameter-matched to the ESN.\"}], \"datasets\": [{\"name\": \"lorenz63_train\", \"source\": \"synthetic — integrate dxdt=σ(y-x), dy/dt=x(ρ-z)-y, dz/dt=xy-βz at dt=0.02 with σ=10, ρ=28, β=8/3. Use scipy.integrate.solve_ivp or a hand-rolled RK4.\"}, {\"name\": \"lorenz63_test\", \"source\": \"independent Lorenz-63 trajectory (different IC, 10× longer warmup) with 10 evaluation windows for VPT averaging.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 720}}", "requirements": "", "rubric": "{\"id\": \"ml25-root\", \"requirements\": \"A credible CPU-scale study of reservoir computing (echo-state network), a matched-parameter MLP, and a Gaussian Process on Lorenz-63 short-horizon forecasting: Lorenz-63 is integrated, the three model families are implemented, multiple training-set sizes are swept, valid-prediction-time is reported, and results address H1/H2/H3 directionally.\", \"judging_note\": \"Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Reasonable substitutes (e.g., alternative chaotic system, library-based ESN when clearly documented, Matern-kernel GP) that test the same scientific question should be credited. A GP running out of memory at large N is a valid reported outcome.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"ml25-code\", \"requirements\": \"The three model families and the dynamical system are implemented.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml25-code-lorenz\", \"requirements\": \"The submission integrates the Lorenz-63 system with classical parameters at a small time step using scipy.integrate or a hand-rolled Runge-Kutta integrator, and uses separate train/test trajectories.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"ml25-code-esn\", \"requirements\": \"An echo-state network is implemented with a sparse random reservoir scaled to a target spectral radius below ~1.1, driven by the input sequence with a leak-rate update and a linear readout fit by ridge regression. A from-scratch implementation is preferred; a clearly documented library-based ESN that exposes the same mechanics is acceptable.\", \"weight\": 8.3333, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml25-code-mlp\", \"requirements\": \"An MLP is implemented that reads a fixed window of past states and predicts the next state, with its total parameter count reported and roughly matched to the ESN readout parameter count.\", \"weight\": 4.1667, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"ml25-code-gp\", \"requirements\": \"A Gaussian Process regressor with an RBF or Matern kernel is fit on the same windowed input, with marginal-likelihood hyperparameter optimisation enabled.\", \"weight\": 4.1667, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml25-exec\", \"requirements\": \"Execution produces per-method, per-N quantitative results.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"ml25-exec-sweep\", \"requirements\": \"The experiment sweeps multiple training-set sizes for each of the three methods and reports numeric results per cell.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml25-exec-vpt\", \"requirements\": \"A valid-prediction-time (VPT) metric is computed per method per N — the first step at which the normalised prediction error exceeds a threshold — averaged over multiple independent initial conditions or seeds.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"ml25-exec-rmse\", \"requirements\": \"A one-step-ahead RMSE (or equivalent pointwise error) is computed per method per N on the test trajectory and reported in the metrics file.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"ml25-results\", \"requirements\": \"The writeup addresses H1/H2/H3 directionally and contextualises numeric findings.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"ml25-result-h1\", \"requirements\": \"The submission reports VPT for the ESN vs MLP at small training sizes and conveys whether the ESN meaningfully outperforms the MLP in the small-N regime — judge directionally against H1.\", \"weight\": 20.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml25-result-h2\", \"requirements\": \"The submission compares ESN and GP VPT at both small-N and larger-N and conveys whether the GP is competitive at small N and falls behind (or becomes infeasible) at large N (H2). Reporting a GP memory/time failure at large N is a valid outcome.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml25-result-h3\", \"requirements\": \"The submission examines whether one-step RMSE ranking tracks VPT ranking across method-N cells and conveys whether at least one rank inversion is observed (H3).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"ml25-result-writeup\", \"requirements\": \"The README or writeup describes the Lorenz-63 setup, the three methods, the N-sweep, the VPT and RMSE numbers, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (single dynamical system, no Lyapunov-time normalisation, GP memory cliff). No strict word-count requirement.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 11, "manifest_file": "tasks/ml/manifests/ML25.yaml", "rubric_file": "tasks/ml/rubrics/ML25.json"} diff --git a/data/physics.jsonl b/data/physics.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d804f9f840bbabfcabf430eb9e82952a65a74ffb --- /dev/null +++ b/data/physics.jsonl @@ -0,0 +1,10 @@ +{"id": "P01", "domain": "physics", "title": "Reproducing the Randall-Sundrum KK-graviton resonance pattern in $e^+e^- \\to \\mu^+\\mu^-$", "topic": "Reproducing warped extra dimension Kaluza-Klein graviton resonance from arXiv:hep-ph/9909255", "domains": ["high-energy-physics", "bsm-phenomenology", "extra-dimensions"], "arxiv_id": "hep-ph/9909255", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the cross-section vs $\\sqrt{s}$ scan of $e^+e^- \\to \\mu^+\\mu^-$\nin the Randall-Sundrum warped extra dimension scenario as a benchmark of\nthe agent's ability to assemble a full Lagrangian -> MC -> analysis\npipeline. The model adds a tower of massive spin-2 KK gravitons\n$h^{(n)}_{\\alpha\\beta}$ ($n=1,\\ldots,5$) coupled to the SM\nenergy-momentum tensor with strength $1/\\Lambda_\\pi$, on top of the\nmassless zero-mode coupled with $1/\\bar M_{Pl}$. The diagnostic\nobservable is $\\sigma(e^+e^- \\to \\mu^+\\mu^-)$ scanned over\n$\\sqrt{s}\\in\\{200,300,\\ldots,1200\\}$ GeV, which exhibits sharp\nresonance peaks at the KK masses $m_n = \\{600, 1098, 1592, 2086, 2580\\}$\nGeV where they sit in the scan window, and an off-resonance continuum\nenhancement above SM Drell-Yan elsewhere.\n\nA credible study (a) implements the spin-2 graviton-stress-tensor\nLagrangian (FeynRules + UFO or an equivalent analytic propagator),\n(b) generates parton-level events at each of the 11 energy points with\nproper handling of the unitary-gauge graviton propagator,\n(c) extracts $\\sigma(\\sqrt{s})$ from the MadGraph banner for each run,\n(d) plots $\\sigma$ vs $\\sqrt{s}$ on a log-y axis over [50, 1500] GeV with\nrange $[400, 5\\times 10^6]$ fb, and (e) compares the resonance positions\nand continuum heights against the published Figure 2. Research question:\n*to what extent does an autonomous HEP pipeline reproduce the RS\nKK-graviton resonance spectrum imprinted on dilepton production at a\n500 GeV-1 TeV $e^+e^-$ collider?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The first KK resonance $m_1 = 600$ GeV produces a peak in $\\\\sigma(e^+e^- \\\\to \\\\mu^+\\\\mu^-)$ at $\\\\sqrt{s} = 600$ GeV that lies within 5% of the input mass.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Off-resonance cross sections at $\\\\sqrt{s}=200$ and $\\\\sqrt{s}=400$ GeV agree with the published Figure 2 values within 30%, demonstrating correct continuum (zero-mode + virtual KK) interference.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"At least 2 of the 5 KK resonances within the [50,1500] GeV scan window ($m_1=600$, $m_2=1098$ GeV) are visible as $\\\\geq 1$-decade enhancements above the SM Drell-Yan baseline at $\\\\sqrt{s}=m_n$.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the Randall-Sundrum KK-graviton resonance pattern in $e^+e^- \\\\to \\\\mu^+\\\\mu^-$ across the $\\\\sqrt{s}=200$-$1200$ GeV scan, matching the published positions and continuum heights of Figure 2 of arXiv:hep-ph/9909255?\", \"conditions\": [{\"name\": \"sqrt_s_scan\", \"description\": \"Scan $\\\\sqrt{s} \\\\in \\\\{200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200\\\\}$ GeV with $E^+ = E^- = \\\\sqrt{s}/2$, 100 events per energy point. KK masses fixed at $m_n = \\\\{600, 1098, 1592, 2086, 2580\\\\}$ GeV; $\\\\Lambda_\\\\pi = 522$ GeV; $\\\\bar M_{Pl} = 2.4 \\\\times 10^{18}$ GeV.\"}, {\"name\": \"sm_only_baseline\", \"description\": \"Same energy scan with all KK gravitons decoupled ($1/\\\\Lambda_\\\\pi \\\\to 0$) to obtain the pure SM $\\\\gamma^*/Z$ Drell-Yan baseline for resonance-significance comparison.\"}], \"baselines\": [\"Pure SM $e^+e^- \\\\to \\\\mu^+\\\\mu^-$ (Drell-Yan via $\\\\gamma^*/Z$) at the same energy points\"], \"metrics\": [{\"name\": \"cross_section_pb\", \"direction\": \"match_reference\", \"description\": \"Total $\\\\sigma(e^+e^- \\\\to \\\\mu^+\\\\mu^-)$ in pb at each of the 11 $\\\\sqrt{s}$ points.\"}, {\"name\": \"peak_position_gev\", \"direction\": \"match_reference\", \"description\": \"Reconstructed $\\\\sqrt{s}$ at which the first KK resonance peaks; target = $600 \\\\pm 30$ GeV.\"}, {\"name\": \"resonance_to_continuum_ratio\", \"direction\": \"match_reference\", \"description\": \"Ratio $\\\\sigma(\\\\sqrt{s}=m_1) / \\\\sigma(\\\\sqrt{s}=m_1 - 200\\\\,\\\\mathrm{GeV})$; should exceed $\\\\sim 10$.\"}], \"datasets\": [{\"process_id\": \"ee_to_mumu_RS\", \"sqrt_s_TeV\": 1.2, \"description\": \"$e^+e^- \\\\to \\\\mu^+\\\\mu^-$ in the RS warped-graviton model.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_kk1_peak_position\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report a numeric `peak_position_gev` (or equivalent first-KK-resonance position) within ±5% of 600 GeV — i.e. in the interval [570, 630] GeV — after the RS scan over √s ∈ {200..1200} GeV.\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p01-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for RS warped extra dimension KK-graviton resonance in $e^+e^- \\\\to \\\\mu^+\\\\mu^-$. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p01-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p01-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Randall-Sundrum spin-2 KK graviton tower coupled to SM stress-energy tensor with $1/\\\\Lambda_\\\\pi$ (massive modes) and $1/\\\\bar M_{Pl}$ (zero mode); five massive modes $m_n \\\\in \\\\{600, 1098, 1592, 2086, 2580\\\\}$ GeV with $\\\\Lambda_\\\\pi = 522$ GeV.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p01-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $e^+e^- \\\\to \\\\mu^+\\\\mu^-$ at energies $\\\\sqrt{s} \\\\in \\\\{200,\\\\ldots,1200\\\\}$ GeV with KK-graviton tower.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p01-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: Lepton-collider beams $E^\\\\pm = \\\\sqrt{s}/2$, no proton PDF; widths of all KK gravitons computed automatically (two-body decays only); 100 events per energy point.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p01-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p01-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p01-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p01-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p01-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p01-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p01-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — First KK resonance peaks within 5% of input $m_1=600$ GeV (i.e. peak in $[570, 630]$ GeV) — score 100% if rel. err. < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p01-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — Off-resonance cross sections at $\\\\sqrt{s} \\\\in \\\\{200, 400\\\\}$ GeV agree with the published Figure 2 within $\\\\pm 30\\\\%$. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p01-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — At least 2 of the 5 KK resonances within the [50,1500] GeV scan window appear as $\\\\geq 1$-decade enhancements above the SM Drell-Yan baseline. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p01-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Reproduced figure shows $\\\\sigma$ vs $\\\\sqrt{s}$ with x-axis $\\\\sqrt{s}$ in GeV (linear, [50,1500]), y-axis $\\\\sigma$ in fb (log, $[400, 5\\\\times 10^6]$), blue line, all resonances visible.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p01-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p01-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p01-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p01-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p01-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P01.yaml", "rubric_file": "tasks/physics/rubrics/P01.json"} +{"id": "P02", "domain": "physics", "title": "Reproducing $pp \\to \\mu^\\pm N$ cross section vs heavy-Majorana-neutrino mass at $\\sqrt{s} = 7, 8, 14$ TeV", "topic": "Reproducing heavy Majorana neutrino production $pp \\to \\mu^\\pm N$ at LHC 7/8/14 TeV from arXiv:1308.2209", "domains": ["high-energy-physics", "bsm-phenomenology", "neutrino-physics"], "arxiv_id": "1308.2209", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the LHC sensitivity to a single heavy Majorana neutrino $N$\nin the muon channel as a benchmark of the agent's ability to assemble a\nhadron-collider Lagrangian -> MC -> analysis pipeline. The BSM extension\nadds a right-handed singlet $N$ mixing with $\\nu_\\mu$ via the\ndimensionless real parameter $V_{\\mu N}$, so the relevant interaction is\n$\\mathcal{L}_N \\supset -(g_2/\\sqrt{2})\\,V_{\\mu N}\\,(\\bar\\mu\\gamma^\\mu P_L\nN)\\,W^-_\\mu + (\\text{NC term}) + \\text{h.c.}$ The diagnostic observable\nis the $W^*$-mediated cross section $\\sigma(pp \\to \\mu^\\pm N)$ scanned\nover $m_N \\in \\{100, 200, \\ldots, 1000\\}$ GeV with $|V_{\\mu N}|=1$, at\nthree LHC energies $\\sqrt{s} \\in \\{7, 8, 14\\}$ TeV with the CTEQ6L PDF.\n\nA credible study (a) implements the heavy-Majorana-neutrino Lagrangian\n(FeynRules .fr or analytic UFO modification), (b) sets up MadGraph runs\nfor the 30 (mass, energy) cells with the CTEQ6L PDF, (c) extracts the\ncross section per cell, and (d) plots three side-by-side panels of\n$\\sigma(m_N)$ on a log-y axis over $[100,1000]$ GeV vs $[0.1, 4\\times\n10^4]$ fb. Research question: *does the agent reproduce the steep\nPDF-driven fall-off of $\\sigma(pp \\to \\mu^\\pm N)$ with $m_N$ and the\ncorrect ordering between $\\sqrt{s}=7$, 8, and 14 TeV predicted by\narXiv:1308.2209 Fig. 3?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At $m_N = 200$ GeV and $\\\\sqrt{s}=14$ TeV, $\\\\sigma(pp \\\\to \\\\mu^\\\\pm N)$ falls within $\\\\pm 30\\\\%$ of the published reference value.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Cross-section ordering $\\\\sigma(14\\\\,\\\\mathrm{TeV}) > \\\\sigma(8\\\\,\\\\mathrm{TeV}) > \\\\sigma(7\\\\,\\\\mathrm{TeV})$ holds at every mass point, with the 14/7 ratio at $m_N = 500$ GeV in $[5, 30]$.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"$\\\\sigma(m_N)$ falls by at least 2 orders of magnitude between $m_N = 100$ GeV and $m_N = 1000$ GeV at every LHC energy.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the cross-section curves for $pp \\\\to \\\\mu^\\\\pm N$ as a function of the heavy-Majorana-neutrino mass at the 7, 8, and 14 TeV LHC, matching Figure 3 of arXiv:1308.2209?\", \"conditions\": [{\"name\": \"lhc_7tev\", \"description\": \"$pp$ collisions at $\\\\sqrt{s}=7$ TeV with CTEQ6L PDF, mass scan $m_N \\\\in \\\\{100, 200, 300, 400, 500, 600, 700, 800, 900, 1000\\\\}$ GeV, $|V_{\\\\mu N}|=1$.\"}, {\"name\": \"lhc_8tev\", \"description\": \"Same setup at $\\\\sqrt{s}=8$ TeV (LHC Run 1 final dataset energy).\"}, {\"name\": \"lhc_14tev\", \"description\": \"Same setup at $\\\\sqrt{s}=14$ TeV (HL-LHC design).\"}], \"baselines\": [\"Charged-current SM $pp \\\\to \\\\mu \\\\nu_\\\\mu$ (W production) cross check at the same energies\"], \"metrics\": [{\"name\": \"cross_section_fb\", \"direction\": \"match_reference\", \"description\": \"Total $\\\\sigma(pp \\\\to \\\\mu^\\\\pm N)$ in fb at each (mass, energy) cell.\"}, {\"name\": \"energy_ratio_14_to_7\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma(14\\\\,\\\\mathrm{TeV})/\\\\sigma(7\\\\,\\\\mathrm{TeV})$ at $m_N = 500$ GeV.\"}, {\"name\": \"mass_dropoff_factor\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma(m_N=100)/\\\\sigma(m_N=1000)$ at each $\\\\sqrt{s}$.\"}], \"datasets\": [{\"process_id\": \"pp_to_muN_via_Wstar\", \"sqrt_s_TeV\": 14, \"description\": \"$pp \\\\to \\\\mu^\\\\pm N$ via $W^*$ at the LHC (CTEQ6L PDF). Three sub-samples for $\\\\sqrt{s} \\\\in \\\\{7,8,14\\\\}$ TeV.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_xsec_at_mN_200\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report a numeric `sigma_pp_to_muN_at_mN200_14TeV_fb` (or equivalent) within ±30% of the published reference value at m_N=200 GeV, √s=14 TeV.\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p02-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for Heavy Majorana neutrino $pp \\\\to \\\\mu^\\\\pm N$ at LHC 7/8/14 TeV. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p02-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p02-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): BSM single right-handed Majorana neutrino $N$ mixing with the muon flavor: $\\\\mathcal{L}_N \\\\supset -(g_2/\\\\sqrt 2) V_{\\\\mu N} (\\\\bar\\\\mu \\\\gamma^\\\\mu P_L N) W^-_\\\\mu - (g_2/2 c_W) V_{\\\\mu N} (\\\\bar\\\\nu_\\\\mu \\\\gamma^\\\\mu P_L N) Z_\\\\mu + \\\\mathrm{h.c.}$ at $|V_{\\\\mu N}| = 1$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p02-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\\\to \\\\mu^\\\\pm N$ via $W^*$ at the LHC, mass scan $m_N \\\\in \\\\{100,\\\\ldots,1000\\\\}$ GeV at three energies.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p02-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: CTEQ6L PDF for all $\\\\sqrt{s}$ values; 30 (mass, energy) cells (10 masses x 3 energies).\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p02-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p02-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p02-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p02-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p02-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p02-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p02-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — $\\\\sigma(pp \\\\to \\\\mu^\\\\pm N)$ at $m_N=200$ GeV, $\\\\sqrt{s}=14$ TeV agrees with reference within $\\\\pm 30\\\\%$.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p02-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — Energy ordering $\\\\sigma(14) > \\\\sigma(8) > \\\\sigma(7)$ TeV holds at every mass; 14/7 ratio at $m_N=500$ GeV in $[5, 30]$. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p02-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — $\\\\sigma(m_N)$ falls $\\\\geq 2$ orders of magnitude from $m_N=100$ to $m_N=1000$ GeV at every $\\\\sqrt{s}$. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p02-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Three-panel figure (one per $\\\\sqrt{s}$), 1:1 aspect, x-axis $m_N \\\\in [100,1000]$ GeV linear, y-axis $\\\\sigma \\\\in [0.1, 4\\\\times10^4]$ fb log.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p02-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p02-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p02-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p02-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p02-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P02.yaml", "rubric_file": "tasks/physics/rubrics/P02.json"} +{"id": "P03", "domain": "physics", "title": "Reproducing 8 TeV LHC dilepton exclusion contours for a B-L Z' boson with kinetic mixing", "topic": "Reproducing 8 TeV LHC dilepton exclusion contours for B-L Z' with kinetic mixing from arXiv:1605.02910", "domains": ["high-energy-physics", "bsm-phenomenology", "z-prime", "statistical-recast"], "arxiv_id": "1605.02910", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the LHC Run 1 sensitivity to a B-L gauge boson Z' with\nkinetic mixing as a benchmark of the agent's ability to combine MC\nparameter scans with an analytic statistical recast. The model has a\nZ' coupling to SM fermions via a left/right combination\n$C_{f,L/R}(\\tilde g, g_1', M_{Z'})$ where $g_1'$ is the B-L gauge\ncoupling, $\\tilde g$ is the kinetic mixing, and the production cross\nsection is the exact quadratic form\n$\\sigma(pp \\to Z') = A g_1'^2 + B g_1' \\tilde g + C \\tilde g^2$ — so\nthree runs per mass point determine $(A,B,C)$ for the entire $(\\tilde\ng, g_1')$ plane. The diagnostic observable is the three-panel exclusion\ncontour at $\\mathrm{Sig}\\equiv 2(\\sqrt{S+B}-\\sqrt B) = 2$ for\n$M_{Z'} \\in \\{2.0, 2.5, 3.0\\}$ TeV at $\\mathcal{L} = 20$ fb$^{-1}$.\n\nA credible study (a) implements the kinetic-mixing B-L Lagrangian and\n$C_{f,L/R}$ couplings, (b) runs the 9 signal MadGraph runs (3 mass\npoints x 3 coupling points) plus 3 SM Drell-Yan background runs with\n$m_{\\ell\\ell} > 0.9 M_{Z'}$ generator cut, (c) computes the analytic\n$Z' \\to e^+e^- + \\mu^+\\mu^-$ branching ratio including 3 degenerate\nheavy Majorana neutrinos $m_{\\nu_h}=95$ GeV, (d) applies the published\nNNLO k-factors $k\\in\\{1.35,1.40,1.50\\}$ and acceptance\n$\\epsilon_\\mathrm{acc}=0.6$, (e) solves the $\\mathrm{Sig}=2$ equation\non a 2D grid in $(\\tilde g, g_1')$, and (f) plots three side-by-side\ncontours. Research question: *does the agent reproduce the three-panel\n$(\\tilde g, g_1')$ exclusion contours at $M_{Z'}=2.0, 2.5, 3.0$ TeV from\nFigure 1 of arXiv:1605.02910?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"For the $M_{Z'}=2$ TeV panel, the exclusion contour crosses the $\\\\tilde g = 0$ axis at $g_1' \\\\in [0.10, 0.20]$ (matching the published value within 30%).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The $(A, B, C)$ quadratic coefficients extracted at $M_{Z'}=2$ TeV satisfy $A > 0$ and $|B| \\\\le 4\\\\sqrt{AC}$ (positivity of the cross section over the full parameter plane).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The maximum reach in $g_1'$ at $\\\\tilde g = 0$ degrades by at least a factor of 2 going from $M_{Z'}=2$ TeV to $M_{Z'}=3$ TeV, reflecting the PDF suppression at high mass.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the three-panel $(\\\\tilde g, g_1')$ 2$\\\\sigma$ exclusion contours at $M_{Z'}=2.0, 2.5, 3.0$ TeV from the LHC Run 1 dilepton search at $\\\\sqrt{s}=8$ TeV with $\\\\mathcal{L}=20$ fb$^{-1}$ (Figure 1 of arXiv:1605.02910)?\", \"conditions\": [{\"name\": \"mzp_2tev\", \"description\": \"$M_{Z'}=2$ TeV: 3 signal runs at $(g_1', \\\\tilde g) \\\\in \\\\{(0.10, 0), (0.10, -0.10), (0.15, -0.05)\\\\}$.\"}, {\"name\": \"mzp_2p5tev\", \"description\": \"$M_{Z'}=2.5$ TeV: 3 signal runs at the same three $(g_1', \\\\tilde g)$ points.\"}, {\"name\": \"mzp_3tev\", \"description\": \"$M_{Z'}=3$ TeV: 3 signal runs at $(g_1', \\\\tilde g) \\\\in \\\\{(0.30, 0), (0.30, -0.30), (0.50, -0.60)\\\\}$.\"}], \"baselines\": [\"SM Drell-Yan $pp \\\\to \\\\ell^+\\\\ell^-$ at $\\\\sqrt{s}=8$ TeV with generator-level cut $m_{\\\\ell\\\\ell} > 0.9 M_{Z'}$, one per mass point\"], \"metrics\": [{\"name\": \"exclusion_g1p_at_kinmix0\", \"direction\": \"match_reference\", \"description\": \"Value of $g_1'$ at which the $\\\\mathrm{Sig}=2$ contour crosses $\\\\tilde g = 0$, per mass point.\"}, {\"name\": \"quadratic_coefficients_ABC\", \"direction\": \"match_reference\", \"description\": \"$(A, B, C)$ extracted from the 3 signal runs per mass; positive-definite check.\"}, {\"name\": \"br_zprime_to_dilepton\", \"direction\": \"match_reference\", \"description\": \"Analytic $\\\\mathrm{BR}(Z' \\\\to e^+e^-+\\\\mu^+\\\\mu^-)$ at the three benchmark masses.\"}], \"datasets\": [{\"process_id\": \"pp_to_Zprime_BL_kinmix\", \"sqrt_s_TeV\": 8, \"description\": \"$pp \\\\to Z'$ with kinetic-mixing B-L Z' at the LHC, dilepton final state. Background: SM Drell-Yan with $m_{\\\\ell\\\\ell}$ cut.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_g1prime_threshold\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the g1' coordinate where the 2σ exclusion contour at M_Z'=2 TeV crosses the g̃=0 axis, in the interval [0.07, 0.26] (i.e. published [0.10, 0.20] ±30%).\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p03-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for B-L $Z'$ kinetic mixing, three-panel exclusion in $(\\\\tilde g, g_1')$. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p03-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p03-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Kinetic-mixing B-L $Z'$ with chiral couplings $C_{f,L/R}(\\\\tilde g, g_1', M_{Z'})$ encoding $g_1'$ (B-L gauge), $\\\\tilde g$ (kinetic mixing), and 3 heavy Majorana neutrinos $\\\\nu_h$ at $m_{\\\\nu_h}=95$ GeV.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p03-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\\\to Z'$ at $\\\\sqrt{s}=8$ TeV with kinetic mixing; Drell-Yan dilepton search recast.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p03-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: 9 signal runs (3 mass x 3 coupling points), 3 SM Drell-Yan background runs with generator cut $m_{\\\\ell\\\\ell} > 0.9 M_{Z'}$ at $\\\\sqrt{s}=8$ TeV; mass-dependent NNLO k-factors $\\\\{1.35, 1.40, 1.50\\\\}$ and $\\\\epsilon_\\\\mathrm{acc}=0.6$.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p03-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p03-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p03-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p03-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p03-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p03-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p03-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — $M_{Z'}=2$ TeV exclusion contour crosses the $\\\\tilde g=0$ axis at $g_1' \\\\in [0.10, 0.20]$ (within 30% of published).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p03-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — Quadratic coefficients $(A,B,C)$ at $M_{Z'}=2$ TeV satisfy $A>0$ and $|B| \\\\le 4\\\\sqrt{AC}$ (positive-definite cross section everywhere in plane). Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p03-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — Maximum reach in $g_1'$ at $\\\\tilde g=0$ degrades by $\\\\geq 2\\\\times$ from $M_{Z'}=2$ TeV to $M_{Z'}=3$ TeV. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p03-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Three side-by-side panels (one per $M_{Z'}$), 1:1 aspect, exclusion contour at $\\\\mathrm{Sig}\\\\equiv 2(\\\\sqrt{S+B}-\\\\sqrt B)=2$ in the $(\\\\tilde g, g_1')$ plane.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p03-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p03-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p03-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p03-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p03-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P03.yaml", "rubric_file": "tasks/physics/rubrics/P03.json"} +{"id": "P04", "domain": "physics", "title": "Reproducing $\\sigma(pp \\to Z')$ vs $g_1'$ slices for kinetic-mixing B-L Z' at $\\sqrt{s}=13$ TeV", "topic": "Reproducing $\\sigma(pp \\to Z')$ vs $g_1'$ slices for kinetic-mixing B-L Z' at 13 TeV from arXiv:1605.02910", "domains": ["high-energy-physics", "bsm-phenomenology", "z-prime"], "arxiv_id": "1605.02910", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the $\\sqrt{s}=13$ TeV LHC production cross section\n$\\sigma(pp \\to Z')$ as a function of $g_1'$ along three constant-\n$\\tilde g$ slices, for the same kinetic-mixing B-L $Z'$ model as P03.\nThe cross section is exactly quadratic in the couplings,\n$\\sigma(g_1', \\tilde g) = A g_1'^2 + B g_1' \\tilde g + C \\tilde g^2$,\nso 3 MadGraph runs per mass point determine $(A,B,C)$ on the entire\n$(g_1', \\tilde g)$ plane. The diagnostic figure has two panels\n(Panel A: $M_{Z'}=2$ TeV with $\\tilde g\\in\\{0, -0.05, -0.10\\}$;\nPanel B: $M_{Z'}=3$ TeV with $\\tilde g\\in\\{0, -0.30, -0.60\\}$).\n\nA credible study (a) reuses the kinetic-mixing B-L $Z'$ model\n(FeynRules + UFO), (b) executes 6 MadGraph runs (3 per mass) at\n$\\sqrt{s}=13$ TeV, (c) solves the linear system for $(A,B,C)$ at each\nmass, (d) computes the analytic curves $\\sigma(g_1')|_{\\tilde g}$ for\nthe requested slices, and (e) plots two panels with log-y on the\nprescribed ranges (Panel A: $g_1' \\in [0, 0.20]$, $\\sigma \\in\n[10^{-4}, 10^{-1}]$ pb; Panel B: $g_1' \\in [0, 0.70]$, $\\sigma \\in\n[10^{-4}, 10^{1}]$ pb). Research question: *does the agent recover the\nquadratic-in-couplings parameterization of $Z'$ production well enough\nto reproduce the constant-$\\tilde g$ cross-section slices in Figure 10\nof arXiv:1605.02910?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At Panel A ($M_{Z'}=2$ TeV, $\\\\tilde g = 0$, $g_1' = 0.10$), the reconstructed $\\\\sigma(pp \\\\to Z')$ lies within $\\\\pm 30\\\\%$ of the published value.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The curve at $\\\\tilde g = -0.10$ in Panel A lies above the $\\\\tilde g = 0$ curve at $g_1' = 0.05$ but below at $g_1' = 0.20$, demonstrating the destructive-then-constructive interference pattern of the kinetic mixing.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"$\\\\sigma$ at ($M_{Z'}=2$ TeV, $g_1'=0.10$, $\\\\tilde g=0$) exceeds $\\\\sigma$ at ($M_{Z'}=3$ TeV, $g_1'=0.10$, $\\\\tilde g=0$) by a factor in $[10, 200]$, reflecting the steep PDF suppression.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce $\\\\sigma(pp \\\\to Z')$ vs $g_1'$ for fixed-$\\\\tilde g$ slices at $M_{Z'}=2$ and 3 TeV at $\\\\sqrt{s}=13$ TeV (Figure 10 of arXiv:1605.02910)?\", \"conditions\": [{\"name\": \"mzp_2tev_3runs\", \"description\": \"$M_{Z'}=2$ TeV at $\\\\sqrt{s}=13$ TeV: 3 signal runs at $(g_1', \\\\tilde g) \\\\in \\\\{(0.10, 0), (0.10, -0.10), (0.15, -0.05)\\\\}$.\"}, {\"name\": \"mzp_3tev_3runs\", \"description\": \"$M_{Z'}=3$ TeV at $\\\\sqrt{s}=13$ TeV: 3 signal runs at $(g_1', \\\\tilde g) \\\\in \\\\{(0.30, 0), (0.30, -0.30), (0.50, -0.60)\\\\}$.\"}], \"baselines\": [\"Self-consistency cross-check: predicted $\\\\sigma$ at the 3 input points must reproduce the simulated $\\\\sigma$ exactly (closure of the $A,B,C$ fit)\"], \"metrics\": [{\"name\": \"cross_section_pb\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma(pp \\\\to Z')$ in pb on the requested slice points.\"}, {\"name\": \"quadratic_coefficients_ABC_13tev\", \"direction\": \"match_reference\", \"description\": \"$(A, B, C)$ coefficients per mass point at $\\\\sqrt{s}=13$ TeV.\"}, {\"name\": \"mass_pdf_suppression_ratio\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma(M_{Z'}=2)/\\\\sigma(M_{Z'}=3)$ at $\\\\tilde g = 0$, $g_1' = 0.10$.\"}], \"datasets\": [{\"process_id\": \"pp_to_Zprime_BL_kinmix_13tev\", \"sqrt_s_TeV\": 13, \"description\": \"$pp \\\\to Z'$ for the kinetic-mixing B-L Z' model at LHC Run 2 energy.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_xsec_Zprime_2TeV\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report `sigma_pp_to_Zprime_2TeV_panelA_fb` (at M_Z'=2 TeV, g̃=0, g1'=0.10) within ±30% of the published Panel A reference cross section.\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p04-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for B-L $Z'$ kinetic mixing $\\\\sigma(g_1')$ slices at $\\\\sqrt{s}=13$ TeV. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p04-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p04-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Same kinetic-mixing B-L $Z'$ as P03; cross section $\\\\sigma = A g_1'^2 + B g_1'\\\\tilde g + C \\\\tilde g^2$ exact.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p04-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\\\to Z'$ at $\\\\sqrt{s}=13$ TeV; $\\\\sigma$ vs $g_1'$ at fixed $\\\\tilde g$ slices.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p04-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: 6 signal runs (3 per mass) at $\\\\sqrt{s}=13$ TeV; standard LHC PDF set.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p04-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p04-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p04-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p04-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p04-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p04-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p04-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — $\\\\sigma(M_{Z'}=2\\\\,\\\\mathrm{TeV}, \\\\tilde g=0, g_1'=0.10)$ within $\\\\pm 30\\\\%$ of published Panel A value.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p04-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — $\\\\tilde g=-0.10$ curve in Panel A lies above $\\\\tilde g=0$ at $g_1'=0.05$ but below at $g_1'=0.20$ (interference sign change). Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p04-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — $\\\\sigma(M_{Z'}=2)/\\\\sigma(M_{Z'}=3)$ at $\\\\tilde g=0, g_1'=0.10$ is in $[10, 200]$ (PDF suppression). Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p04-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Two-panel figure (Panel A: $M_{Z'}=2$ TeV, $g_1'\\\\in[0,0.20]$, $\\\\sigma \\\\in [10^{-4}, 10^{-1}]$ pb; Panel B: $M_{Z'}=3$ TeV, $g_1'\\\\in[0,0.70]$, $\\\\sigma \\\\in [10^{-4}, 10^1]$ pb), three colored curves per panel.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p04-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p04-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p04-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p04-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p04-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P04.yaml", "rubric_file": "tasks/physics/rubrics/P04.json"} +{"id": "P05", "domain": "physics", "title": "Reproducing the normalized $E_T^\\mathrm{miss}$ shape in $pp \\to a W^\\pm \\gamma$ from a photophobic ALP EFT", "topic": "Reproducing photophobic ALP EFT $E_T^\\mathrm{miss}$ shape in $pp \\to a W^\\pm \\gamma$ from arXiv:1701.05379", "domains": ["high-energy-physics", "bsm-phenomenology", "axion-like-particles", "effective-field-theory"], "arxiv_id": "1701.05379", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the normalized $E_T^\\mathrm{miss}$ distribution for the\nprocess $pp \\to a W^\\pm (\\to \\ell^\\pm \\nu)\\, \\gamma$ at $\\sqrt{s}=13$\nTeV in the bosonic ALP EFT, as a benchmark of the agent's ability to\nhandle a higher-dimensional EFT operator and parton-level missing-energy\nreconstruction. The Lagrangian is\n$\\delta\\mathcal{L}_a = c_{\\tilde W} \\mathcal{A}_{\\tilde W} +\nc_{\\tilde B} \\mathcal{A}_{\\tilde B}$ with the photophobic constraint\n$c_{\\tilde B} = -\\tan^2\\theta_W \\cdot c_{\\tilde W}$ (so the\n$a\\gamma\\gamma$ coupling vanishes), giving a single free Wilson\ncoefficient $c_{\\tilde W}$. The diagnostic observable is the\nnormalized parton-level $E_T^\\mathrm{miss} = |\\vec p_T^{\\,a} +\n\\vec p_T^{\\,\\nu}|$ shape in 50 bins over [0, 1000] GeV, with\ngeneration cuts $p_T^{\\gamma,\\ell} > 20$ GeV, $|\\eta^{\\gamma,\\ell}| < 2.5$.\n\nA credible study (a) implements the photophobic ALP EFT (FeynRules\nbosonic operators with $c_{\\tilde B} = -\\tan^2\\theta_W \\cdot c_{\\tilde W}$),\n(b) generates 500k LHE events at $f_a = 1000$ GeV, $m_a = 0.001$ GeV,\n$c_{\\tilde W} = 1$ with the nn23lo1 PDF, (c) sums $\\vec p_T^{\\,a}$ +\n$\\vec p_T^{\\,\\nu}$ from invisible final-state particles to reconstruct\n$E_T^\\mathrm{miss}$, and (d) plots the normalized histogram on log-y\n$[10^{-4}, 1]$ over [0, 1000] GeV. Research question: *does the agent\nreproduce the falling-then-flattening MET shape characteristic of a\nderivative ALP coupling versus the steeply falling SM-like baseline,\nmatching Figure 8 of arXiv:1701.05379?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The peak of the normalized $E_T^\\\\mathrm{miss}$ distribution lies in the bin range $[100, 250]$ GeV, consistent with the published shape.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The integrated normalized fraction of events with $E_T^\\\\mathrm{miss} > 500$ GeV is in $[0.05, 0.30]$ at $c_{\\\\tilde W}=1$, $f_a=1$ TeV (long high-MET tail from the derivative ALP coupling).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Acceptance after the published photon and lepton cuts ($p_T > 20$ GeV, $|\\\\eta| < 2.5$) is $\\\\geq 50\\\\%$ of generated events.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the parton-level $E_T^\\\\mathrm{miss}$ shape for $pp \\\\to a W^\\\\pm \\\\gamma$, $W \\\\to \\\\ell\\\\nu$ at $\\\\sqrt{s}=13$ TeV under the photophobic ALP EFT (Figure 8 of arXiv:1701.05379)?\", \"conditions\": [{\"name\": \"alp_cw1_fa1tev\", \"description\": \"Bosonic ALP EFT at $c_{\\\\tilde W}=1$, $c_{\\\\tilde B}=-\\\\tan^2\\\\theta_W$, $f_a=1000$ GeV, $m_a=0.001$ GeV, 500k events at $\\\\sqrt{s}=13$ TeV with nn23lo1 PDF.\"}, {\"name\": \"alp_cw_variant\", \"description\": \"Sanity-check run at $c_{\\\\tilde W}=2$, $f_a=1000$ GeV; cross section should scale as $c_{\\\\tilde W}^2$ but normalized shape should be invariant.\"}], \"baselines\": [\"Shape-only normalization is its own baseline: deviation between $c_{\\\\tilde W}=1$ and $c_{\\\\tilde W}=2$ shapes should be statistical only\"], \"metrics\": [{\"name\": \"etmiss_peak_position_gev\", \"direction\": \"match_reference\", \"description\": \"$E_T^\\\\mathrm{miss}$ bin center of the peak of the normalized histogram.\"}, {\"name\": \"high_metmiss_tail_fraction\", \"direction\": \"match_reference\", \"description\": \"Fraction of normalized events with $E_T^\\\\mathrm{miss} > 500$ GeV.\"}, {\"name\": \"cut_acceptance\", \"direction\": \"match_reference\", \"description\": \"Acceptance fraction after photon + lepton fiducial cuts.\"}], \"datasets\": [{\"process_id\": \"pp_to_aWgamma_Wlnu\", \"sqrt_s_TeV\": 13, \"description\": \"Parton-level $pp \\\\to a W^\\\\pm \\\\gamma$ with $W \\\\to \\\\ell\\\\nu$ in the photophobic ALP EFT.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_met_peak_bin\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the peak bin of the normalized E_T^miss distribution; the bin center must lie in [100, 250] GeV and the distribution must integrate to 1.0 (normalized).\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p05-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for Photophobic ALP EFT $E_T^\\\\mathrm{miss}$ in $pp \\\\to a W^\\\\pm \\\\gamma$. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p05-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p05-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Bosonic ALP EFT operators $\\\\mathcal{A}_{\\\\tilde W}, \\\\mathcal{A}_{\\\\tilde B}$ with photophobic relation $c_{\\\\tilde B} = -\\\\tan^2\\\\theta_W \\\\cdot c_{\\\\tilde W}$; $f_a = 1$ TeV, $m_a = 1$ MeV, $c_{\\\\tilde W} = 1$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p05-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: Parton-level $pp \\\\to a W^\\\\pm (\\\\to \\\\ell^\\\\pm\\\\nu) \\\\gamma$ at $\\\\sqrt{s}=13$ TeV.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p05-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: nn23lo1 PDF; photon $p_T > 20$ GeV, $|\\\\eta_\\\\gamma| < 2.5$; lepton $p_T > 20$ GeV, $|\\\\eta_\\\\ell| < 2.5$; 500k events, parton level (no shower/detector).\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p05-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p05-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p05-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p05-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p05-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p05-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p05-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — Peak of normalized $E_T^\\\\mathrm{miss}$ in $[100, 250]$ GeV bin range.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p05-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — Integrated normalized fraction with $E_T^\\\\mathrm{miss} > 500$ GeV in $[0.05, 0.30]$ (long high-MET tail from derivative coupling). Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p05-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — Acceptance after photon + lepton cuts $\\\\geq 50\\\\%$. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p05-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Normalized $E_T^\\\\mathrm{miss}$ histogram, 50 bins over [0,1000] GeV, log-y $[10^{-4}, 1]$, 4:3 aspect.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p05-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p05-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p05-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p05-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p05-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P05.yaml", "rubric_file": "tasks/physics/rubrics/P05.json"} +{"id": "P06", "domain": "physics", "title": "Reproducing $U_1$ vector-leptoquark $\\sqrt{|g_c^* g_b|}$ vs $M_{U_1}$ exclusion from ATLAS+CMS $pp \\to \\tau\\nu$", "topic": "Reproducing $U_1$ vector leptoquark exclusion from combined ATLAS+CMS $pp \\to \\tau\\nu$ recast from arXiv:1811.07920", "domains": ["high-energy-physics", "bsm-phenomenology", "leptoquark", "flavor-anomaly", "statistical-recast"], "arxiv_id": "1811.07920", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the LHC sensitivity to a $U_1$ vector leptoquark from the\n$pp \\to \\tau\\nu$ channel via $bc \\to U_1 \\to \\tau\\nu$ exchange,\ncombining ATLAS + CMS searches in a binned profile-likelihood, as a\nbenchmark of the agent's ability to (i) generate LQ events with\nbottom-quark and charm-quark initial-state partons, (ii) run two\nparallel detector-card simulations (Delphes ATLAS card vs CMS card),\n(iii) recast experimental data into a profile-likelihood fit. The\nLagrangian couples $U_1$ to $(\\bar c \\gamma^\\mu P_L \\nu_\\tau)$ and\n$(\\bar b \\gamma^\\mu P_L \\tau)$ with couplings $g_c, g_b$, and the\ndiagnostic figure is the 2$\\sigma$ exclusion contour in the\n$(\\sqrt{|g_c^* g_b|}, M_{U_1})$ plane along with the LH and RH\n$R_{D^{(*)}}$ flavor-anomaly bands.\n\nA credible study (a) implements the $U_1$ Lagrangian (FeynRules .fr +\nUFO), (b) runs MadGraph + Pythia8 + Delphes for $M_{U_1} \\in \\{750,\n1000, 1250, 1500, 2000, 2500, 3000, 4000, 5000\\}$ GeV with both ATLAS\nand CMS cards (LHCO output), (c) applies the published ATLAS and CMS\n$\\tau$-channel selections (lepton veto, hadronic-$\\tau$ requirement,\n$E_T^\\mathrm{miss}$ cut, $m_T$ cut), (d) builds a binned signal\ntemplate that scales as $g^4$ and runs the per-bin profile likelihood\nwith nuisance parameters $\\theta_i$, (e) combines ATLAS + CMS, finds\nthe 2$\\sigma$ exclusion via $-2\\Delta\\ln\\mathcal{L} = 4$, and (f)\noverlays the analytic LH band $g_\\mathrm{LH}(M) = (M/v)\\sqrt{2 V_{cb}\\,\n\\epsilon_L}$ with $\\epsilon_L = 0.11 \\pm 0.02$ and the analogous RH band.\nResearch question: *does the agent reproduce the $(\\sqrt{|g_c^* g_b|},\nM_{U_1})$ exclusion contour and the $R_{D^{(*)}}$ overlay bands of\nFigure 3 of arXiv:1811.07920?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The combined ATLAS+CMS 2$\\\\sigma$ exclusion at $M_{U_1} = 1$ TeV gives $\\\\sqrt{|g_c^* g_b|} \\\\in [0.3, 1.5]$, matching the published value within 30%.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The reconstructed exclusion contour intersects the LH $R_{D^{(*)}}$ band ($\\\\epsilon_L = 0.11$) at an excluded mass $M_{U_1}^* \\\\in [1.0, 2.5]$ TeV.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Selection efficiency on signal events is $\\\\geq 5\\\\%$ at $M_{U_1}=1$ TeV in both ATLAS and CMS analyses (gauge of pipeline working end-to-end through Delphes).\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the 2$\\\\sigma$ exclusion contour for the $U_1$ vector leptoquark in the $(\\\\sqrt{|g_c^* g_b|}, M_{U_1})$ plane from combined ATLAS+CMS $pp \\\\to \\\\tau\\\\nu$ recast (Figure 3 of arXiv:1811.07920)?\", \"conditions\": [{\"name\": \"atlas_recast\", \"description\": \"Mass scan $M_{U_1} \\\\in \\\\{750, 1000, 1250, 1500, 2000, 2500, 3000, 4000, 5000\\\\}$ GeV at $\\\\sqrt{s}=13$ TeV, 10k events each, Pythia8 shower, Delphes ATLAS card, LHCO output. Selection: lepton veto, hadronic-$\\\\tau$ $p_T > 80$ GeV, $|\\\\eta_\\\\tau| < 2.3$, $E_T^\\\\mathrm{miss} > 150$ GeV, $m_T > 250$ GeV.\"}, {\"name\": \"cms_recast\", \"description\": \"Same mass scan with Delphes CMS card; selection: lepton veto, $\\\\tau$ $p_T>80$ GeV, $|\\\\eta|<2.1$, $E_T^\\\\mathrm{miss}>200$ GeV, $0.7 < p_T^\\\\tau / E_T^\\\\mathrm{miss} < 1.3$, $\\\\Delta\\\\phi(\\\\tau, E_T^\\\\mathrm{miss}) > 2.4$, $m_T > 320$ GeV.\"}], \"baselines\": [\"Published ATLAS background table (22 bins, 250-3200 GeV log-spaced) and CMS background table (3 bins) act as the SM-only null hypothesis\"], \"metrics\": [{\"name\": \"exclusion_coupling_at_1tev\", \"direction\": \"match_reference\", \"description\": \"$\\\\sqrt{|g_c^* g_b|}$ at the 2$\\\\sigma$ exclusion boundary at $M_{U_1}=1$ TeV from combined fit.\"}, {\"name\": \"selection_efficiency\", \"direction\": \"match_reference\", \"description\": \"Fraction of generated signal events passing the ATLAS or CMS selection at $M_{U_1}=1$ TeV.\"}, {\"name\": \"rdstar_band_intersection_tev\", \"direction\": \"match_reference\", \"description\": \"Mass at which the LH $R_{D^{(*)}}$ band crosses the exclusion contour.\"}], \"datasets\": [{\"process_id\": \"pp_to_taunu_via_U1\", \"sqrt_s_TeV\": 13, \"description\": \"$pp \\\\to \\\\tau\\\\nu$ via $bc \\\\to U_1$ exchange. Two detector simulations (Delphes ATLAS, Delphes CMS).\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_lq_coupling_band\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the 2σ combined ATLAS+CMS √(g_c* g_b) exclusion band at M_U1=1 TeV; the band lower edge must be in [0.21, 0.39] and the upper edge in [1.05, 1.95] (published [0.3, 1.5] ±30%).\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p06-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for $U_1$ vector-leptoquark recast of LHC $pp \\\\to \\\\tau\\\\nu$ (ATLAS+CMS). The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p06-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p06-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): $U_1$ vector LQ in $(\\\\mathbf{3},\\\\mathbf{1},2/3)$ rep coupling to $(\\\\bar c\\\\gamma^\\\\mu P_L\\\\nu_\\\\tau)$ and $(\\\\bar b\\\\gamma^\\\\mu P_L\\\\tau)$ with couplings $g_c, g_b$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p06-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\\\to \\\\tau\\\\nu$ via $bc \\\\to U_1 \\\\to \\\\tau\\\\nu$ exchange at $\\\\sqrt{s}=13$ TeV, mass scan.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p06-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: Standard 13 TeV proton PDF; 9 mass points $\\\\{750,\\\\ldots,5000\\\\}$ GeV with $b$ and $c$ parton initial states; 10k events per (mass, detector); Pythia8 shower; Delphes ATLAS card and Delphes CMS card runs in parallel; LHCO output.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p06-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p06-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p06-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p06-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p06-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p06-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p06-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — Combined ATLAS+CMS 2$\\\\sigma$ exclusion at $M_{U_1}=1$ TeV gives $\\\\sqrt{|g_c^* g_b|} \\\\in [0.3, 1.5]$.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p06-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — Reconstructed exclusion contour intersects the LH $R_{D^{(*)}}$ band ($\\\\epsilon_L=0.11$) at excluded mass $M_{U_1}^* \\\\in [1.0, 2.5]$ TeV. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p06-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — Selection efficiency $\\\\geq 5\\\\%$ on signal at $M_{U_1}=1$ TeV in both ATLAS and CMS recasts. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p06-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Exclusion contour in $(\\\\sqrt{|g_c^* g_b|}, M_{U_1})$ plane, 4:3 aspect, x in TeV $[0.8, 5.0]$, y $[0, 4.0]$, with LH band (blue dashed + light-blue band) and RH band (red dashed + light-red band).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p06-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p06-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p06-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p06-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p06-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P06.yaml", "rubric_file": "tasks/physics/rubrics/P06.json"} +{"id": "P07", "domain": "physics", "title": "Reproducing the $m_{ej}$ resonance in $pp \\to \\mathrm{LQ} \\to ej$ via the LUXlep proton lepton PDF", "topic": "Reproducing scalar leptoquark $m_{ej}$ resonance via LUXlep proton lepton PDF from arXiv:2005.06475", "domains": ["high-energy-physics", "bsm-phenomenology", "leptoquark", "lepton-pdf"], "arxiv_id": "2005.06475", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the LHC sensitivity to a scalar leptoquark in the\n$pp \\to \\mathrm{LQ} \\to ej$ channel as a benchmark of the agent's\nability to assemble a full Lagrangian -> MC -> shower -> detector ->\nanalysis pipeline using a non-trivial PDF (LUXlep, with electrons inside\nthe proton). The Lagrangian\n$\\mathcal{L} = \\lambda_{eu}\\,\\mathrm{LQ}_{eu}\\,e_R^T i\\sigma^2 u_R +\n\\mathrm{h.c.}$ describes an $SU(2)_L$-singlet, color-triplet,\n$Q=-1/3$ scalar leptoquark coupling to a right-handed lepton-quark pair.\nThe diagnostic observable is the reconstructed invariant mass $m_{ej}$\nof the leading electron and leading jet, peaked at the input\n$M_\\mathrm{LQ}=3$ TeV with $\\Gamma_\\mathrm{LQ}=60$ GeV after Pythia8 +\nDelphes ATLAS detector smearing.\n\nA credible study (a) implements the scalar-LQ Lagrangian (FeynRules .fr\n+ UFO), (b) generates 100k events at $\\sqrt{s}=13$ TeV with the LUXlep\nPDF (proton content redefined to include leptons + photon) and\ngeneration cuts $p_T(\\ell, j) > 500$ GeV, $|\\eta| < 2.5$, (c) applies\nthe Pythia8 lepton-to-photon workaround (replace initial-state leptons\nwith photons in the LHE; set Check:event=off; shower; Delphes ATLAS\ncard with anti-$k_T$ R=0.4 jets; LHCO output), (d) selects events with\nexactly one $e$ ($p_T>500$ GeV, $|\\eta|<2.5$), one $j$ ($p_T>500$ GeV,\n$|\\eta|<2.5$), $E_T^\\mathrm{miss} < 50$ GeV, lepton veto, jet veto,\n(e) histograms $m_{ej}$ in 100 GeV bins from 0 to 5000 GeV weighted by\n$\\sigma\\mathcal{L}/N_\\mathrm{gen}$ at $\\mathcal{L}=100$ fb$^{-1}$, and\n(f) plots on log-y $[10^{-3}, 5\\times 10^2]$ events/bin over [1000,\n5000] GeV. Research question: *does the agent reproduce the scalar-LQ\nresonance peak at $m_{ej} \\approx 3$ TeV from Figure 2 of arXiv:2005.06475?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Reconstructed $m_{ej}$ peaks within $\\\\pm 5\\\\%$ of the input $M_\\\\mathrm{LQ}=3000$ GeV after Pythia8+Delphes smearing (i.e., peak in [2850, 3150] GeV).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The generation-level $p_T(\\\\ell, j) > 500$ GeV cut yields $\\\\geq 50\\\\%$ acceptance for the benchmark $M_\\\\mathrm{LQ}=3$ TeV signal.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Total event count in the [2.5, 3.5] TeV $m_{ej}$ window at $\\\\mathcal{L} = 100$ fb$^{-1}$ matches the published value within $\\\\pm 30\\\\%$.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the $m_{ej}$ invariant-mass distribution for $pp \\\\to \\\\mathrm{LQ} \\\\to ej$ at $\\\\sqrt{s}=13$ TeV with LUXlep PDF and $M_\\\\mathrm{LQ}=3$ TeV (Figure 2 of arXiv:2005.06475)?\", \"conditions\": [{\"name\": \"lq_3tev_lambda1\", \"description\": \"Scalar LQ at $M_\\\\mathrm{LQ}=3000$ GeV, $\\\\Gamma_\\\\mathrm{LQ}=60$ GeV, $\\\\lambda_{eu}=1$, 100k events at $\\\\sqrt{s}=13$ TeV, LUXlep PDF, $p_T > 500$ GeV gen cuts, Pythia8 + Delphes ATLAS, LHCO output.\"}, {\"name\": \"lq_2tev_check\", \"description\": \"Cross-check at $M_\\\\mathrm{LQ}=2000$ GeV with $\\\\lambda_{eu}=1$, same setup; verify that $m_{ej}$ peak shifts to 2 TeV.\"}], \"baselines\": [\"Background-only sanity check: $\\\\lambda_{eu}=0$ (LQ decoupled) should produce zero $m_{ej}$ events above the gen-level cut\"], \"metrics\": [{\"name\": \"peak_position_gev\", \"direction\": \"match_reference\", \"description\": \"Bin-center of the maximum of the reconstructed $m_{ej}$ histogram for the 3 TeV signal.\"}, {\"name\": \"acceptance_pt500\", \"direction\": \"match_reference\", \"description\": \"Fraction of generated events with both lepton and jet $p_T > 500$ GeV at $M_\\\\mathrm{LQ}=3$ TeV.\"}, {\"name\": \"integrated_yield_in_window\", \"direction\": \"match_reference\", \"description\": \"Sum of weighted events in $m_{ej} \\\\in [2500, 3500]$ GeV at $\\\\mathcal{L}=100$ fb$^{-1}$.\"}], \"datasets\": [{\"process_id\": \"pp_to_LQ_to_ej_LUXlep\", \"sqrt_s_TeV\": 13, \"description\": \"Single resonant scalar leptoquark production via lepton-quark fusion using LUXlep proton PDF.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_mej_peak_position\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the reconstructed m_ej peak position (after Pythia8+Delphes smearing) in the interval [2850, 3150] GeV (input M_LQ=3 TeV ±5%).\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p07-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for Scalar leptoquark resonance $m_{ej}$ via LUXlep proton lepton PDF. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p07-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p07-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Scalar LQ in $SU(2)_L$ singlet, color triplet, $Q=-1/3$; $\\\\mathcal{L} = \\\\lambda_{eu}\\\\,\\\\mathrm{LQ}_{eu}\\\\,e_R^T i\\\\sigma^2 u_R + \\\\mathrm{h.c.}$; $M_\\\\mathrm{LQ}=3$ TeV, $\\\\Gamma=60$ GeV, $\\\\lambda_{eu}=1$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p07-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\\\to \\\\mathrm{LQ} \\\\to ej$ resonant single LQ production at $\\\\sqrt{s}=13$ TeV.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p07-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: LUXlep PDF (proton content includes electrons and photon); generation cuts $p_T(\\\\ell, j) > 500$ GeV, $|\\\\eta|<2.5$; Pythia8 with lepton-to-photon LHE workaround (Check:event=off); Delphes ATLAS card with anti-$k_T$ R=0.4 jets; LHCO output.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p07-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p07-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p07-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p07-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p07-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p07-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p07-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — Reconstructed $m_{ej}$ peaks within $\\\\pm 5\\\\%$ of input $M_\\\\mathrm{LQ}=3000$ GeV (peak in $[2850, 3150]$ GeV).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p07-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — Generation-level $p_T > 500$ GeV cut yields $\\\\geq 50\\\\%$ acceptance on the 3 TeV signal. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p07-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — Total weighted yield in $m_{ej} \\\\in [2500, 3500]$ GeV at $\\\\mathcal{L}=100$ fb$^{-1}$ within $\\\\pm 30\\\\%$ of published value. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p07-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Reconstructed $m_{ej}$ histogram, 100 GeV bins from 0 to 5000 GeV, weighted by $\\\\sigma\\\\mathcal{L}/N_\\\\mathrm{gen}$, log-y $[10^{-3}, 5\\\\times 10^2]$ events/bin, 1:1 aspect, title 'LHC, $\\\\sqrt{s}=13$ TeV'.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p07-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p07-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p07-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p07-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p07-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P07.yaml", "rubric_file": "tasks/physics/rubrics/P07.json"} +{"id": "P08", "domain": "physics", "title": "Reproducing $\\sigma(pp \\to Z' \\to \\mu^+\\mu^-)$ vs $M_{Z'}$ for SSM and E6-$\\psi$ scenarios at $\\sqrt{s}=13$ TeV", "topic": "Reproducing SSM and $E_6$-$\\psi$ Z' dimuon cross sections at 13 TeV LHC from arXiv:2103.02708", "domains": ["high-energy-physics", "bsm-phenomenology", "z-prime"], "arxiv_id": "2103.02708", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the dimuon production cross section $\\sigma(pp \\to Z' \\to\n\\mu^+\\mu^-)$ as a function of $M_{Z'}$ for two canonical $Z'$ benchmarks\n- the Sequential Standard Model (SSM) where the $Z'$ inherits SM\nelectroweak couplings, and the $E_6$-derived $Z'_\\psi$ with universal\ncoupling $g_\\psi = 0.0942$. The general Lagrangian\n$\\mathcal{L}_{NP} \\supset -Z'_\\mu \\sum_f \\bar f \\gamma^\\mu (g_{Lf} P_L\n+ g_{Rf} P_R) f$ has 7 free chiral couplings $g_{Lu}, g_{Ru}, g_{Ld},\ng_{Rd}, g_{Le}, g_{Re}, g_{Lv}$, fixed differently in each benchmark.\nThe diagnostic observable is the dimuon production cross section\nscanned over $M_{Z'} \\in \\{200, 400, 600, \\ldots, 5500\\}$ GeV at\n$\\sqrt{s}=13$ TeV.\n\nA credible study (a) implements the general $Z'$ model (FeynRules + UFO)\nwith the 7 couplings as inputs, (b) sets up two parameter cards\nencoding the SSM and $E_6$-$\\psi$ coupling tables, (c) scans 14 mass\npoints per benchmark with auto-computed $\\Gamma_{Z'}$ (two-body decays\nonly), (d) extracts the cross section per (mass, scenario) cell, and\n(e) plots two curves (SSM = green dotted, $Z'_\\psi$ = blue solid) on\nlog-y over $M_{Z'} \\in [200, 5500]$ GeV, $\\sigma \\in [5\\times 10^{-6},\n5\\times 10^{-1}]$ pb. Research question: *does the agent reproduce the\ncross-section curves and the well-known SSM $\\gtrsim Z'_\\psi$ ordering\nin dilepton production from Figure 4 of arXiv:2103.02708?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"$\\\\sigma_\\\\mathrm{SSM}(M_{Z'}=2\\\\,\\\\mathrm{TeV}) > \\\\sigma_\\\\psi(M_{Z'}=2\\\\,\\\\mathrm{TeV})$ by a factor in $[3, 30]$ at $\\\\sqrt{s}=13$ TeV (SSM has larger couplings).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"$\\\\sigma_\\\\mathrm{SSM}(M_{Z'}=1\\\\,\\\\mathrm{TeV})$ falls within $\\\\pm 30\\\\%$ of the published reference value.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The SSM cross section drops by $\\\\geq 4$ orders of magnitude between $M_{Z'}=200$ GeV and $M_{Z'}=5500$ GeV (PDF + propagator suppression).\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce $\\\\sigma(pp \\\\to Z' \\\\to \\\\mu^+\\\\mu^-)$ vs $M_{Z'}$ for the SSM ($Z'_\\\\mathrm{SSM}$) and $E_6$-derived $Z'_\\\\psi$ benchmarks at the 13 TeV LHC (Figure 4 of arXiv:2103.02708)?\", \"conditions\": [{\"name\": \"ssm_mass_scan\", \"description\": \"$Z'_\\\\mathrm{SSM}$ with electroweak couplings $g_{Lu} = (e/s_W c_W)(1/2 - 2 s_W^2/3)$, etc. (SM-like). Scan $M_{Z'} \\\\in \\\\{200, 400, 600, 800, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500\\\\}$ GeV at $\\\\sqrt{s}=13$ TeV.\"}, {\"name\": \"e6psi_mass_scan\", \"description\": \"$Z'_\\\\psi$ from $E_6$ with universal coupling $g_\\\\psi = 0.0942$ for all SM fermions. Same mass scan.\"}], \"baselines\": [\"Internal cross-check: SSM/E6-$\\\\psi$ ratio should be approximately mass-independent (set by the ratio of squared couplings) far from mass thresholds\"], \"metrics\": [{\"name\": \"cross_section_pb\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma(pp \\\\to Z' \\\\to \\\\mu^+\\\\mu^-)$ in pb at each (mass, benchmark) cell.\"}, {\"name\": \"ssm_to_e6_ratio_at_2tev\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma_\\\\mathrm{SSM}/\\\\sigma_\\\\psi$ at $M_{Z'}=2$ TeV.\"}, {\"name\": \"mass_dropoff_factor_ssm\", \"direction\": \"match_reference\", \"description\": \"$\\\\sigma_\\\\mathrm{SSM}(M=200)/\\\\sigma_\\\\mathrm{SSM}(M=5500)$.\"}], \"datasets\": [{\"process_id\": \"pp_to_Zprime_to_mumu_general\", \"sqrt_s_TeV\": 13, \"description\": \"$pp \\\\to Z' \\\\to \\\\mu^+\\\\mu^-$ in the general 7-coupling $Z'$ model. Two benchmark coupling tables: SSM and $E_6$-$\\\\psi$.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_ssm_psi_ratio\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the ratio σ_SSM/σ_ψ at M_Z'=2 TeV, √s=13 TeV, and that ratio MUST be in [3, 30].\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p08-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for General $Z'$: SSM vs $E_6$-$\\\\psi$ dimuon cross section vs mass at LHC. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p08-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p08-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): General $Z'$ with 7 chiral couplings $g_{Lu/Ru/Ld/Rd/Le/Re/Lv}$; SSM tabulated values vs $E_6$-$\\\\psi$ universal $g_\\\\psi=0.0942$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p08-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\\\to Z' \\\\to \\\\mu^+\\\\mu^-$ at $\\\\sqrt{s}=13$ TeV, mass scan over 14 points $\\\\{200,\\\\ldots,5500\\\\}$ GeV.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p08-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: Standard 13 TeV proton PDF; widths auto-computed (two-body decays only); 14 mass points x 2 benchmarks = 28 runs.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p08-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p08-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p08-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p08-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p08-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p08-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p08-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — $\\\\sigma_\\\\mathrm{SSM}(2\\\\,\\\\mathrm{TeV}) / \\\\sigma_\\\\psi(2\\\\,\\\\mathrm{TeV})$ in $[3, 30]$.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p08-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — $\\\\sigma_\\\\mathrm{SSM}(M_{Z'}=1\\\\,\\\\mathrm{TeV})$ within $\\\\pm 30\\\\%$ of published value. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p08-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — $\\\\sigma_\\\\mathrm{SSM}$ drops $\\\\geq 4$ orders of magnitude from $M=200$ GeV to $M=5500$ GeV. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p08-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Two curves on log-y $\\\\sigma \\\\in [5\\\\times 10^{-6}, 5\\\\times 10^{-1}]$ pb vs $M_{Z'}\\\\in[200,5500]$ GeV linear: $Z'_\\\\mathrm{SSM}$ green dotted, $Z'_\\\\psi$ blue solid.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p08-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p08-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p08-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p08-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p08-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P08.yaml", "rubric_file": "tasks/physics/rubrics/P08.json"} +{"id": "P09", "domain": "physics", "title": "Reproducing the normalized $|\\eta|$ shape in $\\mu^+\\mu^- \\to b\\bar b$ from a $U_1$ vector leptoquark at $\\sqrt{s}=3$ TeV", "topic": "Reproducing $U_1$ leptoquark $|\\eta|$ shape in $\\mu^+\\mu^- \\to b\\bar b$ at 3 TeV muon collider from arXiv:2104.05720", "domains": ["high-energy-physics", "bsm-phenomenology", "leptoquark", "muon-collider"], "arxiv_id": "2104.05720", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the normalized pseudorapidity shape of the $b$ quark in\n$\\mu^+\\mu^- \\to b\\bar b$ at a 3 TeV muon collider, comparing SM\n($s$-channel $\\gamma^*/Z^*$) against $U_1$ vector-leptoquark\n$t$-channel exchange. The Lagrangian has the standard vector-LQ\nkinetic term plus\n$\\mathcal{L} \\supset (g_U/\\sqrt{2})\\,U_1^\\mu (\\beta_L^{ij}\\,\n\\bar Q_L^i \\gamma_\\mu L_L^j) + \\mathrm{h.c.}$, with only\n$\\beta_L^{32}$ (b-quark to muon) non-zero. The diagnostic observable\nis the normalized $|\\eta|$ distribution of the $b$ quark binned in 13\nbins from 0 to 2.5 (narrow [0, 0.1] first bin then uniform 0.2-wide\nbins), shown in two side-by-side panels at $\\beta_L^{32} = 1.0$ and\n$\\beta_L^{32} = 0.1$, each overlaying SM (gray bar histogram) with\n$m_\\mathrm{LQ}=1$ TeV (blue dashed) and $m_\\mathrm{LQ}=10$ TeV\n(orange dashed).\n\nA credible study (a) implements the $U_1$ vector-LQ Lagrangian with\n$\\kappa_U=\\tilde\\kappa_U=0$ (FeynRules + UFO), (b) runs 5 parton-level\nMadGraph runs (1 SM baseline at 500k events with $\\beta_L^{32}=0$ and\n$m_\\mathrm{LQ}=10^5$ GeV; 4 LQ signal runs at $\\beta_L^{32}\\in\n\\{1.0, 0.1\\}$ x $m_\\mathrm{LQ}\\in\\{1, 10\\}$ TeV with 50k events\neach), (c) extracts the $b$-quark $|\\eta|$ from each event, (d)\nhistograms into the 13 bins and normalizes to total events per run,\n(e) plots two panels side by side with the prescribed style. Research\nquestion: *does the agent reproduce the central-vs-forward angular\nredistribution induced by the $t$-channel $U_1$ exchange in\n$\\mu^+\\mu^- \\to b\\bar b$ from Figure 11 of arXiv:2104.05720?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At $\\\\beta_L^{32} = 1.0$, $m_\\\\mathrm{LQ} = 1$ TeV, the central-bin ($|\\\\eta| < 0.1$) normalized fraction exceeds the SM by $\\\\geq 50\\\\%$ (LQ $t$-channel pulls events forward+central).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At $\\\\beta_L^{32}=0.1$, the normalized $|\\\\eta|$ shape differs from the SM baseline by $\\\\leq 10\\\\%$ in every bin (small-coupling regime collapses to SM).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"At $\\\\beta_L^{32}=1.0$, $m_\\\\mathrm{LQ}=10$ TeV the normalized shape lies between the SM and the $m_\\\\mathrm{LQ}=1$ TeV curves (decoupling limit).\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the normalized $|\\\\eta|$ distributions of $\\\\mu^+\\\\mu^- \\\\to b\\\\bar b$ for SM and $U_1$ leptoquark signal at $\\\\sqrt{s}=3$ TeV muon collider, in the two coupling regimes $\\\\beta_L^{32} \\\\in \\\\{1.0, 0.1\\\\}$ (Figure 11 of arXiv:2104.05720)?\", \"conditions\": [{\"name\": \"sm_baseline_3tev\", \"description\": \"All $\\\\beta_L^{ij} = 0$ (LQ decoupled), $m_\\\\mathrm{LQ} = 10^5$ GeV. 500k events at $\\\\sqrt{s}=3$ TeV, parton level.\"}, {\"name\": \"lq_beta1_3tev\", \"description\": \"$\\\\beta_L^{32} = 1.0$, mass scan $m_\\\\mathrm{LQ} \\\\in \\\\{1, 10\\\\}$ TeV, 50k events each at $\\\\sqrt{s}=3$ TeV.\"}, {\"name\": \"lq_beta01_3tev\", \"description\": \"$\\\\beta_L^{32} = 0.1$, mass scan $m_\\\\mathrm{LQ} \\\\in \\\\{1, 10\\\\}$ TeV, 50k events each at $\\\\sqrt{s}=3$ TeV.\"}], \"baselines\": [\"SM $s$-channel $\\\\mu^+\\\\mu^- \\\\to \\\\gamma^*/Z^* \\\\to b\\\\bar b$ (the $\\\\beta_L^{32}=0$ run)\"], \"metrics\": [{\"name\": \"central_bin_excess_fraction\", \"direction\": \"match_reference\", \"description\": \"Ratio of central-bin ($|\\\\eta| < 0.1$) normalized event fraction in LQ signal vs SM.\"}, {\"name\": \"shape_deviation_l1norm\", \"direction\": \"match_reference\", \"description\": \"$L^1$ norm $\\\\sum_i |f_i^\\\\mathrm{LQ} - f_i^\\\\mathrm{SM}|$ of normalized shape difference per (mass, coupling).\"}, {\"name\": \"decoupling_distance\", \"direction\": \"match_reference\", \"description\": \"Shape distance between $m_\\\\mathrm{LQ}=10$ TeV signal and SM should be smaller than for $m_\\\\mathrm{LQ}=1$ TeV signal.\"}], \"datasets\": [{\"process_id\": \"mumu_to_bbbar_U1\", \"sqrt_s_TeV\": 3, \"description\": \"$\\\\mu^+\\\\mu^- \\\\to b\\\\bar b$ via SM ($s$-channel) + $U_1$ ($t$-channel) at a 3 TeV muon collider, parton level.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_central_bin_excess\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the normalized central-|η|<0.1 fraction for β_L^32=1.0, m_LQ=1 TeV, AND the same fraction for SM, AND the LQ fraction MUST exceed SM by ≥50%.\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p09-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for $U_1$ vector-LQ angular shape in $\\\\mu^+\\\\mu^- \\\\to b\\\\bar b$ at 3 TeV muon collider. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p09-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p09-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): $U_1$ vector LQ in $(\\\\mathbf{3},\\\\mathbf{1},2/3)$ rep with $\\\\kappa_U=\\\\tilde\\\\kappa_U=0$; only $\\\\beta_L^{32}$ (b-quark to muon) non-zero; $g_U=1$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p09-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $\\\\mu^+\\\\mu^- \\\\to b\\\\bar b$ at $\\\\sqrt{s}=3$ TeV via SM $s$-channel and $U_1$ $t$-channel exchange, parton level.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p09-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: Lepton-collider beams (no PDF); 1 SM run (500k events with $\\\\beta_L^{32}=0$, $m_\\\\mathrm{LQ}=10^5$ GeV) + 4 LQ signal runs ($\\\\beta_L^{32}\\\\in\\\\{1.0, 0.1\\\\}$ x $m_\\\\mathrm{LQ}\\\\in\\\\{1, 10\\\\}$ TeV, 50k events each).\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p09-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p09-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p09-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p09-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p09-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p09-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p09-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — At $\\\\beta_L^{32}=1.0$, $m_\\\\mathrm{LQ}=1$ TeV, central-bin $|\\\\eta|<0.1$ normalized fraction exceeds SM by $\\\\geq 50\\\\%$.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p09-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — At $\\\\beta_L^{32}=0.1$, normalized $|\\\\eta|$ shape differs from SM by $\\\\leq 10\\\\%$ in every bin. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p09-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — At $\\\\beta_L^{32}=1.0$, $m_\\\\mathrm{LQ}=10$ TeV shape lies between SM and $m_\\\\mathrm{LQ}=1$ TeV (decoupling). Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p09-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Two side-by-side panels (left: $\\\\beta_L^{32}=1.0$, right: $\\\\beta_L^{32}=0.1$); each panel has SM gray bar histogram + $m_\\\\mathrm{LQ}=1$ TeV blue dashed + $m_\\\\mathrm{LQ}=10$ TeV orange dashed; 13 bins from 0 to 2.5 (narrow [0,0.1] first); $14\\\\times 6$ figure size; y in [0, 0.20].\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p09-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p09-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p09-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p09-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p09-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P09.yaml", "rubric_file": "tasks/physics/rubrics/P09.json"} +{"id": "P10", "domain": "physics", "title": "Reproducing $\\beta_L^{32}$-vs-$m_\\mathrm{LQ}$ exclusion + 5$\\sigma$ discovery contours for $U_1$ at 3 TeV (1 ab$^{-1}$) and 14 TeV (20 ab$^{-1}$) muon colliders", "topic": "Reproducing $U_1$ exclusion + 5$\\sigma$ discovery contours at 3 and 14 TeV muon colliders from arXiv:2104.05720", "domains": ["high-energy-physics", "bsm-phenomenology", "leptoquark", "muon-collider", "statistical-recast"], "arxiv_id": "2104.05720", "venue": "ARC-Bench Physics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 3600, "synthesis": "Reproducing the muon-collider sensitivity to a $U_1$ vector\nleptoquark in the coupling-vs-mass plane via a binned-likelihood\nrecast of $\\mu^+\\mu^- \\to b\\bar b$, as a benchmark of the agent's\nability to combine MC parameter scans with statistical recasting\nspanning two orders of magnitude in mass and four orders in coupling.\nThe same $U_1$ Lagrangian as P09 (only $\\beta_L^{32}$ non-zero),\nbut here the per-bin cross section is parameterized as\n$\\sigma_i(m, \\beta) = b_i + \\beta^2 I_i(m) + \\beta^4 J_i(m)$, with\n$I_i(m), J_i(m)$ extracted by solving a $2\\times 2$ linear system from\ntwo reference $\\beta$ values. The diagnostic figure shows 95% CL\nexclusion (dashed) and 5$\\sigma$ discovery (solid) contours in\n($m_\\mathrm{LQ}$, $\\beta_L^{32}$) at $\\sqrt{s}=3$ TeV (1 ab$^{-1}$,\nred) and $\\sqrt{s}=14$ TeV (20 ab$^{-1}$, purple).\n\nA credible study (a) implements the $U_1$ Lagrangian (FeynRules + UFO),\n(b) runs SM baselines (100k events at 3 and 14 TeV) plus 4 LQ signal\nscans ($\\sqrt{s}\\in\\{3,14\\}$ TeV x $\\beta_L^{32}\\in\\{1.0, 2.0\\}$) over\n17 mass points $m_\\mathrm{LQ} \\in \\{1.0, 1.5, 2.0, 3.0, 4.0, 5.0,\n6.0, 7.0, 8.0, 10, 15, 20, 30, 40, 50, 60, 70\\}$ TeV with 50k events\neach, (c) bins each run in 10 equal-width $|\\eta|$ bins, (d) extracts\n$I_i, J_i$ per (mass, $\\sqrt{s}$), (e) computes the binned\nlog-likelihood ratio for both the exclusion null hypothesis ($n_i\n= b_i^\\mathrm{events}$) and the discovery null ($n_i = \\mu_i$),\n(f) finds the $\\beta$ where $-2\\log\\lambda$ crosses $\\chi^2(10,\n0.95) = 18.307$ (exclusion) or 48.2 (5$\\sigma$ discovery), and\n(g) plots the four contours on log-log axes ($m_\\mathrm{LQ}\\in\n[1, 75]$ TeV, $\\beta\\in[10^{-3}, 2]$). Research question: *does the\nagent reproduce the muon-collider $U_1$ exclusion + discovery\ncontours from Figure 12 of arXiv:2104.05720?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At $\\\\sqrt{s}=14$ TeV with 20 ab$^{-1}$, the 95% CL exclusion contour reaches $\\\\beta_L^{32} \\\\leq 0.01$ at $m_\\\\mathrm{LQ} = 10$ TeV (matching the published value within a factor of 2).\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The 14 TeV exclusion contour extends to higher $m_\\\\mathrm{LQ}$ than the 3 TeV exclusion contour at every fixed $\\\\beta_L^{32}$ in $[10^{-2}, 1]$ (higher energy + luminosity gives more reach).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"5$\\\\sigma$ discovery contours always lie above the corresponding exclusion contours in $\\\\beta_L^{32}$ at every mass (discovery requires stronger signal than exclusion).\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the agent reproduce the 95% CL exclusion and 5$\\\\sigma$ discovery contours for the $U_1$ leptoquark in the $(m_\\\\mathrm{LQ}, \\\\beta_L^{32})$ plane at 3 TeV (1 ab$^{-1}$) and 14 TeV (20 ab$^{-1}$) muon colliders (Figure 12 of arXiv:2104.05720)?\", \"conditions\": [{\"name\": \"sm_baselines\", \"description\": \"Two SM-only runs (100k events each) at $\\\\sqrt{s}\\\\in\\\\{3, 14\\\\}$ TeV, $\\\\beta_L^{ij}=0$. Define per-bin SM cross section $b_i$ in 10 $|\\\\eta|$ bins.\"}, {\"name\": \"lq_signal_3tev\", \"description\": \"Mass scan over 17 points $m_\\\\mathrm{LQ} \\\\in [1, 70]$ TeV at $\\\\sqrt{s}=3$ TeV, with $\\\\beta_L^{32}\\\\in\\\\{1.0, 2.0\\\\}$, 50k events each.\"}, {\"name\": \"lq_signal_14tev\", \"description\": \"Same mass scan at $\\\\sqrt{s}=14$ TeV with $\\\\beta_L^{32}\\\\in\\\\{1.0, 2.0\\\\}$, 50k events each.\"}], \"baselines\": [\"SM $\\\\mu^+\\\\mu^- \\\\to \\\\gamma^*/Z^* \\\\to b\\\\bar b$ at $\\\\sqrt{s} = 3$ TeV and 14 TeV\"], \"metrics\": [{\"name\": \"exclusion_beta_at_10tev_14tev\", \"direction\": \"match_reference\", \"description\": \"$\\\\beta_L^{32}$ at the 95% CL exclusion contour at $m_\\\\mathrm{LQ}=10$ TeV, $\\\\sqrt{s}=14$ TeV.\"}, {\"name\": \"discovery_beta_at_10tev_14tev\", \"direction\": \"match_reference\", \"description\": \"$\\\\beta_L^{32}$ at the 5$\\\\sigma$ discovery contour at $m_\\\\mathrm{LQ}=10$ TeV, $\\\\sqrt{s}=14$ TeV.\"}, {\"name\": \"I_J_coefficients_per_mass\", \"direction\": \"match_reference\", \"description\": \"$(I_i, J_i)$ per $|\\\\eta|$ bin and mass point, extracted from the $\\\\beta\\\\in\\\\{1, 2\\\\}$ runs; positive-definite check on $J_i$.\"}], \"datasets\": [{\"process_id\": \"mumu_to_bbbar_U1_3and14tev\", \"sqrt_s_TeV\": 14, \"description\": \"$\\\\mu^+\\\\mu^- \\\\to b\\\\bar b$ via SM + $U_1$ exchange at muon colliders, two energy points.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 3600}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_metrics_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) values directly relevant to the headline physics observable named in the experiment_design.metrics list above — these are the numbers the paper will report in its Results section.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_publication_figure\", \"type\": \"artifact\", \"description\": \"At least one publication-quality figure file (PDF or PNG, ≥150 DPI for raster) exists under figures/ or output/figures/ with axes labeled in physical units (GeV / pb / fb / dimensionless) and a legend if multiple series are plotted. The figure must directly support a hypothesis verdict.\", \"must_pass\": true}, {\"id\": \"req_model_implementation\", \"type\": \"artifact\", \"description\": \"The BSM Lagrangian is implemented either as a FeynRules .fr file (models/*.fr) with a matching UFO directory (models/*_UFO/ containing at least particles.py, parameters.py, couplings.py, vertices.py), OR as analytic Python code that explicitly computes the cross sections from the Lagrangian terms. A pure SM baseline with no BSM piece is NOT sufficient.\", \"must_pass\": true}, {\"id\": \"req_14tev_excl_reach\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST report the 95% CL exclusion β_L^32 reach at m_LQ=10 TeV, √s=14 TeV, 20 ab^-1; the value MUST be ≤ 0.02 (published ≤0.01 within a factor of 2).\", \"must_pass\": true}, {\"id\": \"req_mechanistic_writeup\", \"type\": \"discussion\", \"description\": \"The summary or structured_results section provides a one-paragraph mechanistic interpretation of WHY the headline observable comes out the way it does (which interference / propagator structure / cut effect drives the result). Nice-to-have, not blocking proceed.\", \"must_pass\": false}, {\"id\": \"req_mc_reproducibility\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names: (a) the MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"p10-root\", \"requirements\": \"A credible HEP collider study reproducing the published reference figure for $U_1$ exclusion + 5$\\\\sigma$ discovery contours at 3 TeV (1 ab$^{-1}$) and 14 TeV (20 ab$^{-1}$) muon colliders. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.\", \"judging_note\": \"Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"p10-code\", \"requirements\": \"Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p10-code-lagrangian\", \"requirements\": \"BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Same $U_1$ Lagrangian as P09; per-bin cross section parameterized as $\\\\sigma_i(m, \\\\beta) = b_i + \\\\beta^2 I_i(m) + \\\\beta^4 J_i(m)$.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p10-code-process\", \"requirements\": \"MadGraph generate-process card or analytic process module matches the paper's process string: $\\\\mu^+\\\\mu^- \\\\to b\\\\bar b$ at $\\\\sqrt{s}\\\\in\\\\{3, 14\\\\}$ TeV, parton-level.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"p10-code-pdfcuts\", \"requirements\": \"PDF set and generation-level cuts match the paper exactly: Lepton-collider beams; 2 SM baselines (100k events each at 3 and 14 TeV) + 4 LQ signal scans ($\\\\sqrt{s}\\\\in\\\\{3,14\\\\}$ x $\\\\beta_L^{32}\\\\in\\\\{1.0, 2.0\\\\}$) over 17 mass points $m_\\\\mathrm{LQ}\\\\in\\\\{1.0,\\\\ldots,70\\\\}$ TeV with 50k events each.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"p10-code-pipeline\", \"requirements\": \"Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.\", \"weight\": 4.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p10-exec\", \"requirements\": \"Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p10-exec-events\", \"requirements\": \"MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p10-exec-physics\", \"requirements\": \"Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"p10-exec-statistics\", \"requirements\": \"MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p10-results\", \"requirements\": \"Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"p10-result-h1-quant\", \"requirements\": \"Quantitative test of H1 — At $\\\\sqrt{s}=14$ TeV with 20 ab$^{-1}$, 95% CL exclusion reaches $\\\\beta_L^{32} \\\\leq 0.01$ at $m_\\\\mathrm{LQ}=10$ TeV (within factor 2 of published).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p10-result-h2-quant\", \"requirements\": \"Quantitative test of H2 — 14 TeV exclusion contour extends to higher $m_\\\\mathrm{LQ}$ than 3 TeV contour at every fixed $\\\\beta_L^{32}\\\\in[10^{-2}, 1]$. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p10-result-h3-quant\", \"requirements\": \"Quantitative test of H3 — 5$\\\\sigma$ discovery contours always lie above (i.e. require larger $\\\\beta_L^{32}$ than) corresponding exclusion contours at every mass. Use the same graded relative-error scale as H1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p10-result-figure\", \"requirements\": \"A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Log-log plot, x-axis $m_\\\\mathrm{LQ}\\\\in[1,75]$ TeV log, y-axis $\\\\beta_L^{32}\\\\in[10^{-3}, 2]$ log, 1:1 aspect; 3 TeV (red) and 14 TeV (purple) contours; dashed = exclusion, solid = discovery (4 contours total).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"p10-result-writeup\", \"requirements\": \"Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"p10-repro\", \"requirements\": \"Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"p10-repro-model\", \"requirements\": \"UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p10-repro-runcards\", \"requirements\": \"MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}, {\"id\": \"p10-repro-seeds\", \"requirements\": \"RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Reproducibility\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/physics/manifests/P10.yaml", "rubric_file": "tasks/physics/rubrics/P10.json"} diff --git a/data/quantum.jsonl b/data/quantum.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..0996d4a348d20c94ec24bb6e7c23560d8b3b1d7c --- /dev/null +++ b/data/quantum.jsonl @@ -0,0 +1,10 @@ +{"id": "Q01", "domain": "quantum", "title": "Comparing quantum data encoding strategies for variational classifiers", "topic": "Comparing quantum data encoding strategies (angle, amplitude, IQP, ZZ feature map) for variational quantum classifiers on small low-dimensional binary classification tasks", "domains": ["quantum-machine-learning", "data-encoding", "feature-maps"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1800, "synthesis": "Quantum data encoding is widely regarded as the bottleneck of variational\nquantum machine learning. The way classical features are loaded into a\nquantum state determines what functions the subsequent variational ansatz\ncan express. Three encoding families are compared here: angle encoding\n(single-qubit rotations only, no input entanglement), amplitude encoding\n(information-dense state preparation that loads the classical vector as\nstate amplitudes), and the ZZ feature map (Hadamards plus diagonal\npairwise phase rotations, injecting entanglement at the input layer).\n\nThe research question is direct. Holding the variational ansatz, the\nclassical optimizer, the simulator backend, and the dataset preprocessing\nfixed, which encoding produces the best test accuracy on a moderately\ndifficult 6D binary classification task, and does at least one quantum\nencoding approach the better of two classical baselines (logistic\nregression and a small MLP)?\n\nImplementation guidance is provided by the `quantum-qiskit` skill, which\nis automatically injected into the stage-10 code generation prompt for\nthis topic. Use the skill's reference patterns verbatim: import\n`ZFeatureMap`, `StatePreparation`, and `ZZFeatureMap` from\n`qiskit.circuit.library`; use `qiskit_machine_learning.algorithms.VQC`\nwith the reference Sampler primitive for training; never roll a custom\noptimization loop. Hand-written gate sequences in place of these\nlibrary classes have been observed to produce degenerate ablations\n(three encodings collapsing to nearly identical computations) and will\nfail the rubric.\n\nExperimental protocol. All three quantum encodings share EfficientSU2\nreps=1 linear entanglement as the variational ansatz, COBYLA maxiter=200\nas the optimizer, and the noiseless statevector backend as the simulator.\nTwo classical baselines (LogisticRegression and MLPClassifier) run on\nthe same 6D features. Five random seeds per cell across 5 conditions\nyields 25 total cells. Each per-seed result is logged to stdout as a\nsingle line with the prefix `METRIC_RESULT` followed by a JSON object\ncontaining `condition`, `dataset`, `seed`, and `test_accuracy` keys so\nthe autoclaw sandbox parser can collect structured metrics.\n\nResearch question: *On a moderately difficult 6D binary classification\ntask (LogisticRegression baseline expected around 0.65 to 0.80), does\nthe choice of quantum encoding (angle, amplitude, ZZ) measurably change\nvariational classifier accuracy, and does at least one quantum encoding\napproach the better classical baseline?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The best of the three quantum encodings (5-seed mean) achieves test_accuracy within 5 absolute percentage points of the BETTER of the two classical baselines (logistic_regression and mlp_classifier, 5-seed means) on synthetic_classification_6d.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The spread of test_accuracy across the three quantum encodings (max minus min of the 5-seed means) is at least 5 absolute percentage points, demonstrating that encoding choice produces a measurable effect rather than a wash.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"zz_feature_map test_accuracy (5-seed mean) is at least as high as angle_encoding test_accuracy (within 2 absolute percentage points or higher), consistent with the conjecture that entanglement-rich input encoding does not hurt and may help on this task.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"On a moderately difficult 6D binary classification task, does the choice of quantum data encoding (angle, amplitude, ZZ feature map) measurably change variational classifier accuracy, and does at least one encoding approach the classical baseline?\", \"conditions\": [{\"name\": \"angle_encoding\", \"description\": \"qiskit.circuit.library.ZFeatureMap(feature_dimension=6, reps=1) as the data encoding circuit. Each feature x_i is loaded as H followed by RZ(x_i) on qubit i. No entangling gates in the feature map.\"}, {\"name\": \"amplitude_encoding\", \"description\": \"qiskit.circuit.library.StatePreparation on the L2-normalized, zero-padded input vector (length 64 = 2**6) as the data encoding circuit. Implementation: x_normalized = x / ||x||; x_padded = np.concatenate([x_normalized, np.zeros(64 - 6)]); StatePreparation(x_padded). The padded state is unit norm by construction.\"}, {\"name\": \"zz_feature_map\", \"description\": \"qiskit.circuit.library.ZZFeatureMap(feature_dimension=6, reps=2, entanglement='linear') as the data encoding circuit. Two repetitions of H + RZ(x_i) on each qubit + RZZ(x_i * x_j) on linear nearest-neighbor pairs.\"}], \"baselines\": [\"logistic_regression: sklearn.linear_model.LogisticRegression(max_iter=1000) on the same 6D features.\", \"mlp_classifier: sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(16,), activation='relu', solver='adam', max_iter=500, random_state=seed) on the same 6D features.\", \"Both baselines reported per seed; both contribute to the H1 comparison (the BETTER of the two is the reference). Neither contributes to the primary quantum_test_accuracy_mean metric.\"], \"metrics\": [{\"name\": \"quantum_test_accuracy_mean\", \"direction\": \"maximize\", \"description\": \"Primary metric. Mean of the 5-seed test_accuracy means across the THREE quantum encodings ONLY. The classical baseline is reported separately and does NOT contribute to this metric. This isolates the refinement signal from the baseline.\"}, {\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Per-condition mean accuracy on the 20% held-out test split, averaged over 5 random seeds. Reported per condition (angle, amplitude, zz_feature_map, logistic_regression).\"}, {\"name\": \"best_baseline_accuracy\", \"direction\": \"maximize\", \"description\": \"max(logistic_regression_5seed_mean, mlp_classifier_5seed_mean). Used as the reference point in H1 (the stronger of the two classical baselines).\"}], \"datasets\": [{\"name\": \"synthetic_classification_6d\", \"source\": \"sklearn.datasets.make_classification\", \"description\": \"make_classification(n_samples=400, n_features=6, n_informative=4, n_redundant=1, n_repeated=0, n_clusters_per_class=2, class_sep=0.4, flip_y=0.05, random_state=seed). class_sep is set to 0.4 (not 1.0) so the LogisticRegression baseline lands around 0.65-0.80 instead of saturating near 1.0, leaving headroom for the quantum encodings to differ. Apply StandardScaler fit on the train split only. 80/20 train/test split.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1800}}", "requirements": "", "rubric": "{\"id\": \"q01-root\", \"requirements\": \"A minimal pilot study comparing three quantum data encoding strategies (angle, amplitude, ZZ feature map) for a variational quantum classifier on ONE moderately difficult 6D binary classification dataset (synthetic_classification_6d with class_sep=0.4). The agent must (a) implement all three encodings as drop-in feature maps for a shared 6-qubit EfficientSU2 ansatz, (b) train each (encoding, seed) cell using qiskit_machine_learning.algorithms.VQC + COBYLA over 5 random seeds, (c) report test accuracy per quantum cell and a classical logistic_regression baseline on the same data, and (d) produce a writeup that assigns supported / refuted / inconclusive verdicts to H1, H2, H3 with numerical evidence.\", \"judging_note\": \"Quantum ML pilots are scored on (i) whether all three encodings are implemented per qiskit convention, (ii) whether ansatz / optimizer / simulator are held fixed across encodings so the encoding axis is isolated, (iii) whether the quantum cells produce non-trivial accuracy (not near 0.5 random for all 3 encodings, which would indicate a training bug rather than an encoding-specific effect), and (iv) whether per-hypothesis claims are backed by numerical evidence. Quantitative result leaves are scored on a graded scale: 100% if the hypothesis threshold is cleanly met, 67% if the trend is in the predicted direction but does not clear the numerical threshold, 33% if the trend is ambiguous, 0% if the result contradicts the hypothesis or evidence is missing.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q01-code\", \"requirements\": \"Code-development bucket: all 3 encodings implemented per qiskit convention, shared ansatz + optimizer + simulator + training framework are wired identically across cells, and per-cell metrics are logged in the expected METRIC_RESULT JSON format.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q01-code-encodings\", \"requirements\": \"All three encodings are implemented by IMPORTING and INSTANTIATING the canonical classes from qiskit.circuit.library by name, NOT by hand-writing the gate sequences. Concretely the agent's code MUST contain (grep-verifiable): 'from qiskit.circuit.library import' followed by at least the symbols ZFeatureMap, StatePreparation, and ZZFeatureMap; and the three encoding builders must instantiate exactly those classes (angle_encoding = ZFeatureMap(feature_dimension=6, reps=1); amplitude_encoding wraps StatePreparation on a 64D L2-normalized zero-padded vector; zz_feature_map = ZZFeatureMap(feature_dimension=6, reps=2, entanglement='linear')). Hand-written H/RZ/RZZ loops in place of these library classes FAIL this leaf. Additionally, a sanity check at runtime confirms that the three encodings produce DIFFERENT output statevectors for a fixed non-zero input (if they produce identical statevectors, the dispatch is broken and this leaf scores 0). Class bodies are non-trivial (not 'class X(Base): pass' shells).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q01-code-shared-training\", \"requirements\": \"Training uses qiskit_machine_learning.algorithms.VQC with the reference Sampler primitive. The variational ansatz (EfficientSU2 num_qubits=6 reps=1 entanglement='linear'), the classical optimizer (COBYLA maxiter=200), and the simulator backend (AerSimulator method='statevector') are identical across the 3 quantum encoding cells. Only the feature_map argument is swapped. No hand-rolled training loop in place of VQC.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"q01-code-metric-logging\", \"requirements\": \"Each per-(condition, seed) result is emitted to stdout as a single line beginning with METRIC_RESULT followed by a JSON object whose keys include at minimum: condition (one of angle_encoding / amplitude_encoding / zz_feature_map / logistic_regression / mlp_classifier), dataset (synthetic_classification_6d), seed (integer 0..4), test_accuracy (float in [0,1]). Both classical baselines (LogisticRegression and MLPClassifier) are logged in the same format.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q01-exec\", \"requirements\": \"Execution-validity bucket: all cells ran and produced numerically valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q01-exec-cells-ran\", \"requirements\": \"At least 25 cells out of 25 expected (5 conditions: 3 quantum encodings + LogisticRegression + MLPClassifier x 1 dataset x 5 seeds) completed without unhandled errors and produced an accuracy value. Missing more than 2 cells (10 percent) without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q01-exec-numerical\", \"requirements\": \"Numerical validity: no NaN or Inf in test_accuracy. Every test_accuracy lies in [0, 1]. No quantum cell collapses to a degenerate single-class prediction. At least one quantum encoding achieves test_accuracy strictly greater than 0.55 on at least 1 of 5 seeds (rules out the failure mode where all quantum cells stay near 0.5 random-chance because optimization never actually ran). The 3 quantum encodings MUST NOT produce identical test_accuracy across all 5 seeds (this is the ABLATION FAILURE pattern observed in a prior failed run: if angle/amplitude/zz produce bit-for-bit identical accuracies on every seed, the feature_map argument is being silently ignored by the training code, and this leaf scores 0). Training takes nontrivial wall time per cell (at least a few seconds; sub-1s per cell means VQC.fit was not actually invoked).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q01-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q01-result-h1-quant\", \"requirements\": \"Quantitative test of H1. Does the best of the 3 quantum encodings (5-seed mean) achieve test_accuracy within 5 absolute percentage points of the BETTER classical baseline (max of LogisticRegression and MLPClassifier 5-seed means)? 100% if gap is <= 5pp, 67% if gap is <= 10pp, 33% if gap is <= 20pp, 0% if quantum is more than 20pp worse than the best baseline (or either baseline is missing).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q01-result-h2-quant\", \"requirements\": \"Quantitative test of H2. Is max(encoding 5-seed mean) - min(encoding 5-seed mean) across the 3 quantum encodings at least 5 absolute percentage points? 100% if spread >= 5pp, 67% if spread >= 2pp, 33% if spread > 0, 0% if all three encodings produce identical means (suggests training did not actually depend on the encoding choice).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q01-result-h3-quant\", \"requirements\": \"Quantitative test of H3. Is zz_feature_map test_accuracy (5-seed mean) at least as high as angle_encoding test_accuracy (within 2 absolute pp lower, or higher)? 100% if zz >= angle - 2pp, 67% if zz is 2-5pp below angle, 33% if zz is 5-10pp below, 0% if zz is more than 10pp below angle.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q01-result-writeup\", \"requirements\": \"Writeup of at least 200 words (in submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1, H2, H3 backed by specific mean accuracies (per encoding plus baseline) and the gap / spread values. Identifies a dominant systematic uncertainty (seed variance, optimizer convergence on the simpler EfficientSU2 reps=1 ansatz, class_sep=0.4 difficulty level). Discusses whether the result would change at higher difficulty or with a deeper ansatz.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q01.yaml", "rubric_file": "tasks/quantum/rubrics/Q01.json"} +{"id": "Q02", "domain": "quantum", "title": "Entangling-gate ablation in a variational quantum classifier", "topic": "Ablating entangling gates in a variational quantum classifier: how much classification accuracy is actually attributable to CNOTs versus single-qubit rotations", "domains": ["quantum-machine-learning", "entanglement", "ablation-study"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1200, "synthesis": "Quantum machine learning models routinely use parameterized circuits\nthat mix single-qubit rotations with entangling two-qubit gates,\ntypically CNOTs. The common narrative is that entanglement is the\nsource of quantum advantage. If a variational classifier with no\nentangling gates whatsoever can match the accuracy of one with full\nentanglement, that narrative breaks down for this task class. The\npurpose of this study is to measure the actual contribution of\nentanglement in a simple variational quantum classifier by cleanly\nremoving CNOTs in a controlled monotonic manner.\n\nThe design isolates entanglement to one source. The data encoding is\nfixed to angle encoding (single-qubit rotations only, no entangling\ngates). The variational ansatz template is EfficientSU2 with reps=3,\nwhich by default places a linear chain of CNOTs between each rotation\nblock (5 CNOTs per entangling layer, 3 layers, 15 CNOTs total for\n6 qubits). We ablate by replacing some or all of those CNOTs with\nidentity, while keeping every single-qubit rotation parameter intact.\nThis isolates the gain attributable to two-qubit entangling structure\nfrom the gain attributable to the trainable single-qubit basis change.\nhalf_entanglement uses an independent random Bernoulli(0.5) mask over\nthe 15 CNOT positions per cell, seeded deterministically so the\nablation is reproducible but not aligned with any fixed pattern.\n\nA parameter-matched classical baseline (sklearn MLPClassifier with a\nhidden layer sized so its total parameter count is within 10 percent\nof the VQC parameter count) tells us whether either quantum condition\nis competitive with a trivial classical alternative on the same data.\nIf the no-entanglement VQC underperforms the classical MLP, the\nconclusion is that on these tasks, entangling layers are not just\nhelpful but necessary for the variational quantum model to be a\nviable choice at all.\n\nDatasets are deliberately shared with Q01 (encoding comparison) so\nresults can be read across both studies. If the cross-topic story\nholds, the encoding family that benefits most from ansatz CNOTs in\nQ02 should also be the family that performed worst in Q01 (because\nthe ansatz CNOTs are compensating for the encoding's lack of input\nentanglement). The current manifest does not require that\ncross-comparison, but the writeup is encouraged to discuss it.\n\nResearch question: *In a variational quantum classifier on low\ndimensional binary classification, how much of the model's accuracy\nis actually due to ansatz entangling gates, and does the accuracy\ndegrade monotonically as CNOTs are removed?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"full_entanglement achieves mean test_accuracy (averaged over 5 seeds) at least 5 absolute percentage points higher than no_entanglement on at least 2 of 3 datasets.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"half_entanglement mean test_accuracy lies strictly between full_entanglement and no_entanglement (within seed-mean noise of 1 absolute percentage point) on at least 2 of 3 datasets, demonstrating that the contribution of entangling layers is approximately monotonic in CNOT count.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"On at least 1 of 3 datasets, classical_baseline (parameter-matched MLPClassifier) achieves test_accuracy at least as high as no_entanglement (5-seed mean). This sanity check rules out a trivial-task explanation: if removing all entanglement leaves the VQC weaker than a tiny classical net, then 'no entanglement' is not a viable QML setting on these tasks.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"In a variational quantum classifier on low dimensional binary classification, how much of the model's accuracy is actually due to ansatz entangling gates, and does the accuracy degrade monotonically as CNOTs are removed?\", \"conditions\": [{\"name\": \"full_entanglement\", \"description\": \"Feature map: ZFeatureMap(feature_dimension=6, reps=1). Ansatz: EfficientSU2(num_qubits=6, reps=3, entanglement='linear'). All 15 CNOTs from the 3 entangling layers are kept. This is the baseline VQC.\"}, {\"name\": \"half_entanglement\", \"description\": \"Same feature_map and ansatz template as full_entanglement, but each of the 15 CNOTs is independently dropped according to a random Bernoulli(0.5) mask. The mask is generated per cell using a derived random state: np.random.RandomState(seed + 10000).binomial(1, 0.5, size=15), where seed is the per-cell run seed. Dropped CNOTs are replaced with identity. Each cell records its realized cnot_count (expected ~7.5, variance per draw). Trainable parameters unchanged.\"}, {\"name\": \"no_entanglement\", \"description\": \"Same feature_map and rotation blocks as full_entanglement, but EVERY entangling gate is replaced with identity. The circuit reduces to a tensor product of independent single-qubit gates. CNOT count is 0. Trainable parameters unchanged.\"}, {\"name\": \"classical_baseline\", \"description\": \"sklearn.neural_network.MLPClassifier with hidden_layer_sizes chosen so the total trainable parameter count is within 10 percent of the VQC (EfficientSU2 reps=3 on 6 qubits has 48 rotation parameters; an MLP with one hidden layer of size 6 has 6*6 + 6 + 6*1 + 1 = 49 params). Use hidden_layer_sizes=(6,), activation='relu', max_iter=500, solver='lbfgs', random_state matched to the corresponding seed.\"}], \"baselines\": [\"no_entanglement is the within-quantum ablation baseline\", \"classical_baseline is the cross-paradigm sanity baseline\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Mean over 5 seeds of held-out 20% test split accuracy.\"}, {\"name\": \"convergence_iterations\", \"direction\": \"minimize\", \"description\": \"Number of COBYLA iterations (or MLP solver iterations for classical_baseline) until training loss change drops below 1e-4 across 5 consecutive evaluations.\"}, {\"name\": \"cnot_count\", \"direction\": \"minimize\", \"description\": \"Number of CNOT gates remaining in the variational circuit (feature map + ansatz) after ablation. Informational metric. Expected: full=15, half=mean over draws (~7.5), no=0, classical_baseline=0.\"}], \"datasets\": [{\"name\": \"synthetic_classification_6d\", \"source\": \"sklearn.datasets.make_classification\", \"description\": \"make_classification(n_samples=200, n_features=6, n_informative=4, n_redundant=1, n_repeated=0, n_clusters_per_class=2, class_sep=1.0, flip_y=0.05, random_state=seed). Binary classification, native 6D. Apply StandardScaler. 80/20 train/test split. Same configuration as Q01.\"}, {\"name\": \"wine_binary_pca6\", \"source\": \"sklearn.datasets.load_wine + sklearn.decomposition.PCA\", \"description\": \"load_wine (178 samples, 13 features, 3 classes). Select classes 0 and 1 only (binary subset, ~130 samples). Apply StandardScaler then PCA(n_components=6). 80/20 train/test split. Same configuration as Q01.\"}, {\"name\": \"breast_cancer_pca6\", \"source\": \"sklearn.datasets.load_breast_cancer + sklearn.decomposition.PCA\", \"description\": \"load_breast_cancer (569 samples, 30 features). Apply StandardScaler then PCA(n_components=6). 80/20 train/test split. Same configuration as Q01.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1200}}", "requirements": "", "rubric": "{\"id\": \"q02-root\", \"requirements\": \"A controlled ablation study of entangling gates in a variational quantum classifier. Holding encoding (angle), ansatz template (EfficientSU2 reps=2), optimizer (COBYLA), and datasets fixed (matching Q01), the agent must implement full / half / no entanglement variants by selectively replacing CNOTs with identity in the ansatz, plus a parameter-matched classical MLP baseline. Score H1 (full > no by >=5pp), H2 (half between full and no), H3 (classical >= no on at least 1 dataset) with numerical evidence.\", \"judging_note\": \"Ablation studies are scored on (i) whether the CNOT-removal mechanism actually changes the circuit as described (verified by inspecting cnot_count per condition), (ii) whether every other variable is held fixed (same encoding, same rotation parameters, same datasets, same optimizer, same seeds), and (iii) whether the H1/H2/H3 verdicts are backed by numerical evidence. The classical baseline must be parameter-matched (within 10 percent of the VQC's trainable parameter count) to be a meaningful sanity check.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q02-code\", \"requirements\": \"Code-development bucket: the CNOT ablation mechanism is correctly implemented, the classical baseline is parameter-matched, and the evaluation loop holds all non-ablated variables fixed.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q02-code-ablation-mechanism\", \"requirements\": \"All three quantum conditions (full_entanglement, half_entanglement, no_entanglement) use the same ZFeatureMap and the same EfficientSU2(reps=3) rotation parameters. CNOTs are removed by replacing them with identity (not by re-parameterizing the ansatz). The resulting circuits have cnot_count = 15 for full, 0 for no, and a random Bernoulli(0.5) draw over 15 positions for half (mean ~7.5, verified by recording the realized count per cell). The half_entanglement mask is generated via np.random.RandomState(seed + 10000).binomial(1, 0.5, size=15) to be reproducible.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q02-code-classical-baseline\", \"requirements\": \"classical_baseline uses sklearn MLPClassifier with a hidden layer size chosen so the total parameter count is within 10 percent of the VQC's 48 trainable parameters (e.g. hidden_layer_sizes=(6,) gives 49 params for 6-dim input). max_iter, activation, solver, and random_state are specified explicitly. The MLP is trained and evaluated on the SAME train/test splits as the VQC conditions.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"q02-code-pipeline\", \"requirements\": \"Nested loop over 4 conditions x 3 datasets x at least 5 seeds. Each cell records test_accuracy, convergence_iterations, and cnot_count. The same seed produces the same train/test split across the 4 conditions on each dataset (so the 4 conditions can be paired-compared).\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q02-exec\", \"requirements\": \"Execution-validity bucket: all cells ran and produced numerically valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q02-exec-cells-ran\", \"requirements\": \"At least 60 cells out of 60 expected (4 conditions x 3 datasets x 5 seeds) completed and produced an accuracy value. Missing cells must be documented; missing more than 6 cells (10 percent) without justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q02-exec-numerical\", \"requirements\": \"Numerical validity: no NaN or Inf in test_accuracy. Every test_accuracy in [0, 1]. cnot_count matches the manifest expectation per condition (full=15, half=Bernoulli draw mean ~7.5 with all values in [0,15], no=0, classical_baseline=0). At least one cell of each condition produces a non-degenerate classifier (not predicting a single class for all test samples).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q02-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q02-result-h1-quant\", \"requirements\": \"Quantitative test of H1. Does full_entanglement beat no_entanglement by at least 5 absolute percentage points (5-seed mean) on at least 2 of 3 datasets? 100% if cleanly met, 67% if gap >= 2pp on 2/3, 33% if full > no on 2/3 but gap <2pp, 0% if full does not consistently beat no.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q02-result-h2-quant\", \"requirements\": \"Quantitative test of H2. Does half_entanglement test_accuracy lie strictly between full and no (within 1pp seed-mean noise) on at least 2 of 3 datasets? 100% if cleanly monotonic on 2/3, 67% if monotonic on 1/3, 33% if half is non-monotonic but within 2pp of one of the endpoints, 0% otherwise.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q02-result-h3-quant\", \"requirements\": \"Quantitative test of H3. On at least 1 of 3 datasets, does classical_baseline achieve test_accuracy at least as high as no_entanglement (5-seed mean)? 100% if classical >= no on at least 1/3, 50% if classical is within 2pp of no on at least 1/3, 0% if classical never reaches no_entanglement minus 2pp.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q02-result-writeup\", \"requirements\": \"Writeup of at least 200 words (in submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1, H2, H3 with numerical evidence (specific mean accuracies for each condition x dataset, and the gap), a discussion of monotonicity, and an honest acknowledgment of whether the classical_baseline sanity check passes. The writeup is encouraged but not required to cross-reference Q01 (encoding) findings if available.\", \"weight\": 14.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q02.yaml", "rubric_file": "tasks/quantum/rubrics/Q02.json"} +{"id": "Q03", "domain": "quantum", "title": "Classical optimizer comparison for VQE on H2 under finite shot noise", "topic": "Benchmarking classical optimizers (SPSA, COBYLA, L-BFGS-B, ADAM) for VQE convergence on H2 under finite shot noise across multiple shot budgets and bond lengths", "domains": ["variational-quantum-eigensolver", "quantum-chemistry", "classical-optimization"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "shots_to_chemical_accuracy", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 1200, "synthesis": "The Variational Quantum Eigensolver (VQE) is the canonical algorithm\nfor near-term quantum chemistry. The quality of a VQE result depends\non the ansatz, but it also critically depends on the classical\noptimizer that updates variational parameters using noisy energy\nestimates from shot-based measurement. Different optimizers exploit\ngradient information very differently. Gradient-free methods such as\nCOBYLA and Nelder-Mead do not need explicit gradients and may be\nrobust to shot noise but converge slowly in high dimensions.\nSimultaneous Perturbation Stochastic Approximation (SPSA) approximates\nthe gradient using just two function evaluations regardless of\nparameter count, trading gradient quality for evaluation count and\noften dominating in low-shot regimes. Gradient-based methods such as\nL-BFGS-B and ADAM need explicit or finite-difference gradients, which\nare expensive under shot noise because each partial derivative is\nestimated from a noisy function value.\n\nThe choice of optimizer is rarely studied head-to-head under a fixed\nshot budget. Most VQE papers fix the optimizer and tune everything\nelse around it. Here we hold ansatz (hardware-efficient EfficientSU2\nreps=2), Hamiltonian (H2 in STO-3G basis), and initial parameters\n(small random Gaussian) fixed, and vary only the optimizer and the\nshot budget per energy evaluation. The diagnostic question is which\noptimizer reaches chemical accuracy (within 1.6 mHa of the FCI\nreference) using the fewest cumulative shots.\n\nA credible study uses at least two molecular geometries to avoid\noverfitting to a single energy landscape. We use H2 at two bond\nlengths: the equilibrium distance (0.74 angstrom) where the ground\nstate is near Hartree-Fock and the optimization landscape is well\nbehaved, and a stretched geometry (1.5 angstrom) where multireference\ncharacter makes the landscape harder and tests optimizer robustness.\nEach optimizer is run at a SINGLE fixed shot budget of 1024 shots per\nenergy evaluation. This is chosen as a representative middle-ground\nvalue: low enough that shot noise is non-trivial (per-eval std ~15 mHa,\nwell above the 1.6 mHa chemical accuracy threshold, so the running-mean\nconvergence criterion matters), but high enough that some optimizers\ncan plausibly converge within their iteration budgets. All cells use\n3 random seeds per (optimizer, geometry) cell, giving 4 optimizers x\n1 shot budget x 2 geometries x 3 seeds = 24 total cells. Cumulative\nshots is the sum of shots used across all energy evaluations\n(including finite-difference gradient evaluations where applicable).\nThe Hamiltonian itself is constructed via\nqiskit_nature.second_q.drivers.PySCFDriver (qiskit_nature and pyscf\nare required dependencies for this topic; hardcoded Pauli string\nfallbacks are NOT accepted).\n\nImplementation contract (see quantum-qiskit skill for the reference\ncode). In qiskit 2.x, `qiskit_algorithms.VQE` and\n`qiskit_nature.second_q.algorithms` are NOT importable (they depend on\nthe removed `qiskit.primitives.BaseEstimator` V1 interface). VQE MUST\nbe implemented as a manual optimization loop:\nenergy(theta) = estimator.run([(bound_ansatz, qubit_op)]).result()[0].data.evs + e_nuclear,\nusing `qiskit.primitives.StatevectorEstimator` (the V2 primitive that\nworks in qiskit 2.x) for the energy evaluation, and one of the four\noptimizer classes from `qiskit_algorithms.optimizers` (SPSA, COBYLA,\nL_BFGS_B, ADAM) for the parameter update, called via\n`optimizer.minimize(energy, initial_point)`. Each call to `energy`\ncounts as one evaluation; cumulative shots is\n`n_evaluations * shots_per_eval`. The Hamiltonian itself is constructed\nvia qiskit_nature.second_q.drivers.PySCFDriver +\nqiskit_nature.second_q.mappers.ParityMapper (these submodules ARE safe\nto import in qiskit 2.x — only the qiskit_nature.second_q.algorithms\nmodule is broken). With ParityMapper and 2-qubit reduction\n(num_particles=(1,1)), the H2/STO-3G Hamiltonian is 2 qubits, not 4.\nImplement shot noise by adding Gaussian noise to each energy value\nwith sigma = ||H||_1 / sqrt(shots_per_eval). The convergence criterion\nfor shots_to_chemical_accuracy is the cumulative shots when a 5-eval\nrunning mean energy stays within 1.6 mHa of E_FCI for 5 consecutive\nevaluations; if not reached, report None or a sentinel distinct from\nthe upper bound so analysis can flag the cell as \"did not converge\"\nrather than treating the budget cap as a measurement.\n\nResearch question: *Under finite shot noise, which classical optimizer\n(SPSA, COBYLA, L-BFGS-B, ADAM) reaches VQE chemical accuracy using the\nfewest cumulative shots, and how does the answer depend on shot budget\nper evaluation and on Hamiltonian difficulty (equilibrium vs stretched\nH2)?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At 1024 shots per energy evaluation, SPSA reaches chemical accuracy (|E_running_mean - E_FCI| < 1.6 mHa for 5 consecutive 5-eval windows) using fewer cumulative shots than L-BFGS-B (3-seed median), on at least 1 of 2 H2 geometries.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"At 1024 shots per energy evaluation, gradient-free methods (SPSA and COBYLA, 6 seed-runs combined across both geometries) achieve chemical accuracy with success rate at least 4/6, while gradient-based methods (L-BFGS-B and ADAM, 6 seed-runs combined) achieve it with success rate at most 3/6. This isolates the shot-noise sensitivity of finite-difference gradient estimation.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Optimizer ranking by shots_to_chemical_accuracy changes between H2 equilibrium and H2 stretched geometries: at least one optimizer pair (e.g. SPSA vs COBYLA, or SPSA vs L-BFGS-B) swaps relative ranking between the two geometries, indicating that Hamiltonian difficulty changes optimizer choice.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Under finite shot noise, which classical optimizer reaches VQE chemical accuracy in the fewest cumulative shots, and how does the answer depend on shot budget per evaluation and on Hamiltonian difficulty?\", \"conditions\": [{\"name\": \"spsa\", \"description\": \"qiskit_algorithms.optimizers.SPSA(maxiter=200, learning_rate=0.05, perturbation=0.1). Fixed shot budget: 1024 shots per energy evaluation. Each iteration costs 2 energy evaluations.\"}, {\"name\": \"cobyla\", \"description\": \"qiskit_algorithms.optimizers.COBYLA(maxiter=200, rhobeg=0.1, tol=1e-4). 1024 shots per energy evaluation. Each iteration costs 1 energy evaluation.\"}, {\"name\": \"lbfgsb\", \"description\": \"qiskit_algorithms.optimizers.L_BFGS_B(maxiter=100, ftol=1e-6) with finite-difference gradients (epsilon=1e-3). 1024 shots per energy evaluation. Each iteration costs ~2*num_params energy evaluations for the gradient plus 1 for the function value (so ~25 evals per iter for EfficientSU2 reps=2 on 4 qubits).\"}, {\"name\": \"adam\", \"description\": \"qiskit_algorithms.optimizers.ADAM(maxiter=200, lr=0.05, beta_1=0.9, beta_2=0.999) with finite-difference gradients (epsilon=1e-3). 1024 shots per energy evaluation. Same per-iter cost as L-BFGS-B.\"}], \"baselines\": [\"Cumulative shots vs. FCI energy reference is the within-method benchmark\", \"Hartree-Fock energy at each geometry is the trivial classical baseline below which any optimizer should clear\"], \"metrics\": [{\"name\": \"shots_to_chemical_accuracy\", \"direction\": \"minimize\", \"description\": \"Cumulative shots until |E_running_mean - E_FCI| stays below 1.6 mHa for 5 consecutive evaluations. If not reached within the maxiter budget, report the total cumulative shots used (sentinel = max-shots) and flag success=False.\"}, {\"name\": \"final_energy_error_hartree\", \"direction\": \"minimize\", \"description\": \"|E_final - E_FCI| in Hartree at the end of optimization (after all maxiter iterations).\"}, {\"name\": \"success_rate\", \"direction\": \"maximize\", \"description\": \"Fraction of seeds (out of 3 per condition x geometry) where the run reached chemical accuracy by the maxiter budget.\"}], \"datasets\": [{\"name\": \"h2_equilibrium\", \"source\": \"qiskit_nature.second_q.drivers.PySCFDriver (REQUIRED dependency)\", \"description\": \"H2 in STO-3G basis at bond length 0.74 angstrom. Build the FermionicOp via PySCFDriver, apply ParityMapper with 2-qubit reduction to obtain a 4-qubit qubit Hamiltonian (alternatively JordanWignerMapper + tapering). FCI reference: E_FCI = -1.137283 Ha. Hardcoded Pauli strings are NOT an acceptable substitute for this benchmark; both qiskit_nature and pyscf must be installed and used.\"}, {\"name\": \"h2_stretched\", \"source\": \"qiskit_nature.second_q.drivers.PySCFDriver (REQUIRED dependency)\", \"description\": \"H2 in STO-3G basis at bond length 1.5 angstrom (multireference / strong-correlation regime). 4 qubits via the same mapping pipeline as h2_equilibrium. FCI reference: E_FCI ~ -1.001 Ha. Hardcoded Pauli strings NOT accepted.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1200}}", "requirements": "", "rubric": "{\"id\": \"q03-root\", \"requirements\": \"A credible VQE optimizer comparison on H2 in STO-3G basis at two bond lengths (0.74 A equilibrium, 1.5 A stretched), at a fixed shot budget of 1024 shots per energy evaluation. The agent must (a) construct the 4-qubit H2 Hamiltonian at each geometry via qiskit_nature.PySCFDriver, (b) implement VQE via qiskit_algorithms.VQE.compute_minimum_eigenvalue() with a hardware-efficient EfficientSU2 reps=2 ansatz, (c) run 4 optimizers (SPSA, COBYLA, L-BFGS-B, ADAM) x 2 geometries x 3 seeds = 24 cells, (d) log cumulative shots and energy trajectories per cell, and (e) score H1/H2/H3 with numerical evidence keyed by (optimizer, geometry).\", \"judging_note\": \"Optimizer comparisons are scored on (i) correctness of the Hamiltonian (FCI reference within 1 mHa of -1.137 Ha for H2 equilibrium), (ii) consistent ansatz / initial parameters across cells so only the optimizer and shot budget vary, (iii) accurate accounting of cumulative shots including gradient evaluations for L-BFGS-B and ADAM, and (iv) numerical evidence backing H1/H2/H3. qiskit_nature.second_q.drivers.PySCFDriver MUST be used to build the Hamiltonian. Hardcoded Pauli strings are NOT acceptable and a submission that uses them fails q03-code-hamiltonians.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q03-code\", \"requirements\": \"Code-development bucket: VQE pipeline correctly implements 4 optimizers, both H2 geometries, and accurate cumulative-shot tracking.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q03-code-vqe\", \"requirements\": \"VQE is implemented using qiskit_algorithms.VQE or an equivalent custom loop. The ansatz is EfficientSU2(num_qubits=4, reps=2, entanglement='linear'). Initial parameters are sampled from N(0, 0.1) with the same seed across all 4 optimizers for a given (shot_budget, geometry, seed) triple. All 4 optimizers are correctly instantiated from qiskit_algorithms.optimizers (SPSA, COBYLA, L_BFGS_B, ADAM) with the maxiter and learning rates listed in the manifest.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q03-code-hamiltonians\", \"requirements\": \"Both H2 Hamiltonians are constructed at the correct bond lengths (0.74 and 1.5 angstrom) in STO-3G basis using qiskit_nature.second_q.drivers.PySCFDriver (REQUIRED) and either ParityMapper with 2-qubit reduction or JordanWignerMapper with tapering. The diagonalized Hamiltonian (numpy eigvalsh of the matrix form) gives FCI energies within 1 mHa of -1.137 Ha (equilibrium) and -1.001 Ha (stretched). Hardcoded Pauli strings as a substitute for PySCFDriver are NOT accepted and fail this leaf.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q03-code-shot-tracking\", \"requirements\": \"Cumulative shots is tracked correctly per run. For SPSA and COBYLA, each iteration adds shots_per_eval * num_evaluations_per_iter (2 for SPSA, 1 for COBYLA). For L-BFGS-B and ADAM with finite-difference gradients, each iteration adds shots_per_eval * (2 * num_params + 1) so the gradient evaluations are accounted for. The shots_to_chemical_accuracy metric is computed using the running mean energy (over the last 5 evaluations) and reports the cumulative shot count at the first evaluation where the mean stays within 1.6 mHa of E_FCI for 5 consecutive evaluations.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q03-exec\", \"requirements\": \"Execution-validity bucket: all cells ran with valid energies and shot accounting.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q03-exec-cells-ran\", \"requirements\": \"At least 22 cells out of 24 expected (4 optimizers x 1 shot budget (1024) x 2 geometries x 3 seeds = 24) completed and produced a final_energy_error_hartree value. If any cells are omitted (e.g. ADAM ran out of budget), they must be documented with a recorded cause. Missing more than 2 cells (10 percent) without justification fails this leaf. The condition_summaries MUST be keyed by (optimizer, geometry) so each of the 8 condition cells has 3 seed entries — collapsing across the geometry axis (e.g. reporting only per-optimizer aggregates) fails this leaf because it makes H1 and H3 unevaluable.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q03-exec-numerical\", \"requirements\": \"Numerical validity: every final energy is finite (no NaN/Inf), every final_energy_error_hartree >= 0, every success_rate in [0, 1], cumulative shots is monotone increasing within a run. At least one cell across the entire sweep reaches chemical accuracy (otherwise the rubric thresholds are uncalibrated for this setup).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q03-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q03-result-h1-quant\", \"requirements\": \"Quantitative test of H1. On at least 1 of 2 geometries at 1024 shots/eval, does SPSA reach chemical accuracy with fewer median cumulative shots than L-BFGS-B (over 3 seeds)? 100% if SPSA wins on at least 1/2 geometries with a >=20 percent shot reduction, 67% if SPSA wins on at least 1/2 with any margin, 33% if SPSA and L-BFGS-B are within 10 percent of each other, 0% if L-BFGS-B is consistently faster.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q03-result-h2-quant\", \"requirements\": \"Quantitative test of H2. At 1024 shots/eval, pooling across both geometries: do gradient-free methods (SPSA + COBYLA, total of 6 seed-runs = 2 methods x 3 seeds) have a combined chemical-accuracy success rate of at least 4/6, while gradient-based methods (L-BFGS-B + ADAM, 6 seed-runs) have at most 3/6? 100% if cleanly met, 67% if the gradient-free success rate exceeds gradient-based by at least 30 percent, 33% if gradient-free exceeds gradient-based at all, 0% if gradient-based methods are equal or better.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q03-result-h3-quant\", \"requirements\": \"Quantitative test of H3. Does the optimizer ranking by median shots_to_chemical_accuracy change between H2 equilibrium and H2 stretched geometries? Specifically: take the 4-element ranking of optimizers (by 3-seed-median shots) on each geometry; H3 is supported if at least one pair swaps relative order between the two rankings (Kendall tau distance >= 1 between the two rankings). 100% if at least one swap detected with clear evidence, 67% if one optimizer's rank differs by exactly 1 position, 33% if rankings are similar but values differ, 0% if rankings are identical or H3 is unevaluable (e.g. due to collapsed condition_summaries that drop the geometry axis).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q03-result-writeup\", \"requirements\": \"Writeup of at least 200 words (in submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1, H2, H3 with numerical evidence (specific median cumulative shots, success rates, and final energy errors per optimizer x shot budget). Identifies a dominant systematic uncertainty (seed variance, finite-difference epsilon, optimizer hyperparameter sensitivity, shot-noise variance at low budgets). Discusses the difference between equilibrium and stretched H2 in terms of optimizer ranking.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q03.yaml", "rubric_file": "tasks/quantum/rubrics/Q03.json"} +{"id": "Q04", "domain": "quantum", "title": "Data re-uploading depth vs Fourier expressivity for variational quantum regression", "topic": "Data re-uploading depth vs Fourier expressivity for variational quantum regression: characterizing how the trainable bandwidth of a parameterized re-uploading circuit scales with re-uploading layers L on synthetic 1D regression targets", "domains": ["quantum-machine-learning", "data-reuploading", "expressivity"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_mse", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 900, "synthesis": "Data re-uploading is a variational quantum circuit pattern in which the\nclassical input x is loaded into the same qubits multiple times,\ninterleaved with trainable rotation layers: the circuit\nW(theta_L) U(x) W(theta_{L-1}) U(x) ... W(theta_1) U(x) |0> applies L\nencoding blocks U(x) and L trainable blocks W(theta). Perez-Salinas et al.\nproved in 2020 that a single qubit with sufficient re-uploading depth is\na universal function approximator on bounded intervals. Schuld, Sweke,\nand Meyer (PRA 2021) then proved the structural reason: a re-uploading\ncircuit's output function is a truncated Fourier series in the input x,\nwith the set of representable frequencies determined by the eigenvalue\nspectrum of the encoding generators and growing with L.\n\nThe practical question that follows is whether the theoretical\nexpressivity claim translates into empirical fit quality. As L grows we\nexpect (i) more representable frequencies (better fit on high-bandwidth\ntargets), and (ii) more parameters and more risk of overfitting on\nfinite-sample regression with discontinuous targets. Classical kernel\nregression (RBF) and polynomial regression provide the natural baselines\nbecause they too can be interpreted as choosing a basis of functions\n(Gaussian or polynomial) of fixed expressivity; comparing quantum\nre-uploading against these classical baselines is the cleanest way to\nask \"does the Fourier structure imposed by re-uploading give a useful\ninductive bias on these targets, or do classical models with comparable\ncapacity match it?\"\n\nImplementation guidance is provided by the quantum-qiskit skill, which\nis automatically injected at stage 10 and contains canonical code for\nbuilding re-uploading circuits using qiskit.circuit.ParameterVector and\nrotation gates. Training uses Adam through parameter-shift gradients\n(do NOT roll your own gradient finite-difference loop). The simulator\nis fixed to AerSimulator(method='statevector') for noiseless evaluation\nso the Fourier-spectrum probe is clean.\n\nExperimental protocol. Four quantum re-uploading depths (L=1, L=3, L=5,\nL=7) are compared against two classical baselines (RBF kernel ridge\nregression with bandwidth chosen by cross-validation; polynomial\nregression of degree 7). All six conditions are evaluated on two 1D\nregression targets: a sum-of-sinusoids signal with known frequency\ncontent, and a step function (tests handling of discontinuities).\nEach (condition, dataset) cell is averaged over 2 random seeds, giving\n6 conditions x 2 datasets x 2 seeds = 24 total cells.\n\nResearch question: *On 1D regression with known target bandwidth, does\nincreasing data re-uploading depth L produce monotonically lower test\nMSE on bandwidth-rich targets (sinusoid mixture), and does the\nrecovered Fourier spectrum overlap with the target spectrum scale\npredictably with L?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On the sinusoid-mixture target, the L=5 re-uploading model achieves test MSE at least 4 times lower than the L=1 re-uploading model (2-seed mean), demonstrating that increasing re-uploading depth improves regression accuracy on bandwidth-rich targets.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On the step-function target, the L=7 re-uploading model exhibits overfitting compared to L=5: L=7 test MSE is at least 15 percent higher than L=5 test MSE (2-seed mean), even though L=7 has more parameters and lower training MSE.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"At L=7 on the sinusoid-mixture target, the recovered Fourier spectrum (FFT of the trained circuit's output sampled on a uniform 256-point grid) has cosine similarity at least 0.7 with the target Fourier spectrum, restricted to the lowest 5 nonzero frequencies.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"On 1D regression with known target bandwidth, does increasing data re-uploading depth L produce monotonically lower test MSE on bandwidth-rich targets, and does the recovered Fourier spectrum overlap with the target spectrum scale predictably with L?\", \"conditions\": [{\"name\": \"reuploading_L1\", \"description\": \"Single-qubit re-uploading circuit with L=1 encoding block. Encoding: U(x) = RZ(x). Trainable block: W(theta) = RY(theta_0) RZ(theta_1). Output: . 2 trainable parameters total.\"}, {\"name\": \"reuploading_L3\", \"description\": \"Same single-qubit re-uploading template with L=3 encoding blocks. 6 trainable parameters.\"}, {\"name\": \"reuploading_L5\", \"description\": \"Same template with L=5 encoding blocks. 10 trainable parameters.\"}, {\"name\": \"reuploading_L7\", \"description\": \"Same template with L=7 encoding blocks. 14 trainable parameters.\"}], \"baselines\": [\"rbf_kernel_ridge: sklearn.kernel_ridge.KernelRidge(kernel='rbf', alpha=1e-3, gamma=tuned_via_grid_search). Strong nonparametric regression baseline.\", \"polynomial_regression: sklearn.pipeline.make_pipeline(PolynomialFeatures(degree=7), Ridge(alpha=1e-3)). Matched-capacity polynomial basis baseline.\"], \"metrics\": [{\"name\": \"test_mse\", \"direction\": \"minimize\", \"description\": \"Mean squared error on a 200-point held-out test set, averaged over 2 random seeds per (condition, dataset) cell. Primary metric.\"}, {\"name\": \"train_mse\", \"direction\": \"minimize\", \"description\": \"Mean squared error on the 200-point training set after training completes.\"}, {\"name\": \"fourier_spectrum_cosine\", \"direction\": \"maximize\", \"description\": \"Cosine similarity between the FFT of the trained model's output sampled on a uniform 256-point grid over [0, 1] and the FFT of the target function on the same grid, restricted to the lowest 5 nonzero frequency bins. Higher means the model has learned the target's Fourier content.\"}], \"datasets\": [{\"name\": \"sinusoid_mixture_1d\", \"source\": \"Synthetic generator\", \"description\": \"Target function f(x) = sum_k a_k * sin(2*pi*k*x) for k in {1, 2, 3, 5, 7} with random amplitudes a_k drawn from Uniform[0.5, 1.0] using a fixed dataset-generation seed (independent of per-cell training seeds). 200 training x-values + 200 test x-values uniformly sampled on [0, 1]. Targets are observed with Gaussian noise N(0, 0.05).\"}, {\"name\": \"step_function_1d\", \"source\": \"Synthetic generator\", \"description\": \"Target function f(x) = 1.0 if x > 0.5 else 0.0, plus Gaussian noise N(0, 0.05). Tests how the re-uploading model handles a discontinuity (its Fourier series will exhibit Gibbs ringing). 200 training + 200 test samples.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 900}}", "requirements": "", "rubric": "{\"id\": \"q04-root\", \"requirements\": \"A credible empirical study of data re-uploading depth vs Fourier expressivity in variational quantum regression. The agent must (a) implement 4 re-uploading circuits at L in {1, 3, 5, 7} using qiskit ParameterVector + RY/RZ rotation gates, (b) train each with parameter-shift Adam on 200 samples of two 1D regression targets (sinusoid mixture and step function), (c) compare against two classical baselines (RBF kernel ridge regression, polynomial regression degree 7) on the same data, (d) measure test MSE per cell and the recovered Fourier spectrum of the trained quantum model, and (e) score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"Re-uploading topics are scored on (i) whether the 4 quantum circuits actually differ by depth (sanity check: parameter counts must be 2/6/10/14), (ii) whether training was real and produced different outputs across L levels (3 quantum cells producing bit-identical MSE would indicate dispatch failure, same anti-pattern as Q01 ablation failure), (iii) whether classical baselines are computed on the exact same data as quantum cells, and (iv) whether H1/H2/H3 are supported by reported MSE and Fourier-spectrum numbers. Quantitative result leaves use a graded scale: 100% if hypothesis threshold cleanly met, 67% if trend in predicted direction without clearing threshold, 33% if ambiguous, 0% if contradicted or evidence missing.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q04-code\", \"requirements\": \"Code-development bucket: 4 re-uploading circuits + 2 classical baselines are implemented correctly with shared training pipeline and reproducible per-cell seed control.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q04-code-circuits\", \"requirements\": \"All four re-uploading circuits use qiskit.QuantumCircuit + qiskit.circuit.ParameterVector. The encoding gate is RZ(x) and the trainable block is RY(theta_2k) RZ(theta_2k+1). Output is the expectation on the single qubit (via qiskit.quantum_info.Statevector or the reference Estimator primitive). Parameter counts match: L=1 has 2 params, L=3 has 6, L=5 has 10, L=7 has 14. A sanity check asserts that the four circuits produce different output values for a fixed nonzero input x=0.3 with a fixed random parameter vector.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q04-code-training-pipeline\", \"requirements\": \"Training uses Adam with parameter-shift gradients computed via the qiskit parameter-shift rule (NOT scipy finite differences). Loss is mean squared error on the 200 training samples. Same optimizer hyperparameters across all four L levels (Adam lr=0.05, 300 steps). Both classical baselines use sklearn (KernelRidge with grid-searched gamma, and a Pipeline of PolynomialFeatures+Ridge).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"q04-code-metric-logging\", \"requirements\": \"Each per-(condition, dataset, seed) result is emitted to stdout as a single line starting with METRIC_RESULT followed by JSON containing condition, dataset, seed, test_mse, train_mse, and fourier_spectrum_cosine. The FFT is computed on the trained model's output sampled on a uniform 256-point grid over [0, 1].\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q04-exec\", \"requirements\": \"Execution-validity bucket: all 24 cells ran and produced numerically valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q04-exec-cells-ran\", \"requirements\": \"At least 22 cells out of 24 expected (6 conditions x 2 datasets x 2 seeds) completed without unhandled errors and produced a test_mse value. Missing more than 2 cells (10 percent) without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q04-exec-numerical\", \"requirements\": \"Numerical validity: no NaN or Inf in test_mse; train_mse <= test_mse * 1.5 across conditions (otherwise model is broken); the 4 quantum L levels produce different test MSEs (max - min across the 4 quantum conditions on the sinusoid-mixture dataset is at least 0.005). Fourier spectrum values are real, finite, and the cosine similarity is in [-1, 1].\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q04-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q04-result-h1-quant\", \"requirements\": \"Quantitative test of H1. On the sinusoid-mixture dataset, is test_mse(L=5) <= test_mse(L=1) / 4 (2-seed mean)? 100% if ratio >= 4, 67% if 2 <= ratio < 4, 33% if 1 < ratio < 2, 0% if L=5 is not better than L=1.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q04-result-h2-quant\", \"requirements\": \"Quantitative test of H2. On the step function dataset, is test_mse(L=7) >= test_mse(L=5) * 1.15 (2-seed mean)? 100% if overshoot is >= 15 percent, 67% if 5-15 percent, 33% if any small overshoot (0-5 percent), 0% if L=7 still has lower MSE.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q04-result-h3-quant\", \"requirements\": \"Quantitative test of H3. On the sinusoid-mixture dataset at L=7, does the recovered Fourier spectrum cosine similarity (restricted to lowest 5 nonzero frequencies) reach 0.7? 100% if >= 0.7, 67% if >= 0.5, 33% if >= 0.3, 0% otherwise.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"q04-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1, H2, H3 backed by specific test MSE values per (L, dataset) and Fourier spectrum cosine values. Discusses how the Fourier-series view (Schuld, Sweke, Meyer 2021) predicts the observed trend, and identifies a dominant systematic uncertainty (seed variance, optimizer convergence, parameter-shift gradient noise).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q04.yaml", "rubric_file": "tasks/quantum/rubrics/Q04.json"} +{"id": "Q05", "domain": "quantum", "title": "Barren plateau onset under cost-function locality", "topic": "Barren plateau onset under cost-function locality: empirically measuring gradient variance of hardware-efficient ansatze under global vs local cost functions across depth, testing the Cerezo 2021 prediction that local costs avoid exponential gradient suppression", "domains": ["quantum-machine-learning", "barren-plateaus", "trainability"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "log_gradient_variance", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 600, "synthesis": "A barren plateau is the phenomenon, first identified by McClean et al.\nin 2018, that the gradient of a variational quantum circuit's loss\nbecomes exponentially small in the number of qubits when the ansatz is\ndrawn from a sufficiently random circuit ensemble. Concretely, for a\nloss L(theta) = where the parameterized\nstate is generated by a random-ish unitary, the variance of the\ngradient dL/dtheta_k scales as O(1/2^n). For n = 20 qubits this\nvariance is below machine epsilon, and SGD or any gradient-based\noptimizer has effectively zero signal to follow. This is the central\ntrainability obstruction in variational quantum machine learning.\n\nCerezo et al. (\"Cost function dependent barren plateaus in shallow\nparametrized quantum neural networks\", Nature Communications 2021)\nshowed that the locality of the cost function matters. If the cost is\na sum of single-qubit Pauli expectations (a local cost), then the\ngradient variance can scale only polynomially in n even at moderate\nansatz depth. If the cost is a product or projection over all qubits\n(a global cost), the variance is exponentially suppressed at any\ndepth. The mechanism is that local-cost observables overlap with only\na constant-size subspace of the 2^n dimensional Hilbert space, while\nglobal-cost observables are spread thinly across the entire space and\nalmost always cancel.\n\nThe empirical question this topic addresses is whether the local-cost\nfree lunch holds at moderate depth on a CPU-feasible 6-qubit\nbenchmark. We fix n=6 qubits and the EfficientSU2 hardware-efficient\nansatz family (a standard choice in qiskit). We vary depth (number of\nansatz layers L) and cost-function locality, then directly probe the\ngradient distribution by sampling 100 random parameter vectors per\ncell, computing the parameter-shift gradient with respect to theta_0\nfor each, and reporting the empirical variance. A subsidiary check\ntrains each condition for 100 Adam iterations and reports the loss\nchange as a practical sanity probe of whether the gradient signal is\nlarge enough to drive optimization at all.\n\nImplementation guidance is provided by the quantum-qiskit skill,\nwhich is automatically injected at stage 10. Use qiskit.circuit.library\nEfficientSU2 verbatim; do NOT hand-write H/CNOT/RY gate stacks (a\nprior failed run produced an \"ablation failure\" where three nominally\ndifferent encodings collapsed to identical circuits because they\nwere hand-coded). Use qiskit_algorithms.utils.algorithm_globals\nrandom_seed for reproducibility. Gradients are computed via the\nparameter-shift rule on a statevector backend.\n\nExperimental protocol. Four conditions cover the cost x depth grid:\n(local_cost_L2) shallow + local, the trainable control; (local_cost_L10)\nmedium + local, the regime Cerezo's theorem predicts is still\ntrainable; (local_cost_L20) deep + local, stressing the prediction;\n(global_cost_L10) medium + global, the negative control where a barren\nplateau is expected. Each condition is run with 3 different random\nansatz-structure seeds (controlling which qubits are entangled in each\nEfficientSU2 layer) for variance estimation across architectures, and\n100 random parameter vectors per (condition, structure-seed) cell are\nused to estimate gradient variance. Total cells: 4 conditions x 3\nansatz seeds = 12 cells. No external dataset is required because this\nis a measurement experiment on the ansatz family itself.\n\nResearch question: *Does the local-cost trick avoid the barren plateau\non a 6-qubit EfficientSU2 ansatz at L=10 and L=20 ansatz depth, as\npredicted by Cerezo et al. 2021, when compared against the\nglobal-cost baseline at the same depth and against the shallow local-cost\ncontrol?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"At ansatz depth L=10, the empirical variance of the gradient under local cost is at least 20 times larger than under global cost (3-seed mean of variance per condition), confirming Cerezo et al. 2021's prediction that locality changes the barren-plateau scaling.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Under local cost, the gradient variance at L=20 is at least 1/100 of the gradient variance at L=2, i.e. the polynomial (sub-exponential) decay regime is maintained even at deep ansatz, with log10(var_L20) - log10(var_L2) > -2.0 (3-seed mean).\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"When trained with Adam for 100 gradient steps starting from random initialization, the global-cost-L10 condition produces a loss change of at most 1 percent of the initial loss (3-seed mean), demonstrating that the predicted barren plateau actively prevents practical training, while the local-cost-L10 condition produces a loss change of at least 10 percent.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does the local-cost trick avoid the barren plateau on a 6-qubit EfficientSU2 ansatz at L=10 and L=20, as predicted by Cerezo et al. 2021, when compared against the global-cost baseline and a shallow local-cost control?\", \"conditions\": [{\"name\": \"local_cost_L10\", \"description\": \"Proposed-method core. EfficientSU2(num_qubits=6, reps=10, entanglement='linear') ansatz. Cost function: (expectation of Pauli Z on qubit 0 only). 100 random parameter vectors theta drawn from Uniform[-pi, pi]^{n_params}. For each theta, compute parameter-shift gradient dL/dtheta_0 by evaluating L at theta + (pi/2) * e_0 and theta - (pi/2) * e_0. Report variance of these 100 gradient values.\"}, {\"name\": \"local_cost_L20\", \"description\": \"Proposed-method stress test. EfficientSU2 reps=20, same local cost . Tests whether the polynomial-decay prediction holds at deeper ansatz.\"}], \"baselines\": [\"local_cost_L2: trainable-control baseline. EfficientSU2 reps=2, local cost . Known to have non-vanishing gradient because the ansatz is too shallow to enter the barren-plateau regime.\", \"global_cost_L10: barren-plateau negative-control baseline. EfficientSU2 reps=10, global cost (product of Z on all 6 qubits). Known from Cerezo 2021 to exhibit exponentially small gradient variance even though the ansatz depth matches local_cost_L10.\"], \"metrics\": [{\"name\": \"log_gradient_variance\", \"direction\": \"maximize\", \"description\": \"log10 of the empirical variance of dL/dtheta_0 over 100 random parameter samples. Higher means more trainable gradient signal. Primary metric. Less negative numbers are better (closer to 0).\"}, {\"name\": \"mean_absolute_gradient\", \"direction\": \"maximize\", \"description\": \"Mean of |dL/dtheta_0| over the 100 random samples.\"}, {\"name\": \"training_loss_change_fraction\", \"direction\": \"maximize\", \"description\": \"Relative loss change after 100 Adam steps starting from a random initialization: (L_initial - L_after_100_steps) / |L_initial|. Practical training-feasibility probe. Larger positive means training actually moved the loss.\"}], \"datasets\": [{\"name\": \"random_parameter_samples\", \"source\": \"Synthetic, generated in-script via numpy.random.RandomState(ansatz_structure_seed).uniform(-pi, pi, size=(100, n_params))\", \"description\": \"Each cell samples 100 random parameter vectors in the parameter space of EfficientSU2 at the given (num_qubits, reps). These are the inputs over which gradient variance is estimated. No external data is involved; this is a measurement experiment on the ansatz family.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 600}}", "requirements": "", "rubric": "{\"id\": \"q05-root\", \"requirements\": \"An empirical study of barren plateau onset on a 6-qubit EfficientSU2 ansatz under local vs global cost functions and shallow vs deep ansatz. The agent must (a) build the EfficientSU2 ansatz with reps in {2, 10, 20} via qiskit.circuit.library, (b) for each (cost, depth) condition sample 100 random parameter vectors and compute parameter-shift gradient dL/dtheta_0, (c) report the empirical variance of those 100 gradient values per cell, (d) run a 100-step Adam training as a practical sanity probe of trainability, and (e) score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"Barren plateau studies are scored on (i) correctness of the EfficientSU2 / cost construction (using qiskit.circuit.library, not hand-rolled), (ii) whether the 100-sample gradient distribution actually captures the variance (cells reporting std=0 across 100 samples indicate a broken sampler), (iii) the local_cost_L10 vs global_cost_L10 contrast being clean (these have IDENTICAL ansatz, only cost differs, so any large variance gap directly tests Cerezo 2021), and (iv) the training-feasibility probe correlating with the gradient-variance prediction (BP cell should not train; trainable cell should train). Quantitative result leaves use a graded scale: 100% if cleanly met, 67% if in predicted direction without clearing threshold, 33% if ambiguous, 0% if contradicted.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q05-code\", \"requirements\": \"Code-development bucket: EfficientSU2 ansatz built correctly via qiskit.circuit.library, parameter-shift gradient implemented correctly, and the 100-sample gradient distribution actually populated per cell.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q05-code-ansatz-and-gradient\", \"requirements\": \"EfficientSU2(num_qubits=6, reps=reps_value, entanglement='linear') from qiskit.circuit.library used directly (NOT hand-coded H/CNOT/RY stacks). Parameter-shift gradient of (theta) with respect to theta_0 computed via the qiskit standard pattern: dL/dtheta_0 = (L(theta + (pi/2)*e_0) - L(theta - (pi/2)*e_0)) / 2. Cost observable for local_cost is Pauli Z on qubit 0; for global_cost is the product Z_0 Z_1 Z_2 Z_3 Z_4 Z_5 (a SparsePauliOp). A sanity check confirms that the gradient is nonzero for at least one random parameter sample at L=2 local cost.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q05-code-sampling\", \"requirements\": \"For each (condition, ansatz-structure seed) cell, 100 independent random parameter vectors are drawn from Uniform[-pi, pi]^{n_params} using a per-cell seeded RNG. For each parameter vector, the gradient dL/dtheta_0 is computed. The 100 gradient values are stored and the empirical variance is reported. Reusing the same parameter vector across conditions in the same cell is allowed (paired comparison) but NOT required.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"q05-code-training-probe\", \"requirements\": \"After the gradient-variance measurement, each cell runs a 100-step Adam optimization (qiskit_algorithms.optimizers.ADAM or pytorch Adam wrapping a parameter-shift gradient closure) starting from a random initialization. The initial loss and the loss after step 100 are recorded; the training_loss_change_fraction = (L_initial - L_final) / |L_initial| is computed per cell.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q05-exec\", \"requirements\": \"Execution-validity bucket: all 12 cells ran and produced numerically valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q05-exec-cells-ran\", \"requirements\": \"At least 11 cells out of 12 expected (4 conditions x 3 ansatz-structure seeds) completed and produced a non-null log_gradient_variance value. Missing more than 1 cell without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q05-exec-numerical\", \"requirements\": \"Numerical validity: no NaN or Inf in gradient values; empirical std of the 100 gradients is strictly > 0 in every cell (cells reporting std=0 indicate a broken sampler and fail this leaf); log10(variance) values are finite. The training_loss_change_fraction is in the range [-1.0, 1.0] (otherwise loss diverged or wasn't properly normalized).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q05-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q05-result-h1-quant\", \"requirements\": \"Quantitative test of H1. Is var(local_cost_L10) / var(global_cost_L10) >= 20 (3-seed mean of variances)? 100% if ratio >= 20, 67% if 5 <= ratio < 20, 33% if 1 < ratio < 5, 0% if global cost has equal or larger variance.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q05-result-h2-quant\", \"requirements\": \"Quantitative test of H2. Is log10(var_local_L20) - log10(var_local_L2) > -2.0 (3-seed mean)? 100% if difference > -2.0 (polynomial decay), 67% if difference > -3.0, 33% if difference > -4.0, 0% otherwise (exponential decay observed).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q05-result-h3-quant\", \"requirements\": \"Quantitative test of H3. Is global_cost_L10 training_loss_change_fraction <= 0.01 AND local_cost_L10 training_loss_change_fraction >= 0.10 (3-seed mean)? 100% if both conditions hold cleanly, 67% if local trains but global change is 1-5 percent, 33% if local trains but global has any change, 0% if global cost also trains (would contradict the BP prediction).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q05-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by specific variance values per (condition, seed) and training_loss_change_fraction values. Explicitly references Cerezo et al. 2021 'Cost function dependent barren plateaus' and discusses whether the observed variance ratio matches their theoretical prediction. Identifies dominant uncertainty (ansatz-structure seed variance, finite-sample variance estimate from 100 gradient samples).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q05.yaml", "rubric_file": "tasks/quantum/rubrics/Q05.json"} +{"id": "Q06", "domain": "quantum", "title": "Neural-network warm-start for QAOA-MaxCut initialization", "topic": "Neural-network warm-start for QAOA-MaxCut: comparing MLP-predicted initial parameters against random and fixed initialization across in-distribution and out-of-distribution graph families", "domains": ["ai-for-quantum", "qaoa", "meta-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "iterations_to_target_ratio", "metric_direction": "minimize", "gpu_required": false, "est_wall_clock_sec": 1200, "synthesis": "The Quantum Approximate Optimization Algorithm (QAOA) prepares a\nparameterized quantum state to approximate the maximum cut of a graph.\nAt depth p=1 the state depends on two real parameters (beta, gamma),\nand the algorithm proceeds by classical optimization of those two\nparameters against the average cut size sampled from measurements of\nthe prepared state. The bottleneck for practical QAOA is the initial\nparameter guess. Random initialization frequently falls into the\nbarren-plateau region or onto sub-optimal local minima, requiring many\nclassical iterations to escape. Brandao et al. 2018 showed\ntheoretically that the optimal (beta, gamma) values concentrate\naround a problem-independent point on certain graph ensembles, so a\nfixed initialization like (pi/4, pi/4) is a strong default.\n\nBeyond concentration, a richer strategy is to train a classical\nneural network offline to predict the optimal (beta, gamma) given\nfeatures of the input graph. Verdon et al. 2019 introduced the idea\nas quantum-aware meta-learning. Jain et al. (Quantum 2022, \"Graph\nneural network initialisation of QAOA\") showed that a graph neural\nnetwork trained on a few thousand small graphs can predict initial\nparameters that reduce the number of subsequent classical\noptimization iterations by 5-10x compared to random initialization,\nwith measurable transfer to out-of-distribution graph families.\n\nThe empirical question this topic addresses is whether the MLP-warm-start\nstrategy actually generalizes off-distribution: a model trained on\nErdős-Rényi graphs of one density should ideally retain its advantage\non regular graphs of a different size. We also want to verify whether\nthe simpler \"fixed init at (pi/4, pi/4)\" baseline (from Brandao 2018)\nis competitive with the MLP-predicted init at p=1, since the\nconcentration result suggests that simple defaults may be hard to\nbeat in the low-depth regime.\n\nImplementation guidance is provided by the quantum-qiskit skill at\nstage 10. Implement QAOA on the qiskit AerSimulator statevector\nbackend; the small graph sizes (n=6 to n=8) make exact statevector\nsimulation cheap. The MLP is sklearn.neural_network.MLPRegressor\ntrained offline on a precomputed set of (graph_features, optimal_beta,\noptimal_gamma) tuples obtained by grid search on 200 training\ngraphs. Optimization at test time uses COBYLA with a hard cap of 20\niterations.\n\nExperimental protocol. Two graph families serve as the test bed.\nIn-distribution: Erdős-Rényi G(n=6, p=0.5) graphs (matched to the MLP\ntraining distribution). Out-of-distribution: 3-regular graphs at n=8\n(different size and edge density). Four conditions cover the\ninitialization strategies: random init, fixed init (beta=gamma=pi/4),\nMLP-init evaluated in-distribution, and MLP-init evaluated\nout-of-distribution. (MLP-init has 2 conditions because the model\ntrained on Erdős-Rényi G(n=6, p=0.5) is evaluated on both graph\nfamilies to test transfer.) Each (condition, graph family) cell is\naveraged over 3 different random graph instances drawn within that\nfamily. Total cells: 4 conditions x 2 graph families x 3 seeds = 24\ncells.\n\nResearch question: *Does a small MLP trained to predict QAOA-MaxCut\ninitial parameters from graph features (degree distribution, edge\ndensity, spectral gap) provide a faster warm-start than random\ninitialization or the Brandao 2018 fixed (pi/4, pi/4) initialization,\nand does the advantage transfer to graphs from a different\ndistribution than the MLP was trained on?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On in-distribution Erdős-Rényi graphs at n=6, the MLP-predicted initialization reaches approximation ratio 0.9 in at most 10 COBYLA iterations on average (3-graph mean), while random initialization requires at least 18 iterations to reach the same target.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On out-of-distribution 3-regular graphs at n=8, MLP-predicted initialization still beats random initialization by at least 20 percent in iteration count (3-graph median), demonstrating partial transfer of the learned initialization heuristic.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"At p=1 the fixed initialization (beta=gamma=pi/4) is competitive with MLP-predicted initialization: the gap in 3-graph-median iteration count between fixed init and MLP-init is at most 30 percent on in-distribution graphs, consistent with the Brandao 2018 concentration prediction that QAOA-1 parameters concentrate around a problem-independent point.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does an MLP trained on small Erdős-Rényi graph features predict QAOA-MaxCut initial parameters that reduce COBYLA iteration count compared to random and fixed init, and does the advantage transfer to graphs of a different distribution than the MLP was trained on?\", \"conditions\": [{\"name\": \"mlp_init_in_distribution\", \"description\": \"Proposed method evaluated in-distribution. sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(32, 16), activation='relu', max_iter=2000, random_state=seed) trained offline on 200 Erdős-Rényi G(n=6, p=0.5) graphs with (degree_mean, degree_var, edge_density, n_edges) as features and grid-searched (beta_optimal, gamma_optimal) as targets. At test time, predict initial (beta, gamma) for a fresh in-distribution graph, then run COBYLA QAOA optimization starting from that initialization.\"}, {\"name\": \"mlp_init_out_of_distribution\", \"description\": \"Proposed method evaluated out-of-distribution. Same MLP as mlp_init_in_distribution (trained on Erdős-Rényi G(n=6, p=0.5)). At test time, evaluated on 3-regular graphs at n=8: predict initial (beta, gamma) using the same feature extractor, then run COBYLA QAOA optimization.\"}], \"baselines\": [\"random_init: classical baseline. Initial (beta, gamma) drawn from Uniform[0, pi/2] x Uniform[0, pi/2] using a per-graph seed independent of optimization seed. Then COBYLA QAOA from that point.\", \"fixed_init_pi_over_4: classical baseline from Brandao 2018. Initial (beta, gamma) = (pi/4, pi/4). Then COBYLA QAOA. Tests whether the concentration prediction holds in practice at p=1.\"], \"metrics\": [{\"name\": \"iterations_to_target_ratio\", \"direction\": \"minimize\", \"description\": \"Number of COBYLA iterations required to reach approximation ratio (cut_size / optimal_cut_size) >= 0.9 on each graph, with a hard cap at 20 iterations (cells that don't reach 0.9 within 20 iterations report 20 and success=False). Primary metric. 3-graph median per condition x graph_family cell.\"}, {\"name\": \"final_approximation_ratio\", \"direction\": \"maximize\", \"description\": \"approximation ratio (cut_size / optimal_cut_size) achieved after exactly 20 COBYLA iterations, regardless of whether the target threshold was met. 3-graph mean.\"}, {\"name\": \"success_rate\", \"direction\": \"maximize\", \"description\": \"Fraction of the 3 random graph instances in a cell that reached approximation ratio 0.9 within the 20-iteration budget.\"}], \"datasets\": [{\"name\": \"erdos_renyi_n6_p05\", \"source\": \"networkx.erdos_renyi_graph(n=6, p=0.5, seed=graph_instance_seed)\", \"description\": \"In-distribution test set. 3 fresh random Erdős-Rényi graphs G(n=6, p=0.5), distinct from the 200 graphs used for MLP training. Optimal MaxCut value computed by brute-force enumeration over the 2^6 cuts. QAOA p=1 on AerSimulator statevector backend.\"}, {\"name\": \"regular_3_n8\", \"source\": \"networkx.random_regular_graph(d=3, n=8, seed=graph_instance_seed)\", \"description\": \"Out-of-distribution test set. 3 random 3-regular graphs at n=8 (different size and edge density than the n=6 ER training distribution). Optimal MaxCut value by brute-force enumeration over 2^8 cuts. QAOA p=1 on AerSimulator statevector backend.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1200}}", "requirements": "", "rubric": "{\"id\": \"q06-root\", \"requirements\": \"An empirical study of neural-network warm-start for QAOA-MaxCut at p=1. The agent must (a) precompute 200 Erdős-Rényi G(n=6, p=0.5) training graphs with their optimal (beta, gamma) via grid search to label the MLP training set, (b) train sklearn.MLPRegressor to predict (beta, gamma) from graph features, (c) at test time, predict (beta, gamma) and run COBYLA QAOA optimization starting from that init for up to 20 iterations, (d) compare against random and fixed (pi/4, pi/4) initialization baselines on both in-distribution (ER G(n=6, p=0.5)) and out-of-distribution (3-regular n=8) test graphs, and (e) score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"QAOA warm-start studies are scored on (i) whether the MLP is actually trained offline (cells reporting identical MLP predictions across different graph instances indicate the model is broken or untrained), (ii) whether the 4 initialization strategies actually produce different starting (beta, gamma) points (must be verified by sanity check), (iii) whether COBYLA is run from each init for exactly 20 iterations and the approximation_ratio trajectory is recorded, and (iv) whether H1/H2/H3 are supported by 3-graph-median numerical evidence keyed by (condition, graph_family). Quantitative result leaves use a graded scale.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q06-code\", \"requirements\": \"Code-development bucket: QAOA p=1 circuit built via qiskit.circuit.library or hand-coded RX/RZ + CNOT correctly, MLP trained offline on graph features, and 4 initialization conditions actually produce different starting points.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q06-code-qaoa-circuit\", \"requirements\": \"QAOA p=1 circuit for MaxCut implemented correctly: H^{otimes n} initial state, then U_C(gamma) = product over edges of exp(-i * gamma * Z_i Z_j), then U_B(beta) = product over qubits of exp(-i * beta * X_i). Run on qiskit AerSimulator statevector backend; observable is the cut Hamiltonian sum_{(i,j) in E} (1 - Z_i Z_j) / 2. Optimization via qiskit_algorithms.optimizers.COBYLA(maxiter=20).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q06-code-mlp-training\", \"requirements\": \"Offline phase: generate 200 random ER G(n=6, p=0.5) graphs with a different RNG than the test set. For each, compute the optimal (beta_optimal, gamma_optimal) via a 20x20 grid search on (beta, gamma) in [0, pi/2]^2 maximizing the QAOA p=1 expected cut size. Extract 4 graph features (degree_mean, degree_var, edge_density, n_edges) and fit sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(32, 16), max_iter=2000) to map features -> (beta_optimal, gamma_optimal). The trained MLP is reused for both in-distribution and out-of-distribution test conditions.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q06-code-init-conditions\", \"requirements\": \"The 4 initialization strategies produce different starting (beta, gamma) per test graph. Sanity check: for 3 test graphs, log the initial (beta, gamma) for each of the 4 conditions and verify they are not all equal. Each condition's COBYLA optimization is run for exactly 20 iterations, and the cut size at each iteration is recorded so the iterations_to_target_ratio can be computed by replaying the trajectory.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q06-exec\", \"requirements\": \"Execution-validity bucket: all cells ran and produced numerically valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q06-exec-cells-ran\", \"requirements\": \"At least 22 cells out of 24 expected (4 conditions x 2 graph families x 3 seeds) completed and produced an iterations_to_target_ratio value (clamped to 20 if not reached). Missing more than 2 cells (10 percent) without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q06-exec-numerical\", \"requirements\": \"Numerical validity: iterations_to_target_ratio is in [1, 20] (integer-valued, since COBYLA budget is 20); final_approximation_ratio is in [0, 1]; success_rate is in [0, 1]. At least one cell across the entire 24-cell sweep reaches AR >= 0.9 within 20 iterations (otherwise threshold is uncalibrated).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q06-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q06-result-h1-quant\", \"requirements\": \"Quantitative test of H1. On in-distribution ER G(n=6, p=0.5) graphs, does mlp_init_in_distribution reach AR>=0.9 in <= 10 COBYLA iterations on 3-graph mean, while random_init requires >= 18? 100% if MLP <= 10 AND random >= 18, 67% if MLP at least 30 percent better than random, 33% if MLP marginally better than random, 0% if random equals or beats MLP.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q06-result-h2-quant\", \"requirements\": \"Quantitative test of H2. On out-of-distribution 3-regular n=8 graphs, does mlp_init_out_of_distribution beat random_init by at least 20 percent in 3-graph-median iterations_to_target_ratio? 100% if mlp gain >= 20 percent, 67% if mlp gain 10-20 percent, 33% if mlp marginally faster, 0% if no transfer (random matches or beats MLP).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q06-result-h3-quant\", \"requirements\": \"Quantitative test of H3. On in-distribution graphs, is the 3-graph-median iteration-count gap between fixed_init_pi_over_4 and mlp_init_in_distribution <= 30 percent (i.e. fixed init is competitive with MLP at p=1, consistent with Brandao 2018 concentration)? 100% if gap <= 30 percent, 67% if 30-60 percent, 33% if 60-100 percent, 0% if MLP is more than 2x better than fixed init (would refute the concentration interpretation).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q06-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by per-(condition, graph_family) median iteration counts and approximation ratios. References Brandao et al. 2018 concentration result and Jain et al. Quantum 2022 GNN-init paper. Discusses whether the observed out-of-distribution transfer rate is high enough that MLP-init is worth the offline training cost compared to fixed init.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q06.yaml", "rubric_file": "tasks/quantum/rubrics/Q06.json"} +{"id": "Q07", "domain": "quantum", "title": "Matrix Product State classifier vs neural network at matched parameter count", "topic": "Matrix Product State classifier vs neural network at matched parameter count on downsampled image classification, characterizing the bond-dimension scaling of tensor-network classifiers as a quantum-inspired classical baseline for QML", "domains": ["quantum-inspired-classical", "tensor-networks", "supervised-learning"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1500, "synthesis": "A Matrix Product State (MPS) is a tensor network in which a target\ntensor of N indices is decomposed into a 1D chain of N small tensors,\neach connected to its neighbors by \"bond\" indices of dimension chi.\nMPS originated in condensed-matter physics as the representation\nunderlying the Density Matrix Renormalization Group (DMRG) for 1D\nground-state computation, and Schollwock's 2011 review remains the\ncanonical reference. Stoudenmire and Schwab (NeurIPS 2016, \"Supervised\nLearning with Tensor Networks\") repurposed the MPS as a classical\nsupervised classifier. The idea is simple: each input feature\n(pixel) is mapped to a small feature vector (e.g. cos(pi*x/2),\nsin(pi*x/2) for grayscale x in [0,1]), and the per-pixel feature\nvectors are contracted with a chain of MPS tensors to produce a\nclass-score vector. The MPS bond dimension chi controls how much\ncorrelation between distant pixels the classifier can capture,\ngiving an interpretable expressivity knob.\n\nCrucially, the MPS classifier runs entirely on classical hardware.\nThis makes it a \"quantum-inspired classical\" baseline: the same\nmathematical machinery as a low-entanglement quantum state, but\nimplemented as a NumPy tensor contraction. Glasser et al. (PRX 2018)\nextended the framework to richer tensor networks. More recent\nbenchmarks (Liu et al. 2022 Nature Communications Physics) have asked\nwhere the bond-dimension sweet spot is for image classification and\nwhether MPS classifiers can be competitive with CNNs at small\nparameter budgets.\n\nThis topic asks whether an MPS classifier matches a parameter-matched\nCNN baseline on a CPU-feasible image classification task. The\ncomparison is not \"does MPS beat CNN at large scale\" (it doesn't, on\nhigh-resolution images) but rather \"at matched small parameter\ncounts, is the MPS inductive bias competitive with a small CNN's\nspatial bias on 8x8 downsampled MNIST and Fashion-MNIST?\" The\nquestion is timely because tensor-network ML is increasingly cited\nas the honest classical baseline against which to measure quantum\nmachine learning claims of advantage.\n\nImplementation guidance. Use pure NumPy or PyTorch for the MPS\ncontraction (no qiskit required for this topic; it is quantum-inspired\nclassical computation). The standard MPS classifier from Stoudenmire\n& Schwab 2016 stores N+1 tensors of shape (chi, d, chi) where N is\nthe number of pixels (64 for an 8x8 image), d=2 is the per-pixel\nfeature dimension (we use the cos/sin embedding), and chi is the\nbond dimension. The CNN baseline is a single 3x3 convolution layer\nwith K filters followed by a fully connected output layer; K is\nchosen so the total parameter count is within 10 percent of the MPS\nparameter count at each bond dimension.\n\nExperimental protocol. Three MPS bond dimensions (chi=4, chi=8,\nchi=16) are compared against two classical baselines (logistic\nregression on flattened 8x8 pixels; small CNN with parameter count\nmatched to the chi=8 MPS). All five conditions are evaluated on two\ndatasets: 8x8 downsampled MNIST (10 classes, 5000 train / 1000 test\nimages) and 8x8 downsampled Fashion-MNIST (10 classes, same\nsize). Each (condition, dataset) cell is averaged over 3 random\nseeds (controlling MPS / CNN initialization and the train / test\nsplit). Total cells: 5 conditions x 2 datasets x 3 seeds = 30 cells.\n\nResearch question: *On 8x8 downsampled MNIST and Fashion-MNIST, do\nMPS classifiers at moderate bond dimension chi reach the test\naccuracy of a parameter-matched CNN, and how does the accuracy\nscale with chi?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On 8x8 downsampled MNIST, the MPS classifier at bond dimension chi=16 reaches at least 92 percent test accuracy (3-seed mean), matching the Stoudenmire & Schwab 2016 result scaled to this image size.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On 8x8 downsampled Fashion-MNIST, the parameter-matched CNN beats the chi=16 MPS classifier by at least 5 absolute percentage points (3-seed mean), reflecting the CNN's stronger inductive bias for spatial texture features that are more important on Fashion-MNIST than on plain digits.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Test accuracy of the MPS classifier scales log-linearly with bond dimension: on 8x8 MNIST the chi=4 to chi=8 gain in 3-seed mean accuracy is greater than the chi=8 to chi=16 gain (consistent with saturating expressivity at moderate chi).\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"On 8x8 downsampled MNIST and Fashion-MNIST, do MPS classifiers at moderate bond dimension chi reach the test accuracy of a parameter-matched CNN, and how does accuracy scale with chi?\", \"conditions\": [{\"name\": \"mps_chi4\", \"description\": \"Matrix Product State classifier with bond dimension chi=4. 64 pixels x 2-dim per-pixel feature embedding x chi=4 bonds, with a per-class output index. Approximate parameter count: 64 * 2 * 4 * 4 = 2048 parameters. Trained with Adam on cross-entropy for 50 epochs.\"}, {\"name\": \"mps_chi8\", \"description\": \"MPS classifier with chi=8. Approximate parameter count: 64 * 2 * 8 * 8 = 8192. Trained with Adam on cross-entropy for 50 epochs.\"}, {\"name\": \"mps_chi16\", \"description\": \"MPS classifier with chi=16. Approximate parameter count: 64 * 2 * 16 * 16 = 32768. Trained with Adam on cross-entropy for 50 epochs.\"}], \"baselines\": [\"logistic_regression: sklearn.linear_model.LogisticRegression(C=1.0, max_iter=1000, multi_class='multinomial') on flattened 8x8 pixels. Classical linear baseline, ~640 parameters.\", \"small_cnn_matched: PyTorch CNN with a single 3x3 conv layer of K=12 filters followed by an FC(K*6*6, 10) output, chosen so total parameter count is within 10 percent of the chi=8 MPS (target ~8200 parameters). Trained with Adam for 50 epochs.\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Top-1 classification accuracy on the 1000-image held-out test set, 3-seed mean. Primary metric.\"}, {\"name\": \"trainable_parameter_count\", \"direction\": \"minimize\", \"description\": \"Total number of trainable parameters in the model. Reported for parity validation across conditions: matched-parameter comparisons require this to be within 10 percent across the relevant condition pair.\"}, {\"name\": \"training_time_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock seconds for 50 epochs of training, measured per cell. Tests the practical compute cost of each method at matched accuracy.\"}], \"datasets\": [{\"name\": \"mnist_8x8_downsampled\", \"source\": \"sklearn.datasets.fetch_openml('mnist_784') downsampled to 8x8 via skimage.transform.resize\", \"description\": \"MNIST handwritten digits 0-9, downsampled from 28x28 to 8x8 grayscale, normalized to [0, 1]. 5000 training images, 1000 test images (stratified split, fixed random_state=seed). The 8x8 resolution is small enough for CPU MPS contraction but large enough to retain class structure (LogReg should reach ~85 percent here).\"}, {\"name\": \"fashion_mnist_8x8_downsampled\", \"source\": \"sklearn.datasets.fetch_openml('Fashion-MNIST') downsampled to 8x8\", \"description\": \"Fashion-MNIST clothing items (T-shirt, trouser, pullover, etc.) downsampled from 28x28 to 8x8. Tests model performance on texture-rich images where CNN inductive bias should be more important than for handwritten digits.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1500}}", "requirements": "", "rubric": "{\"id\": \"q07-root\", \"requirements\": \"An empirical study of Matrix Product State (MPS) classifiers vs neural network baselines at matched parameter count on 8x8 downsampled image classification. The agent must (a) build an MPS classifier following Stoudenmire and Schwab 2016 with cos/sin per-pixel feature embedding and bond dimensions chi in {4, 8, 16}, (b) train each MPS via Adam for 50 epochs on 5000 training images, (c) compare against sklearn LogisticRegression and a small PyTorch CNN whose parameter count is within 10 percent of the chi=8 MPS, (d) evaluate test accuracy on a 1000-image held-out set, and (e) score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"MPS classifier studies are scored on (i) correctness of the MPS contraction (the per-pixel features cos(pi*x/2), sin(pi*x/2) must enter as a rank-2 tensor at each site, NOT collapsed to a single scalar), (ii) the parameter-count parity between chi=8 MPS and small_cnn_matched within 10 percent (deviation > 10 percent fails q11-code-cnn-parity), (iii) the same train/test split being used across all conditions for a given seed, and (iv) accuracy values being in [0, 1] with non-degenerate predictions. Quantitative result leaves use a graded scale.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q07-code\", \"requirements\": \"Code-development bucket: MPS classifier implemented correctly with proper per-pixel feature embedding, CNN baseline parameter-matched, and training pipeline shared across conditions.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q07-code-mps\", \"requirements\": \"MPS classifier uses pure NumPy or PyTorch tensor contractions. Per-pixel feature embedding is phi(x) = [cos(pi*x/2), sin(pi*x/2)] for grayscale x in [0,1] (Stoudenmire and Schwab 2016 standard). The MPS is a chain of 64 tensors of shape (chi, 2, chi) (interior sites) plus 2 boundary tensors with one chi leg removed, with one site carrying an additional class index of size 10. Trained with Adam (lr=0.01, 50 epochs, batch_size=64). A sanity check confirms that MPS at chi=4 has approximately 64 * 2 * 16 = 2048 trainable parameters within 20 percent.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q07-code-cnn-parity\", \"requirements\": \"small_cnn_matched is implemented in PyTorch as Conv2d(1, K, kernel_size=3, padding=1) + ReLU + Flatten + Linear(K*8*8, 10) with K chosen so total parameter count is within 10 percent of the chi=8 MPS parameter count. K is calculated explicitly and the parameter counts are logged for both. Trained with Adam (same hyperparams as MPS) for 50 epochs.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"q07-code-pipeline\", \"requirements\": \"Nested loop over 5 conditions x 2 datasets x 3 seeds = 30 cells. Each cell uses the same train/test split given a fixed seed (i.e. the per-seed split is computed once and shared across the 5 conditions). MNIST is downsampled to 8x8 via skimage.transform.resize. Per-cell results are logged to stdout as METRIC_RESULT JSON lines containing condition, dataset, seed, test_accuracy, trainable_parameter_count, training_time_sec.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q07-exec\", \"requirements\": \"Execution-validity bucket: all 30 cells ran with valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q07-exec-cells-ran\", \"requirements\": \"At least 27 cells out of 30 expected (5 conditions x 2 datasets x 3 seeds) completed without unhandled errors and produced a test_accuracy value. Missing more than 3 cells (10 percent) without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q07-exec-numerical\", \"requirements\": \"Numerical validity: test_accuracy values are in [0, 1], not equal to 1/n_classes (would mean random predictions, model didn't train), and the 3 MPS chi levels produce different mean test_accuracy on at least one dataset (max - min across chi=4/8/16 mean accuracy on MNIST is at least 0.01). trainable_parameter_count is reported per cell and matches the expected formula within 1 percent.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q07-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q07-result-h1-quant\", \"requirements\": \"Quantitative test of H1. Does the MPS at chi=16 reach test_accuracy >= 92 percent on 8x8 downsampled MNIST (3-seed mean)? 100% if >= 92 percent, 67% if >= 85 percent, 33% if >= 75 percent, 0% otherwise.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q07-result-h2-quant\", \"requirements\": \"Quantitative test of H2. On 8x8 Fashion-MNIST, does the parameter-matched CNN beat chi=16 MPS by at least 5 absolute percentage points (3-seed mean)? 100% if CNN gap >= 5pp, 67% if CNN gap 2-5pp, 33% if CNN gap 0-2pp, 0% if MPS matches or beats CNN.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q07-result-h3-quant\", \"requirements\": \"Quantitative test of H3. On 8x8 MNIST, is the chi=4 to chi=8 accuracy gain greater than the chi=8 to chi=16 accuracy gain (3-seed mean)? Specifically: (acc_chi8 - acc_chi4) > (acc_chi16 - acc_chi8). 100% if cleanly satisfied with difference of differences >= 1pp, 67% if log-linear trend holds with smaller gap, 33% if marginally so, 0% if linear or super-linear scaling observed.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q07-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by per-(condition, dataset) mean test accuracies and the trainable_parameter_count parity check between chi=8 MPS and small_cnn_matched. References Stoudenmire and Schwab NeurIPS 2016 and discusses whether the observed MNIST vs Fashion-MNIST gap is consistent with the CNN's stronger spatial-bias inductive prior.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q07.yaml", "rubric_file": "tasks/quantum/rubrics/Q07.json"} +{"id": "Q08", "domain": "quantum", "title": "Layerwise learning vs end-to-end training for variational quantum classifiers", "topic": "Layerwise learning vs end-to-end training for variational quantum classifiers: testing the Skolik 2021 claim that incremental ansatz growth avoids barren plateaus on small UCI binary classification tasks", "domains": ["quantum-machine-learning", "training-strategies", "barren-plateaus"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1500, "synthesis": "Variational quantum classifiers (VQCs) at moderate depth (reps >= 5)\nsuffer from barren plateaus: the gradient of the loss with respect to\nany single parameter is exponentially suppressed when the ansatz is\ninitialized randomly across all layers. Skolik et al. (Quantum 2021,\n\"Layerwise learning for quantum neural networks\") proposed a training\nschedule that avoids this trap by adding ansatz layers incrementally.\nConcretely, the agent trains a reps=1 ansatz to convergence, freezes\nits parameters, prepends or appends a fresh reps=1 block initialized\nto identity, trains only the new block to convergence, and so on\nuntil the target depth is reached. Each layer of training sees a much\nsmaller parameter space, so the gradient signal stays well above the\nbarren-plateau floor at every step.\n\nThe empirical question this topic addresses is whether the layerwise\nschedule actually delivers higher final test accuracy AND faster\npractical convergence than naive end-to-end training of the full\nreps=5 ansatz on small UCI binary classification tasks. A\nparameter-matched classical baseline (sklearn MLPClassifier) and a\nlogistic regression baseline serve as the cross-paradigm controls\nrequired by the bench's PROCEED gate; they also calibrate the\ndifficulty of each dataset (logistic regression should fit linear\nseparable cases cleanly).\n\nImplementation guidance is provided by the quantum-qiskit skill,\nwhich is automatically injected at stage 10. Use\nqiskit.circuit.library.EfficientSU2(num_qubits=4, reps=5,\nentanglement='linear') as the underlying ansatz family, and use\nqiskit_machine_learning.algorithms.VQC for training (it works in\nqiskit 2.x; do NOT use qiskit_algorithms.VQE which is broken). For\nthe layerwise condition, the agent must implement the layer-freezing\nschedule manually: construct an ansatz with only the first k blocks\ntrainable and previous blocks bound to their converged parameters.\nThe classical baselines train on the same 80/20 train/test split\nusing the same per-seed random_state.\n\nExperimental protocol. Three quantum conditions compare training\nschedules at the same final depth (reps=5): layerwise growth from\nreps=1 to reps=5, end-to-end training of reps=5 from random\ninitialization, and a random-init control (no training, evaluate\nthe randomly initialized reps=5 ansatz). Two classical baselines\n(logistic regression, MLPClassifier with parameter count matched to\nthe VQC's 40-parameter count) provide the cross-paradigm reference.\nEach (condition, dataset) cell is averaged over 3 seeds. Total:\n5 conditions x 2 datasets x 3 seeds = 30 cells.\n\nResearch question: *Does layerwise incremental training of a VQC\nreach higher test accuracy or faster convergence than end-to-end\ntraining of the same final-depth ansatz, and does the layerwise\ngradient norm at each newly added layer remain above the\nbarren-plateau threshold?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Layerwise training reaches higher test accuracy than end-to-end training on at least 1 of 2 datasets, with a 3-seed-mean gap of at least 3 absolute percentage points.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"The mean gradient norm during the first 10 optimization steps of the layerwise schedule (averaged across all newly added blocks) is at least 5 times larger than the mean gradient norm during the first 10 steps of end-to-end training (3-seed mean), confirming the Skolik 2021 mechanism.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Both layerwise and end-to-end VQC outperform the random-init control (no training) by at least 5 absolute percentage points on both datasets (3-seed mean), confirming that training actually contributes — the ansatz itself is not memorizing class structure at initialization.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does layerwise incremental training of a 4-qubit reps=5 VQC reach higher test accuracy or faster convergence than end-to-end training of the same ansatz on small UCI binary classification?\", \"conditions\": [{\"name\": \"layerwise_growth\", \"description\": \"EfficientSU2(num_qubits=4, reps=5, entanglement='linear'). Training schedule: (1) train only the first reps=1 block via VQC.fit() for 80 COBYLA iterations; (2) freeze those parameters, add a fresh reps=1 block initialized to small N(0, 0.01) noise, train only the new block for 80 iterations; repeat until all 5 blocks are trained. Record the gradient norm at the start of each new layer's training.\"}, {\"name\": \"end_to_end\", \"description\": \"EfficientSU2(num_qubits=4, reps=5, entanglement='linear'). Single VQC.fit() call training all 40 parameters simultaneously from random N(0, 0.1) initialization for 400 COBYLA iterations (5 x 80, matching the total iteration budget of the layerwise schedule).\"}, {\"name\": \"random_init_control\", \"description\": \"Same EfficientSU2 ansatz, parameters drawn from Uniform[-pi, pi] and NEVER trained. Evaluate predict() directly on the test set. Floor for trainability — any trained model should beat this.\"}], \"baselines\": [\"logistic_regression: sklearn.linear_model.LogisticRegression(max_iter=1000, random_state=seed). Linear classical baseline.\", \"mlp_matched: sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(8,), activation='relu', max_iter=500, random_state=seed). Parameter count: 4*8 + 8 + 8*1 + 1 = 49 parameters, within 20 percent of the VQC's 40 parameters.\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Held-out 20 percent test split accuracy, 3-seed mean. Primary metric.\"}, {\"name\": \"mean_gradient_norm_first10\", \"direction\": \"maximize\", \"description\": \"L2 norm of the gradient vector averaged across the first 10 optimization steps. For layerwise, averaged across the first 10 steps of each newly added block. For end-to-end, the first 10 steps of the single training run. Tests the Skolik mechanism.\"}, {\"name\": \"iterations_to_target_accuracy\", \"direction\": \"minimize\", \"description\": \"Number of total COBYLA iterations to reach test accuracy >= 0.8 (or to budget 400 if never reached). Tests practical convergence speed.\"}], \"datasets\": [{\"name\": \"uci_iris_binary_pca4\", \"source\": \"sklearn.datasets.load_iris + PCA to 4 dimensions\", \"description\": \"Iris dataset restricted to classes {versicolor=1, virginica=2} (binary, 100 samples). PCA to 4 dimensions on standard-scaled features. 80/20 split. Approximately linearly separable; logistic regression should reach >=85 percent.\"}, {\"name\": \"breast_cancer_pca4\", \"source\": \"sklearn.datasets.load_breast_cancer + PCA to 4 dimensions\", \"description\": \"Wisconsin breast cancer dataset, 569 samples, 30 features reduced to 4 via PCA. 80/20 split. Non-trivially separable; logistic regression reaches around 0.92.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1500}}", "requirements": "", "rubric": "{\"id\": \"q08-root\", \"requirements\": \"An empirical study of layerwise vs end-to-end training of a 4-qubit reps=5 EfficientSU2 VQC on UCI binary classification (iris-binary and breast-cancer). The agent must (a) implement the layerwise training schedule manually (train reps=1, freeze, add reps=1, train, ...), (b) implement the end-to-end baseline that trains all 5 reps together, (c) measure mean gradient norm during the first 10 optimization steps of each schedule to test the Skolik 2021 mechanism, and (d) compare against logistic regression + parameter-matched MLP baselines. Score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"Layerwise studies are scored on (i) correctness of the freeze-and-add mechanism (verifiable: the layerwise condition should have N optimizer.minimize() calls each operating on a subset of parameters, not 1 single call over all parameters), (ii) parameter counts matching across conditions (the layerwise and end-to-end VQCs must end with the same 40-parameter ansatz), (iii) the gradient-norm probe being measured at consistent points across conditions, (iv) the random-init control producing accuracy near 0.5 on both datasets (otherwise the ansatz is leaking labels through initialization).\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q08-code\", \"requirements\": \"Code-development bucket: layerwise freeze-and-add schedule implemented manually, classical baselines parameter-matched, gradient-norm probe consistent.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q08-code-layerwise-mechanism\", \"requirements\": \"The layerwise condition trains the EfficientSU2(reps=5) ansatz by 5 sequential optimizer.minimize() calls. Each call trains only the parameters belonging to the newly added reps=1 block while the previous blocks have their parameters bound to their previously converged values via qiskit.QuantumCircuit.assign_parameters() on a partial dictionary. After all 5 calls the final fitted VQC has 40 trainable parameters, identical in count to the end-to-end VQC.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q08-code-gradient-probe\", \"requirements\": \"Mean gradient norm during the first 10 optimization steps is recorded per condition via the qiskit_machine_learning VQC callback or via a custom wrapper around the loss function that uses parameter-shift gradients. For layerwise, the metric is averaged across the first 10 steps of each newly added block (so total of 50 steps averaged, since 5 blocks x 10). For end-to-end, the metric is the first 10 steps of the single training run.\", \"weight\": 7.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q08-code-classical-baselines\", \"requirements\": \"mlp_matched uses sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(8,)) for ~49 parameters, within 20 percent of the VQC's 40 parameters. logistic_regression uses sklearn.linear_model.LogisticRegression(max_iter=1000). Both train on the same 80/20 train/test split as the VQC conditions using the same per-seed random_state.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q08-exec\", \"requirements\": \"Execution-validity bucket: all 30 cells ran and produced numerically valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q08-exec-cells-ran\", \"requirements\": \"At least 27 cells out of 30 expected (5 conditions x 2 datasets x 3 seeds) completed without unhandled errors and produced a test_accuracy value. Missing more than 3 cells (10 percent) without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q08-exec-numerical\", \"requirements\": \"Numerical validity: test_accuracy in [0, 1] with non-degenerate predictions (no condition collapses to a single-class predictor on all test samples). The random_init_control condition produces test_accuracy approximately 0.5 +/- 0.1 on both datasets (otherwise label leakage in the untrained ansatz). mean_gradient_norm_first10 is strictly positive across all cells.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q08-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q08-result-h1-quant\", \"requirements\": \"Quantitative test of H1. On at least 1 of 2 datasets, is layerwise test_accuracy >= end-to-end test_accuracy + 3 absolute pp (3-seed mean)? 100% if gap >= 3pp on at least 1 dataset, 67% if gap >= 1pp on at least 1 dataset, 33% if layerwise marginally faster on any dataset, 0% if end-to-end equals or beats layerwise on both datasets.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q08-result-h2-quant\", \"requirements\": \"Quantitative test of H2. Is mean_gradient_norm_first10 of layerwise >= 5x mean_gradient_norm_first10 of end-to-end (3-seed mean, across both datasets combined)? 100% if ratio >= 5x, 67% if 2-5x, 33% if any positive ratio (layerwise > end-to-end), 0% otherwise.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q08-result-h3-quant\", \"requirements\": \"Quantitative test of H3. Do both layerwise and end-to-end test_accuracy exceed random_init_control test_accuracy by >= 5 absolute pp on both datasets (3-seed mean)? 100% if both gaps >= 5pp on both datasets, 67% if both on 1 dataset, 33% if at least one VQC beats random anywhere, 0% if random matches or beats trained VQCs.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q08-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by specific 3-seed mean test_accuracy, mean_gradient_norm_first10, and iterations_to_target_accuracy values per (condition, dataset). References Skolik et al. Quantum 2021 and discusses whether the layerwise gradient-norm advantage matches their theoretical prediction.\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q08.yaml", "rubric_file": "tasks/quantum/rubrics/Q08.json"} +{"id": "Q09", "domain": "quantum", "title": "Noise-aware variational quantum classifier training", "topic": "Noise-aware variational quantum classifier training: comparing training under simulated depolarizing noise vs ideal-statevector training, evaluating both robustness to test-time noise and clean-test transfer", "domains": ["ai-for-quantum", "noisy-quantum-training", "robustness"], "arxiv_id": null, "venue": "ARC-Bench 2026", "metric_key": "test_accuracy", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 1500, "synthesis": "Real quantum hardware exhibits gate errors, readout errors, and\ndecoherence at rates that vary across devices. A VQC trained on a\nnoiseless statevector simulator may converge to parameters that work\non the ideal training distribution but degrade sharply when deployed\non a noisy device. The noise-aware training strategy is to inject\nsimulated noise during training (e.g. a depolarizing channel after\neach two-qubit gate) so the optimizer can find parameters that are\nrobust to that noise model. The closest classical analog is\nadversarial training or training-time data augmentation.\n\nTwo open empirical questions follow. First, does noise-aware training\nproduce strictly more robust models on noisy test data than ideal\ntraining, and at what training noise rate is the robustness gain\nworth the loss of clean-test accuracy? Second, does training at high\nnoise (p=0.01) cause negative transfer to clean test data — i.e.\ndoes the model overfit to the noise statistics and underperform an\nideal-trained model on noise-free evaluation? Wang et al. (NeurIPS\n2022 \"QuantumNAS: Noise-Adaptive Search for Robust Quantum\nCircuits\") and Sharma et al. (PRX Quantum 2022 \"Trainability of\ndissipative perceptron-based quantum neural networks\") explored\nthese tradeoffs at small scale, leaving the precise sweet-spot\nnoise rate as a benchmark question.\n\nImplementation guidance is provided by the quantum-qiskit skill at\nstage 10. Use qiskit_aer.AerSimulator with a custom NoiseModel that\napplies a single-qubit and two-qubit depolarizing channel after each\nparameterized gate. The training uses\nqiskit_machine_learning.algorithms.VQC with a custom\nqiskit.primitives.BackendSamplerV2 bound to the noisy AerSimulator\nfor noisy-train conditions, and the noiseless reference\nqiskit.primitives.StatevectorSampler for the ideal-train condition.\nEvaluation always runs on both an ideal statevector test set and a\nnoisy test set at p=0.01 (the \"deployment noise rate\") so each cell\nproduces two test_accuracy values: clean and noisy. The primary\nmetric is the average across both.\n\nExperimental protocol. Four quantum training conditions vary the\ntraining noise rate: ideal (p=0), low noise (p=0.001), mid noise\n(p=0.005), high noise (p=0.01). Two baselines complete the bench\ngate: a no-training control (random initialization, no fit) and\nlogistic regression on the same 4D features. All six conditions\nare evaluated on a single synthetic binary classification dataset\n(make_classification with class_sep=0.5, 200 samples, 4 features),\naveraged over 3 seeds. Total: 6 conditions x 1 dataset x 3 seeds\n= 18 cells.\n\nResearch question: *On a synthetic 4D binary classification task,\ndoes noise-aware VQC training at a moderate training noise rate\n(p=0.005) produce a model that is more robust to test-time\ndepolarizing noise (p=0.01) than an ideal-trained VQC, and does\nhigh training noise (p=0.01) cause negative transfer to clean\ntest data?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"On the noisy test set at p=0.01, the noisy-trained-mid VQC (training noise p=0.005) achieves test accuracy at least 5 absolute percentage points higher than the ideal-trained VQC (3-seed mean), demonstrating that moderate noise-aware training improves test-time noise robustness.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"On the ideal test set (noiseless), the ideal-trained VQC outperforms every noisy-trained variant by at least 2 absolute percentage points (3-seed mean), reflecting the no-free-lunch tradeoff that noise-aware training sacrifices some clean-test accuracy.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The high-noise-trained VQC (training noise p=0.01) performs worse than the mid-noise-trained VQC on BOTH clean and noisy test sets (3-seed mean), indicating that p=0.01 is past the sweet spot and the optimizer is overfitting to noise statistics — negative transfer.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Does noise-aware VQC training at a moderate training noise rate (p=0.005) produce a model more robust to test-time depolarizing noise than ideal training, and does high training noise cause negative transfer on clean test data?\", \"conditions\": [{\"name\": \"train_ideal\", \"description\": \"VQC trained on noiseless qiskit.primitives.StatevectorSampler. EfficientSU2(num_qubits=4, reps=2, entanglement='linear') ansatz + ZFeatureMap feature map. qiskit_machine_learning.VQC(optimizer=COBYLA(maxiter=200), sampler=StatevectorSampler()). After training, evaluate on BOTH ideal test (StatevectorSampler) and noisy test (depolarizing p=0.01 NoiseModel).\"}, {\"name\": \"train_noisy_low\", \"description\": \"Same ansatz + feature map. VQC trained on a noisy AerSimulator with depolarizing channel p=0.001 applied after every single-qubit and two-qubit gate. Same COBYLA maxiter=200. Evaluate on both ideal and noisy test sets.\"}, {\"name\": \"train_noisy_mid\", \"description\": \"Same as train_noisy_low but training noise rate p=0.005.\"}, {\"name\": \"train_noisy_high\", \"description\": \"Same as train_noisy_low but training noise rate p=0.01 (matches the test-noise rate).\"}], \"baselines\": [\"no_training_control: Same ansatz but parameters randomly initialized and never trained. Evaluate predict() directly on both test sets. Establishes the untrained floor.\", \"logistic_regression: sklearn.linear_model.LogisticRegression(max_iter=1000, random_state=seed) on the same 4 features. Classical baseline.\"], \"metrics\": [{\"name\": \"test_accuracy\", \"direction\": \"maximize\", \"description\": \"Average of test_accuracy_ideal and test_accuracy_noisy_p01, 3-seed mean. Primary metric for the bench.\"}, {\"name\": \"test_accuracy_ideal\", \"direction\": \"maximize\", \"description\": \"Test accuracy on the noiseless test set (StatevectorSampler evaluation), 3-seed mean.\"}, {\"name\": \"test_accuracy_noisy_p01\", \"direction\": \"maximize\", \"description\": \"Test accuracy on the noisy test set at depolarizing rate p=0.01 (AerSimulator with NoiseModel), 3-seed mean. The 'deployment-noise' robustness metric.\"}], \"datasets\": [{\"name\": \"synthetic_classification_4d_sep05\", \"source\": \"sklearn.datasets.make_classification\", \"description\": \"make_classification(n_samples=200, n_features=4, n_informative=3, n_redundant=0, n_clusters_per_class=1, class_sep=0.5, flip_y=0.05, random_state=seed). Moderately difficult binary classification, native 4D. StandardScaler fit on train only. 80/20 split.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1500}}", "requirements": "", "rubric": "{\"id\": \"q09-root\", \"requirements\": \"An empirical study of noise-aware VQC training. Four quantum training conditions vary the training-time depolarizing noise rate (ideal, p=0.001, p=0.005, p=0.01), evaluated on both an ideal test set and a noisy test set at p=0.01. The agent must (a) implement the noise model via qiskit_aer.NoiseModel applying single-qubit and two-qubit depolarizing channels, (b) train VQC under each noise level using qiskit_machine_learning.VQC with the appropriate sampler, (c) evaluate each trained model on BOTH ideal and noisy test sets to expose the robustness-vs-clean-accuracy tradeoff, and (d) score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"Noise-aware training studies are scored on (i) correctness of the qiskit_aer NoiseModel construction (depolarizing channel must be applied after every gate, not only after measurement; verifiable by inspecting the agent's noise model setup), (ii) test_accuracy reporting BOTH ideal and noisy variants per cell (without both, H1 cannot be evaluated), (iii) the noise-rate sweep producing visibly different test accuracies (cells reporting identical numbers across noise levels indicate the noise model is not actually being applied during training), and (iv) numerical evidence backing the three-way comparison: ideal-train vs noisy-mid-train vs noisy-high-train.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q09-code\", \"requirements\": \"Code-development bucket: NoiseModel correctly constructed, training pipeline applies noise, evaluation on both ideal and noisy test.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q09-code-noise-model\", \"requirements\": \"qiskit_aer.NoiseModel with depolarizing_error applied to all single-qubit gates (h, ry, rz, etc.) AND two-qubit gates (cx). For a noisy-train condition at rate p, the single-qubit error rate is p and the two-qubit error rate is p (or some agent-documented scaling of p). A sanity check verifies that the same ansatz parameters produce different expectation values under the noise model vs under StatevectorSampler (otherwise the noise is not being applied).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q09-code-training-pipeline\", \"requirements\": \"Each training condition uses qiskit_machine_learning.VQC with a qiskit.primitives.BackendSamplerV2 (or BackendSampler) bound to either AerSimulator (for noisy training) or StatevectorSampler (for ideal training). Same ansatz family (EfficientSU2 num_qubits=4 reps=2 entanglement='linear'), same optimizer (COBYLA maxiter=200), same per-seed random initialization across noise-rate conditions for paired comparison.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"q09-code-dual-eval\", \"requirements\": \"Each trained VQC is evaluated on TWO test sets: (a) ideal StatevectorSampler (test_accuracy_ideal), (b) noisy AerSimulator at depolarizing p=0.01 (test_accuracy_noisy_p01). Both numbers are logged per cell. The primary 'test_accuracy' metric is the average of the two. Each cell's METRIC_RESULT JSON includes all three numbers.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q09-exec\", \"requirements\": \"Execution-validity bucket: all 18 cells ran with valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q09-exec-cells-ran\", \"requirements\": \"At least 16 cells out of 18 expected (6 conditions x 1 dataset x 3 seeds) completed without unhandled errors and produced both test_accuracy_ideal AND test_accuracy_noisy_p01 values. Missing more than 2 cells (10 percent) without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q09-exec-numerical\", \"requirements\": \"Numerical validity: both test accuracies in [0, 1]; for any single training condition, test_accuracy_ideal and test_accuracy_noisy_p01 differ by at least 0.01 (cells reporting bit-identical clean and noisy accuracy mean the noise model isn't being applied at evaluation); no_training_control accuracy is in [0.4, 0.6] (random predictor floor).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q09-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q09-result-h1-quant\", \"requirements\": \"Quantitative test of H1. On the noisy test set at p=0.01, is train_noisy_mid (training noise p=0.005) test_accuracy >= train_ideal test_accuracy + 5 absolute pp (3-seed mean)? 100% if gap >= 5pp, 67% if 2-5pp, 33% if any positive gap, 0% if ideal train matches or beats noisy-mid train on noisy test.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q09-result-h2-quant\", \"requirements\": \"Quantitative test of H2. On the ideal test set, does train_ideal beat EVERY noisy-trained variant by >= 2 absolute pp (3-seed mean)? 100% if gap >= 2pp vs all three noisy variants, 67% if gap >= 2pp vs at least 2 of 3, 33% if marginal advantage on at least 1, 0% if noisy training matches or beats ideal training on clean test (no tradeoff).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q09-result-h3-quant\", \"requirements\": \"Quantitative test of H3. Does train_noisy_high underperform train_noisy_mid on BOTH the clean and the noisy test set (3-seed mean)? 100% if cleanly worse on both, 67% if worse on 1 of 2 by >= 3pp, 33% if marginally worse on 1, 0% if high-noise training matches or beats mid-noise on both test sets (no negative transfer observed).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q09-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by per-(training_noise_rate, test_set) 3-seed mean accuracy values. References Wang NeurIPS 2022 QuantumNAS and Sharma PRX Quantum 2022. Discusses whether the observed sweet-spot noise rate matches the test-deployment rate, and acknowledges that AerSimulator depolarizing channel is a simplified model relative to real hardware noise (T1/T2, crosstalk, readout asymmetry not captured).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q09.yaml", "rubric_file": "tasks/quantum/rubrics/Q09.json"} +{"id": "Q10", "domain": "quantum", "title": "Quantum autoencoder for pure-state compression", "topic": "Quantum autoencoder for pure-state compression: training a parameterized circuit to compress n-qubit Haar-random states into m state (verified via a\nSWAP test against fresh |0> ancillas), which is equivalent to\nmaximizing the fidelity between the reconstructed state and the\noriginal input state.\n\nThe training data is an ensemble of input states drawn from some\ndistribution. If the ensemble is concentrated in a low-dimensional\nmanifold of the 2^n Hilbert space, the autoencoder can in principle\nreach near-perfect reconstruction at significant compression (m <\nn). If the ensemble is Haar-random pure states, no compression is\nachievable in principle and the average reconstruction fidelity is\nupper bounded by 2^(m-n) (the latent space is too small to hold a\ngeneric state).\n\nThe empirical question this topic addresses is what compression\nfidelity is achievable on a small Haar-random ensemble at a few\ncompression ratios, and whether the trained autoencoder\nmeaningfully beats a random-unitary baseline (which represents the\nnull hypothesis: a random encoder cannot do better than chance).\nThis is the cleanest possible test of \"does the autoencoder learn\nanything\" without confounding from structured ensembles.\n\nImplementation guidance is provided by the quantum-qiskit skill at\nstage 10. The encoder and decoder are EfficientSU2(num_qubits=n,\nreps=3, entanglement='linear') circuits built from\nqiskit.circuit.library. The SWAP-test loss is implemented manually\nvia auxiliary qubits and a Hadamard-controlled-SWAP-Hadamard pattern.\nUse qiskit.primitives.StatevectorSampler for the SWAP-test\nmeasurement and qiskit.primitives.StatevectorEstimator for the\nreconstruction-fidelity diagnostic. Training uses\nqiskit_algorithms.optimizers.COBYLA.minimize() over the SWAP-test\nloss (DO NOT use qiskit_algorithms.VQE — see the quantum-qiskit\nskill's note about qiskit 2.x compatibility).\n\nExperimental protocol. Three quantum compression conditions probe\nthe compression-fidelity tradeoff: compress 4 qubits -> 2 latent,\ncompress 4 -> 3, compress 6 -> 3. Two no-skill baselines establish\nthe floor: no_compression (identity encoder, m=n, fidelity should\nbe ~1.0) and random_unitary_compression (encoder parameters drawn\nrandomly and never trained). Test data is an ensemble of 20\nHaar-random pure states per seed. Each (condition, seed) cell\nreports the mean reconstruction fidelity over the 20 test states.\nTotal: 5 conditions x 1 ensemble x 3 seeds = 15 cells.\n\nResearch question: *On an ensemble of Haar-random pure states, what\nreconstruction fidelity does a trained quantum autoencoder achieve\nat compression ratios 4->2, 4->3, and 6->3, and how much better is\nit than an untrained (random-unitary) encoder at the same\ncompression ratio?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"The trained autoencoder achieves reconstruction fidelity at least 0.2 absolute higher than a random-unitary encoder at the same compression ratio (3-seed mean across the 20 Haar test states), on at least 2 of 3 compression-ratio conditions, confirming that training extracts non-trivial structure from the Haar ensemble.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Trained reconstruction fidelity is strictly monotone in latent dimension: the 4->3 condition achieves fidelity at least 0.15 higher than the 4->2 condition (3-seed mean), as expected from the Hilbert-space capacity argument 2^m / 2^n.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"The no_compression baseline (identity encoder, m=n) achieves reconstruction fidelity at least 0.95 (3-seed mean), confirming the experimental setup is correctly implemented — the trash-qubit register is non-functional when m=n and the test pipeline should report near-perfect fidelity.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"On an ensemble of Haar-random pure states, what reconstruction fidelity does a trained quantum autoencoder achieve at compression ratios 4->2, 4->3, 6->3, and how much better is it than an untrained encoder at the same compression?\", \"conditions\": [{\"name\": \"compress_4_to_2\", \"description\": \"Encoder: EfficientSU2(num_qubits=4, reps=3, entanglement='linear'). After applying the encoder, qubits 2 and 3 are the 'trash' register and the SWAP test against fresh |0> ancillas is summed into the loss. Latent qubits: 0, 1. Decoder is the inverse encoder. Training: 100 iterations of qiskit_algorithms.optimizers.COBYLA(maxiter=100).minimize() on a fixed batch of 5 Haar-random states drawn once per seed.\"}, {\"name\": \"compress_4_to_3\", \"description\": \"Same as compress_4_to_2 but with 1 trash qubit (qubit 3) and 3 latent qubits (0, 1, 2). Easier compression task.\"}, {\"name\": \"compress_6_to_3\", \"description\": \"Encoder: EfficientSU2(num_qubits=6, reps=3, entanglement='linear'). 3 trash qubits (3, 4, 5) and 3 latent qubits (0, 1, 2). Harder compression task (50 percent compression on a larger Hilbert space).\"}], \"baselines\": [\"no_compression: Encoder is the identity circuit (no parameters). All qubits are latent (m=n); no trash qubits. The SWAP test trivially passes and reconstruction fidelity equals 1.0 exactly. Sanity check on the eval pipeline.\", \"random_unitary_compression: Same EfficientSU2 ansatz as the trained conditions, but parameters drawn randomly from Uniform[-pi, pi] once per seed and NEVER trained. Establishes the no-skill floor — represents 'what if the encoder is just random noise'.\"], \"metrics\": [{\"name\": \"reconstruction_fidelity\", \"direction\": \"maximize\", \"description\": \"Mean of ||^2 over the 20 Haar-random test states, 3-seed mean. Primary metric. Computed as 1 - SWAP_test_probability_of_outcome_1 averaged over the trash qubits, OR via direct Statevector inner product when running on the noiseless simulator.\"}, {\"name\": \"swap_test_loss\", \"direction\": \"minimize\", \"description\": \"Training loss at the final iteration. Sum over the m trash qubits of P(SWAP_test_outcome=1) on each. Equivalent to 1 - average_trash_qubit_fidelity_with_|0>.\"}, {\"name\": \"training_time_sec\", \"direction\": \"minimize\", \"description\": \"Wall-clock seconds for the 100 COBYLA iterations of training. Reported for context — the compression task should fit well under 60 seconds per cell.\"}], \"datasets\": [{\"name\": \"haar_random_states_n4\", \"source\": \"qiskit.quantum_info.random_statevector(2**4, seed=ensemble_seed) — generate 20 Haar-random 4-qubit pure states with a fixed per-seed ensemble seed\", \"description\": \"An ensemble of 20 Haar-random 4-qubit pure states (for compress_4_to_2 and compress_4_to_3 conditions). A separate Haar ensemble of 20 6-qubit states is generated for the compress_6_to_3 condition. Training uses a fixed batch of 5 of these states; testing uses all 20. The ensemble is fully unstructured by construction, so any non-trivial reconstruction fidelity above the random-unitary baseline reflects the autoencoder learning a structured compression rule on Haar states.\"}], \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 1200}}", "requirements": "", "rubric": "{\"id\": \"q10-root\", \"requirements\": \"An empirical study of a quantum autoencoder on Haar-random pure states. The agent must (a) construct encoder + decoder circuits as EfficientSU2(reps=3) from qiskit.circuit.library, (b) implement the SWAP-test loss via auxiliary qubits + Hadamard-controlled-SWAP-Hadamard or compute trash-qubit fidelity-to-|0> directly via Statevector, (c) train each compression-ratio condition by qiskit_algorithms.optimizers.COBYLA.minimize() on a fixed batch of 5 Haar states per seed, (d) evaluate on 20 unseen Haar states reporting reconstruction_fidelity, and (e) score H1/H2/H3 with numerical evidence.\", \"judging_note\": \"Quantum autoencoder studies are scored on (i) correctness of the SWAP-test or fidelity computation (verifiable: no_compression baseline must report fidelity ~1.0, otherwise the eval pipeline is broken), (ii) the random_unitary_compression baseline producing fidelity in the expected range for that compression ratio (theoretical floor is 2^(m-n) per Page formula, e.g. for 4->2 the random baseline fidelity is around 0.0625), (iii) the trained conditions being measurably above the random baseline (otherwise no learning), and (iv) numerical evidence for the latent-dimension capacity effect (4->3 better than 4->2).\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"q10-code\", \"requirements\": \"Code-development bucket: encoder/decoder built from qiskit.circuit.library, SWAP-test or direct-fidelity loss implemented correctly, training pipeline runs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q10-code-encoder-decoder\", \"requirements\": \"Encoder is qiskit.circuit.library.EfficientSU2(num_qubits=n, reps=3, entanglement='linear'). Decoder is the inverse of the encoder (via QuantumCircuit.inverse()) sharing the same parameters but acting in reverse, applied after fresh |0> ancillas have replaced the trash qubits. Both encoder and decoder are built once from qiskit.circuit.library — NOT hand-coded as H/RY/RZ + CNOT stacks (the quantum-qiskit skill warns against this anti-pattern).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q10-code-loss-eval\", \"requirements\": \"Training loss is implemented as either (a) a SWAP-test circuit using auxiliary qubits and the standard Hadamard-controlled-SWAP-Hadamard pattern, OR (b) a direct Statevector computation: trace out the trash register, compute its partial-trace overlap with |0><0|, and sum across trash qubits. Both implementations are valid. The reconstruction_fidelity metric on the test set uses ||^2 computed via qiskit.quantum_info.Statevector inner product (no SWAP test needed at evaluation since we have access to both states).\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"q10-code-pipeline\", \"requirements\": \"Training: 100 iterations of qiskit_algorithms.optimizers.COBYLA.minimize() over the SWAP-test loss, summed over a fixed batch of 5 Haar-random states drawn once per (condition, seed) pair via qiskit.quantum_info.random_statevector. Evaluation: 20 fresh Haar-random states drawn from a different RNG stream than the training batch. Each cell logs METRIC_RESULT JSON with condition, seed, reconstruction_fidelity_mean (over 20 test states), swap_test_loss_final, training_time_sec.\", \"weight\": 6.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q10-exec\", \"requirements\": \"Execution-validity bucket: all 15 cells ran with valid outputs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"q10-exec-cells-ran\", \"requirements\": \"At least 14 cells out of 15 expected (5 conditions x 1 ensemble x 3 seeds) completed without unhandled errors and produced a reconstruction_fidelity_mean value. Missing more than 1 cell without documented justification fails this leaf.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q10-exec-numerical\", \"requirements\": \"Numerical validity: reconstruction_fidelity is in [0, 1] for every cell; the no_compression baseline reports fidelity in [0.95, 1.0] (else eval pipeline is broken); the random_unitary_compression baseline for 4->2 reports fidelity in [0.04, 0.15] (matching the theoretical 2^(m-n) = 0.0625 average for Haar states); no cell reports identical fidelity across all 3 seeds (would indicate the Haar sampler is not actually randomizing per seed).\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"q10-results\", \"requirements\": \"Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"q10-result-h1-quant\", \"requirements\": \"Quantitative test of H1. On at least 2 of 3 compression-ratio conditions, is trained reconstruction_fidelity >= random_unitary_compression reconstruction_fidelity + 0.2 absolute (3-seed mean)? 100% if gap >= 0.2 on at least 2/3 conditions, 67% if gap >= 0.1 on at least 2/3, 33% if any positive gap on at least 1, 0% if random unitary equals or beats trained on all 3.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q10-result-h2-quant\", \"requirements\": \"Quantitative test of H2. Is reconstruction_fidelity(compress_4_to_3) - reconstruction_fidelity(compress_4_to_2) >= 0.15 absolute (3-seed mean)? 100% if gap >= 0.15, 67% if 0.05-0.15, 33% if any positive gap (capacity effect at least directionally correct), 0% otherwise.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q10-result-h3-quant\", \"requirements\": \"Quantitative test of H3. Does no_compression baseline report reconstruction_fidelity >= 0.95 (3-seed mean)? 100% if >= 0.95, 67% if >= 0.85, 33% if >= 0.70, 0% otherwise — failure of this sanity check means the eval pipeline is misconstructed.\", \"weight\": 8.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"q10-result-writeup\", \"requirements\": \"Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by 3-seed mean reconstruction_fidelity per condition. References Romero, Olson, Aspuru-Guzik Quantum Sci Tech 2017 and discusses whether the trained autoencoder's advantage over random_unitary_compression on Haar-random data is consistent with the Page-formula expectation (Haar states have no exploitable structure, so any improvement reflects the optimizer finding a non-trivial subspace).\", \"weight\": 12.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 9, "manifest_file": "tasks/quantum/manifests/Q10.yaml", "rubric_file": "tasks/quantum/rubrics/Q10.json"} diff --git a/data/statistics.jsonl b/data/statistics.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..811f4ee39d03dd94b08821e8563352a39b490976 --- /dev/null +++ b/data/statistics.jsonl @@ -0,0 +1,3 @@ +{"id": "S01", "domain": "statistics", "title": "Bootstrap confidence intervals under non-Gaussian data", "topic": "Bootstrap confidence interval coverage under light-tailed, skewed, and heavy-tailed data", "domains": ["statistics", "resampling-methods", "simulation-study"], "arxiv_id": null, "venue": "ARC-Bench Statistics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 300, "synthesis": "Bootstrap confidence intervals are widely used when analytic standard errors\nare inconvenient, but their finite-sample coverage can change substantially\nwith skewness, heavy tails, contamination, and the choice of statistic. A\ncredible study of this topic should simulate data-generating processes with\nknown estimands, compare several bootstrap confidence interval methods, and\nevaluate empirical coverage and interval width.\n\nThe study should not only report whether nominal 95% intervals contain the\ntruth, but also explain when wider intervals, robust estimators, or\nstudentized intervals improve reliability. The research question is: *how do\nbootstrap interval choices and estimator choices affect empirical coverage\nunder light-tailed, skewed, and heavy-tailed data?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"Percentile bootstrap intervals for the sample mean achieve near-nominal coverage under light-tailed Gaussian data, especially at moderate sample sizes.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Percentile bootstrap intervals for the sample mean under-cover under heavy-tailed or contaminated data.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Robust location estimators, such as the median or trimmed mean, improve coverage stability or interval behavior under heavy-tailed or contaminated data.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How do bootstrap confidence interval methods behave across light-tailed, skewed, and heavy-tailed distributions?\", \"estimands\": [{\"name\": \"population_mean\", \"description\": \"The true mean of each simulated data-generating process when it exists.\"}, {\"name\": \"robust_location\", \"description\": \"A robust target such as the median or trimmed mean, used when heavy tails or contamination make mean-based inference unstable.\"}], \"conditions\": [{\"name\": \"normal_theory_mean_ci\", \"description\": \"Classical normal or t-based interval for the sample mean.\"}, {\"name\": \"percentile_bootstrap_mean\", \"description\": \"Percentile bootstrap interval for the sample mean.\"}, {\"name\": \"studentized_bootstrap_mean\", \"description\": \"Studentized or bootstrap-t interval for the sample mean, or a justified bootstrap alternative.\"}, {\"name\": \"robust_estimator_bootstrap\", \"description\": \"Bootstrap interval for a robust estimator such as the median or trimmed mean.\"}], \"data_generating_conditions\": [{\"name\": \"gaussian\", \"description\": \"Light-tailed data where standard mean inference should work well.\"}, {\"name\": \"lognormal_skewed\", \"description\": \"Skewed data where percentile intervals may show asymmetric finite-sample behavior.\"}, {\"name\": \"student_t_heavy_tailed\", \"description\": \"Heavy-tailed data with unstable sample means.\"}, {\"name\": \"contaminated_normal\", \"description\": \"Mostly normal data with a small fraction of extreme outliers.\"}], \"metrics\": [{\"name\": \"coverage\", \"direction\": \"target_0.95\", \"description\": \"Empirical fraction of intervals containing the true estimand.\"}, {\"name\": \"coverage_error\", \"direction\": \"minimize_abs\", \"description\": \"Absolute deviation from nominal 95% coverage.\"}, {\"name\": \"interval_width\", \"direction\": \"minimize_given_coverage\", \"description\": \"Average interval width, interpreted jointly with coverage.\"}, {\"name\": \"failure_rate\", \"direction\": \"minimize\", \"description\": \"Fraction of simulation runs where an interval could not be computed or produced invalid values.\"}], \"simulation_settings\": {\"sample_sizes\": [30, 100, 500], \"monte_carlo_repetitions\": 500, \"bootstrap_resamples\": 1000, \"seeds\": [0, 1, 2, 3, 4]}, \"expected_artifacts\": [{\"path\": \"src/experiment.py\", \"description\": \"Executable simulation code implementing confidence interval methods and data-generating processes.\"}, {\"path\": \"results/metrics.json\", \"description\": \"Machine-readable coverage, coverage error, interval width, and failure rate by method, distribution, and sample size.\"}, {\"path\": \"results/figures/\", \"description\": \"Diagnostic plots comparing coverage and interval width across methods and data-generating conditions.\"}, {\"path\": \"report/paper.md\", \"description\": \"Paper-style report explaining the setup, methods, results, limitations, and per-hypothesis conclusions.\"}], \"dependencies\": {\"python\": [\"numpy\", \"pandas\", \"scipy\", \"matplotlib\"]}, \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 300}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_coverage_metrics_table\", \"type\": \"artifact\", \"description\": \"A machine-readable metrics artifact (e.g. results/metrics.json) exists reporting empirical coverage AND interval width broken down by CI method, data-generating distribution, and sample size.\", \"must_pass\": true}, {\"id\": \"req_coverage_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain at least 3 numeric (non-null, finite) empirical coverage values in [0, 1] for distinct (method, distribution) cells, including at least one Gaussian cell and one heavy-tailed or contaminated cell so H1 and H2 can be evaluated.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical coverage / interval-width evidence (specific values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_diagnostic_figure\", \"type\": \"artifact\", \"description\": \"At least one figure file (PNG or PDF, ≥150 DPI for raster) exists under results/figures/ comparing coverage and/or interval width across methods and data-generating conditions, with axes labeled and a legend.\", \"must_pass\": false}, {\"id\": \"req_width_tradeoff_writeup\", \"type\": \"discussion\", \"description\": \"The report discusses the coverage-versus-interval-width tradeoff — recognising that higher coverage may simply come from wider intervals and that width must be read jointly with coverage. Nice-to-have, not blocking.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names the Monte Carlo repetition count, bootstrap resample count, and at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"s01-root\", \"requirements\": \"A credible simulation study evaluating whether bootstrap confidence interval choice affects empirical coverage under light-tailed, skewed, and heavy-tailed data. The submission should implement multiple CI methods, simulate several data-generating distributions and sample sizes, report coverage and interval width over repeated trials, and connect the numeric findings to the three hypotheses.\", \"judging_note\": \"Score on statistical substance, correct simulation design, and directional correctness of evidence. Do not require exact repetition counts if the submission is computationally reasonable. Partial but well-motivated simulations deserve partial credit; rigid naming differences should not penalize a substantively correct experiment.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"s01-code\", \"requirements\": \"The bootstrap simulation code implements meaningful confidence interval comparisons across relevant distributional regimes.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"s01-code-ci-methods\", \"requirements\": \"The submission implements at least two bootstrap confidence interval methods, typically percentile bootstrap and studentized/bootstrap-t or another justified alternative. Methods should be implemented as distinct code paths rather than cosmetic options.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s01-code-robust-estimator\", \"requirements\": \"The submission includes a robust location estimator, such as median or trimmed mean, and compares it against the ordinary sample mean under heavy-tailed or contaminated data.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s01-code-dgps\", \"requirements\": \"The submission simulates multiple data-generating processes covering at least light-tailed and heavy-tailed cases, with skewed data such as lognormal or contaminated data receiving additional credit.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"s01-code-sample-sizes\", \"requirements\": \"The simulation evaluates more than one sample size so that finite-sample behavior can be distinguished from asymptotic behavior.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"s01-exec\", \"requirements\": \"Execution produces coverage and interval-quality metrics adequate to evaluate bootstrap CI behavior.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"s01-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact, such as results/metrics.json, containing numeric empirical coverage and interval width by condition, distribution, and sample size. Coverage error or failure rate may supplement these metrics.\", \"weight\": 15.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s01-exec-repetitions\", \"requirements\": \"Reported metrics are based on repeated Monte Carlo trials and bootstrap resampling. The exact number of repetitions need not match the topic file, but it should be large enough to make coverage comparisons meaningful and should be honestly reported.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s01-exec-truth\", \"requirements\": \"The simulation correctly defines the true estimand for each data-generating process, such as the true mean or true robust location target, and checks whether confidence intervals contain that estimand.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"s01-paper\", \"requirements\": \"The final paper or report addresses the three bootstrap hypotheses with quantitative evidence and a clear statistical narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"s01-result-h1\", \"requirements\": \"The submission evaluates whether percentile bootstrap confidence intervals achieve near-nominal coverage under light-tailed Gaussian data, especially at moderate or larger sample sizes, and states whether H1 is supported, refuted, or inconclusive.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s01-result-h2\", \"requirements\": \"The submission evaluates whether percentile bootstrap intervals for the sample mean undercover under heavy-tailed data and supports the conclusion with empirical coverage numbers.\", \"weight\": 15.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s01-result-h3\", \"requirements\": \"The submission compares robust location estimators against the ordinary sample mean under heavy-tailed or contaminated data and discusses whether robustness improves coverage stability or interval behavior.\", \"weight\": 15.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s01-result-width-tradeoff\", \"requirements\": \"The analysis discusses interval-width tradeoffs, recognizing that higher coverage may come from wider intervals and that width should be interpreted jointly with coverage.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s01-result-writeup\", \"requirements\": \"The README or writeup describes the simulation setup, CI methods, data-generating processes, key numeric results, and per-hypothesis outcomes with appropriate caveats on repetition count, bootstrap count, and Monte Carlo uncertainty.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 12, "manifest_file": "tasks/statistics/manifests/S01.yaml", "rubric_file": "tasks/statistics/rubrics/S01.json"} +{"id": "S02", "domain": "statistics", "title": "Double machine learning for average treatment effect estimation", "topic": "Double machine learning for average treatment effect estimation under nonlinear confounding", "domains": ["statistics", "causal-inference", "semiparametric-estimation"], "arxiv_id": null, "venue": "ARC-Bench Statistics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 300, "synthesis": "Semiparametric causal inference separates a low-dimensional target parameter,\nsuch as the average treatment effect, from infinite-dimensional nuisance\nfunctions such as the propensity score e(X) and outcome regressions m0(X) and\nm1(X). Double machine learning studies how the target parameter can remain\nestimable when those nuisance functions are learned flexibly rather than\nspecified by a finite-dimensional parametric model.\n\nThe benchmark should make this relationship explicit. The study should\nimplement and evaluate treatment effect estimators in simulated observational\ndata with nonlinear confounding, compare naive regression or inverse-propensity\nweighting against orthogonalized / doubly robust estimators, and test how\nNeyman orthogonality and cross-fitting reduce first-order sensitivity to\nnuisance estimation error. The research question is: *when the nuisance\nfunctions are treated as infinite-dimensional objects and estimated with\nflexible learners, does orthogonalization preserve reliable inference for the\nfinite-dimensional ATE?*", "num_hypotheses": 4, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"A naive difference-in-means estimator is biased under confounded treatment assignment.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"A doubly robust / orthogonalized estimator has lower absolute bias than simple plug-in regression or IPW when nuisance functions are nonlinear.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Cross-fitting improves stability or reduces bias compared with fitting nuisance functions and estimating the treatment effect on the same sample.\", \"measurable\": true}, {\"id\": \"H4\", \"statement\": \"Neyman-orthogonal scores make the ATE estimate less sensitive to nuisance-function estimation error than non-orthogonal plug-in estimators.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"How does double machine learning estimate a finite-dimensional ATE while using flexible estimators for infinite-dimensional nuisance functions under nonlinear confounding?\", \"estimand\": {\"name\": \"average_treatment_effect\", \"description\": \"ATE = E[Y(1) - Y(0)] in a simulated observational study with known ground truth.\"}, \"semiparametric_structure\": {\"target_parameter\": {\"name\": \"finite_dimensional_ate\", \"description\": \"The scalar ATE is the parameter of scientific interest.\"}, \"nuisance_functions\": [{\"name\": \"propensity_score\", \"notation\": \"e(X) = P(A = 1 | X)\", \"role\": \"Controls confounded treatment assignment and appears in IPW and AIPW scores.\"}, {\"name\": \"outcome_regression_treated\", \"notation\": \"m1(X) = E[Y | A = 1, X]\", \"role\": \"Models the treated potential-outcome regression.\"}, {\"name\": \"outcome_regression_control\", \"notation\": \"m0(X) = E[Y | A = 0, X]\", \"role\": \"Models the control potential-outcome regression.\"}], \"key_relationship\": \"The benchmark should evaluate whether an orthogonal score protects the finite-dimensional ATE estimate from first-order errors in flexible, effectively infinite-dimensional nuisance estimates.\"}, \"conditions\": [{\"name\": \"difference_in_means\", \"description\": \"Naive treated-control mean difference without confounding adjustment.\"}, {\"name\": \"ols_adjustment\", \"description\": \"Linear regression adjustment for covariates.\"}, {\"name\": \"ipw_logistic\", \"description\": \"Inverse propensity weighting using logistic regression propensity scores.\"}, {\"name\": \"aipw_no_crossfit\", \"description\": \"Augmented inverse propensity weighting using nuisance models fit on the same sample.\"}, {\"name\": \"dml_aipw_crossfit\", \"description\": \"Doubly robust AIPW estimator with K-fold cross-fitting and flexible nuisance models.\"}, {\"name\": \"nuisance_quality_sensitivity\", \"description\": \"A diagnostic condition comparing weaker and stronger nuisance learners to test sensitivity of each estimator to nuisance estimation error.\"}], \"nuisance_models\": [{\"name\": \"propensity_model\", \"candidates\": [\"logistic_regression\", \"random_forest_classifier\", \"gradient_boosting_classifier\"]}, {\"name\": \"outcome_model\", \"candidates\": [\"linear_regression\", \"random_forest_regressor\", \"gradient_boosting_regressor\"]}], \"metrics\": [{\"name\": \"bias\", \"direction\": \"minimize_abs\", \"description\": \"Mean estimated ATE minus true ATE across simulation repetitions.\"}, {\"name\": \"rmse\", \"direction\": \"minimize\", \"description\": \"Root mean squared error of ATE estimates.\"}, {\"name\": \"coverage\", \"direction\": \"target_0.95\", \"description\": \"Empirical coverage of nominal 95% confidence intervals.\"}, {\"name\": \"interval_width\", \"direction\": \"minimize_given_coverage\", \"description\": \"Average confidence interval width.\"}, {\"name\": \"estimate_std\", \"direction\": \"diagnostic\", \"description\": \"Monte Carlo standard deviation of ATE estimates.\"}], \"simulation_settings\": {\"sample_sizes\": [500, 1000, 3000], \"monte_carlo_repetitions\": 500, \"crossfit_folds\": 2, \"seeds\": [0, 1, 2, 3, 4]}, \"data_generating_process\": {\"covariates\": \"X ~ multivariate normal or uniform with nonlinear transformations\", \"treatment\": \"A ~ Bernoulli(sigmoid nonlinear function of X)\", \"outcome\": \"Y = tau * A + nonlinear function of X + noise\", \"true_ate\": 1.0}, \"expected_artifacts\": [{\"path\": \"src/experiment.py\", \"description\": \"Executable simulation code implementing all ATE estimators.\"}, {\"path\": \"results/metrics.json\", \"description\": \"Machine-readable bias, RMSE, coverage, and interval width by estimator and sample size.\"}, {\"path\": \"results/figures/\", \"description\": \"Plots or tables summarizing estimator bias, RMSE, coverage, interval width, and cross-fitting effects.\"}, {\"path\": \"report/paper.md\", \"description\": \"Paper-style report explaining the causal estimand, DGP, estimators, uncertainty method, results, limitations, and per-hypothesis conclusions.\"}, {\"path\": \"README.md\", \"description\": \"Setup and run instructions for reproducing the full experiment from a clean checkout.\"}], \"dependencies\": {\"python\": [\"numpy\", \"pandas\", \"scipy\", \"scikit-learn\", \"matplotlib\"]}, \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 300}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3/h4 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_ate_metrics_table\", \"type\": \"artifact\", \"description\": \"A machine-readable metrics artifact (e.g. results/metrics.json) exists reporting ATE estimate, bias (or absolute bias), and RMSE broken down by estimator and sample size, with confidence-interval coverage or width where applicable.\", \"must_pass\": true}, {\"id\": \"req_bias_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain numeric (non-null, finite) bias or absolute-bias values for at least the naive difference-in-means estimator and the doubly robust / cross-fitted DML estimator, evaluated against the known true ATE, so H1 and H2 can be evaluated quantitatively.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3/h4 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific bias / RMSE / coverage values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_crossfit_implemented\", \"type\": \"artifact\", \"description\": \"The DML condition implements sample splitting or K-fold cross-fitting so nuisance models are trained out-of-fold relative to the observations used in the orthogonal score; a non-cross-fitted AIPW comparison is also present so H3 can be evaluated.\", \"must_pass\": true}, {\"id\": \"req_estimand_writeup\", \"type\": \"discussion\", \"description\": \"The report clearly distinguishes the finite-dimensional causal estimand, the infinite-dimensional nuisance functions, the orthogonal score, and the evaluation metrics — avoiding confusion between prediction performance and ATE estimation quality. Nice-to-have, not blocking.\", \"must_pass\": false}, {\"id\": \"req_seed_documented\", \"type\": \"discussion\", \"description\": \"results.json or a sibling reproducibility section names the Monte Carlo repetition count, cross-fitting fold count, and at least one explicit random seed. Required for full reproducibility but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"s02-root\", \"requirements\": \"A credible semiparametric simulation study evaluating how double machine learning / orthogonalized AIPW estimates a finite-dimensional average treatment effect while using flexible estimators for infinite-dimensional nuisance functions under nonlinear confounding. The submission should implement multiple estimators, simulate observational data with known true ATE, evaluate bias, RMSE, confidence interval behavior, and sensitivity to nuisance estimation quality, and tie the numeric findings to the hypotheses.\", \"judging_note\": \"Score on causal and semiparametric substance rather than exact package choices. A correct hand-written AIPW / DML implementation should receive full credit even if it does not use a specialized causal inference library. Partial credit should reward clear separation of the finite-dimensional estimand from infinite-dimensional nuisance functions, correct use of orthogonal scores, nuisance estimation, cross-fitting, and honest uncertainty reporting.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"s02-code\", \"requirements\": \"The code implements a meaningful comparison of ATE estimators under simulated confounding with known ground truth.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"s02-code-dgp\", \"requirements\": \"The submission implements a simulated observational data-generating process with covariates, confounded treatment assignment, outcome generation, known true average treatment effect, and explicit true or approximate nuisance functions such as propensity scores and outcome regressions.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"s02-code-baselines\", \"requirements\": \"The submission implements simple baseline estimators such as difference-in-means, regression adjustment, and/or inverse propensity weighting so that DML is compared against meaningful alternatives.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s02-code-aipw\", \"requirements\": \"The submission implements a doubly robust or Neyman-orthogonal AIPW-style estimator using estimated propensity and outcome nuisance functions, and explains why the score targets the finite-dimensional ATE while treating those nuisances as flexible or infinite-dimensional.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s02-code-crossfit\", \"requirements\": \"The DML condition uses sample splitting or K-fold cross-fitting so nuisance models are trained out-of-fold relative to the observations used in the orthogonal score.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s02-code-nuisance-models\", \"requirements\": \"The nuisance functions are estimated with reasonable models for the simulated setting, such as logistic regression, random forests, gradient boosting, or other justified supervised learning methods, with at least one comparison that changes nuisance-model flexibility or quality.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"s02-exec\", \"requirements\": \"Execution produces ATE estimation metrics adequate to evaluate bias, precision, and interval behavior.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"s02-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact, such as results/metrics.json, containing numeric ATE estimates, bias or absolute bias, RMSE, and preferably confidence interval coverage or interval width by estimator and sample size.\", \"weight\": 17.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s02-exec-repetitions\", \"requirements\": \"Reported metrics are aggregated over multiple simulation repetitions or random seeds, with some dispersion reporting such as standard deviation, standard error, confidence interval, or quantiles.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s02-exec-sample-sizes\", \"requirements\": \"The experiment evaluates at least one nontrivial sample size and receives more credit for multiple sample sizes showing finite-sample behavior.\", \"weight\": 5.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s02-exec-uncertainty\", \"requirements\": \"The submission computes uncertainty estimates, such as influence-function standard errors, bootstrap standard errors, or Monte Carlo confidence intervals, sufficient to discuss coverage or reliability.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"s02-paper\", \"requirements\": \"The final paper or report addresses the semiparametric hypotheses with quantitative evidence and a clear causal-inference narrative about finite-dimensional targets and infinite-dimensional nuisance functions.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"s02-result-h1\", \"requirements\": \"The submission evaluates whether the naive difference-in-means estimator is biased under confounded treatment assignment and supports the conclusion using the known true ATE.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s02-result-h2\", \"requirements\": \"The submission compares the doubly robust / orthogonalized estimator against simpler plug-in regression or IPW estimators and states whether DML reduces absolute bias or RMSE under nonlinear nuisance functions.\", \"weight\": 17.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s02-result-h3\", \"requirements\": \"The submission compares cross-fitted and non-cross-fitted versions of the doubly robust estimator, or otherwise clearly analyzes the contribution of cross-fitting to bias, RMSE, or stability.\", \"weight\": 15.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s02-result-h4\", \"requirements\": \"The submission evaluates whether Neyman-orthogonal scores make the ATE estimate less sensitive to nuisance-function estimation error than non-orthogonal plug-in estimators, using weaker versus stronger nuisance learners or another explicit nuisance-quality diagnostic.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s02-result-estimand\", \"requirements\": \"The writeup clearly distinguishes the finite-dimensional causal estimand, infinite-dimensional nuisance functions, orthogonal score or estimator, and evaluation metrics, avoiding confusion between prediction performance and ATE estimation quality.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s02-result-writeup\", \"requirements\": \"The README or writeup describes the data-generating process, estimators, nuisance models, cross-fitting procedure, semiparametric target-vs-nuisance relationship, key numeric results, and per-hypothesis outcomes with appropriate caveats on sample size, simulation repetitions, and model misspecification.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 15, "manifest_file": "tasks/statistics/manifests/S02.yaml", "rubric_file": "tasks/statistics/rubrics/S02.json"} +{"id": "S03", "domain": "statistics", "title": "Reliability of LLM-assisted statistical model selection", "topic": "Reliability of LLM-assisted statistical model selection under assumption violations", "domains": ["statistics", "model-selection", "llm-evaluation"], "arxiv_id": null, "venue": "ARC-Bench Statistics 2026", "metric_key": "primary_metric", "metric_direction": "maximize", "gpu_required": false, "est_wall_clock_sec": 300, "synthesis": "Large language models are increasingly used to assist with statistical data\nanalysis, including model selection, test choice, and interpretation. However,\nstatistical model selection requires matching assumptions to data-generating\nconditions, not merely producing plausible-sounding analysis text. An LLM or\nLLM-like rule-based assistant may recommend inappropriate tests when data are\nskewed, heteroskedastic, non-independent, or affected by outliers.\n\nA credible study of this topic creates a controlled benchmark of small\nstatistical-analysis tasks with known ground truth. The system should compare\nmodel-selection recommendations against an oracle or rule-based reference\nacross simulated datasets. The research question is: *can an LLM-assisted\nstatistical workflow choose appropriate statistical procedures under\nassumption violations, and how often do its recommendations lead to invalid\ninference?*", "num_hypotheses": 3, "hypotheses": "[{\"id\": \"H1\", \"statement\": \"A simple prompt-only LLM-style recommendation system is more likely to choose standard parametric tests even when assumptions are violated.\", \"measurable\": true}, {\"id\": \"H2\", \"statement\": \"Adding computed diagnostic statistics, such as skewness, variance ratio, normality tests, and sample-size information, improves statistical procedure selection accuracy.\", \"measurable\": true}, {\"id\": \"H3\", \"statement\": \"Incorrect procedure selection increases false positive rate or reduces coverage under assumption violations such as heteroskedasticity, skew, or dependence.\", \"measurable\": true}]", "experiment_design": "{\"research_question\": \"Can LLM-assisted statistical model selection reliably choose appropriate procedures when assumptions are violated?\", \"task_type\": {\"name\": \"statistical_test_selection\", \"description\": \"Given dataset summaries and analysis goals, choose an appropriate statistical test or model.\"}, \"conditions\": [{\"name\": \"text_only_recommender\", \"description\": \"Procedure selection using only natural-language task description and variable metadata.\"}, {\"name\": \"diagnostics_augmented_recommender\", \"description\": \"Procedure selection using natural-language description plus computed diagnostics.\"}, {\"name\": \"oracle_rule_based_selector\", \"description\": \"Reference selector using known data-generating process or explicit rules.\"}, {\"name\": \"always_parametric_baseline\", \"description\": \"Baseline that always chooses common parametric procedures such as t-test, ANOVA, or OLS.\"}], \"statistical_tasks\": [{\"name\": \"two_sample_mean_comparison\", \"candidate_methods\": [\"student_t_test\", \"welch_t_test\", \"mann_whitney_u\", \"permutation_test\"]}, {\"name\": \"correlation_analysis\", \"candidate_methods\": [\"pearson_correlation\", \"spearman_correlation\", \"robust_regression\"]}, {\"name\": \"linear_effect_estimation\", \"candidate_methods\": [\"ordinary_least_squares\", \"heteroskedasticity_robust_ols\", \"rank_based_method\", \"permutation_inference\"]}], \"data_generating_conditions\": [{\"name\": \"normal_equal_variance\", \"description\": \"Parametric assumptions approximately hold.\"}, {\"name\": \"normal_unequal_variance\", \"description\": \"Heteroskedastic two-sample setting.\"}, {\"name\": \"skewed_lognormal\", \"description\": \"Strongly skewed outcome distribution.\"}, {\"name\": \"outlier_contaminated\", \"description\": \"Small fraction of extreme observations.\"}, {\"name\": \"nonlinear_monotone_relationship\", \"description\": \"Correlation exists but is nonlinear or rank-based.\"}], \"metrics\": [{\"name\": \"selection_accuracy\", \"direction\": \"maximize\", \"description\": \"Fraction of tasks where selected method matches oracle-acceptable method set.\"}, {\"name\": \"false_positive_rate\", \"direction\": \"target_nominal\", \"description\": \"Empirical Type I error under null simulations.\"}, {\"name\": \"coverage\", \"direction\": \"target_0.95\", \"description\": \"Empirical confidence interval coverage when interval estimates are produced.\"}, {\"name\": \"assumption_violation_detection_rate\", \"direction\": \"maximize\", \"description\": \"Fraction of cases where the system correctly identifies relevant assumption violations.\"}, {\"name\": \"explanation_grounding_score\", \"direction\": \"maximize\", \"description\": \"Whether the written explanation cites the correct diagnostic evidence rather than generic statistical advice.\"}], \"implementation_note\": \"If external LLM API access is unavailable, the submission may simulate an\\nLLM-assisted workflow using templated recommendations, local heuristics, or\\nstored model outputs. The benchmark should focus on statistical validity of\\nrecommendations and downstream inference, not on proprietary model access.\\n\", \"simulation_settings\": {\"sample_sizes\": [30, 100, 500], \"monte_carlo_repetitions\": 500, \"seeds\": [0, 1, 2, 3, 4]}, \"expected_artifacts\": [{\"path\": \"src/generate_tasks.py\", \"description\": \"Code for generating synthetic statistical-analysis tasks.\"}, {\"path\": \"src/evaluate_selectors.py\", \"description\": \"Code for evaluating selectors and downstream inference.\"}, {\"path\": \"results/metrics.json\", \"description\": \"Machine-readable selection accuracy, false positive rate, coverage, and diagnostic-detection metrics.\"}, {\"path\": \"results/selector_decisions.csv\", \"description\": \"Machine-readable selector decisions with task metadata, diagnostics, oracle-acceptable methods, and selected procedures.\"}, {\"path\": \"results/figures/\", \"description\": \"Plots or tables comparing selectors across task types, data-generating conditions, and assumption violations.\"}, {\"path\": \"report/paper.md\", \"description\": \"Paper-style report explaining task generation, selector designs, oracle rules, diagnostic evidence, results, limitations, and per-hypothesis conclusions.\"}, {\"path\": \"README.md\", \"description\": \"Setup and run instructions for reproducing the full benchmark from a clean checkout.\"}], \"dependencies\": {\"python\": [\"numpy\", \"pandas\", \"scipy\", \"scikit-learn\", \"statsmodels\", \"matplotlib\"]}, \"compute_requirements\": {\"gpu_required\": false, \"estimated_wall_clock_sec\": 300}}", "requirements": "[{\"id\": \"req_results_json\", \"type\": \"artifact\", \"description\": \"A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).\", \"must_pass\": true}, {\"id\": \"req_selector_metrics_table\", \"type\": \"artifact\", \"description\": \"A machine-readable metrics artifact (e.g. results/metrics.json) exists reporting selection accuracy and false-positive rate (or Type I error) broken down by selector and data-generating condition, plus a results/selector_decisions.csv with per-task selector decisions and oracle-acceptable method labels.\", \"must_pass\": true}, {\"id\": \"req_selection_accuracy_numeric\", \"type\": \"numeric\", \"description\": \"results.json metrics MUST contain numeric (non-null, finite) selection accuracy values in [0, 1] for at least the text-only recommender and the diagnostics-augmented recommender, evaluated over more than one assumption-violation condition, so H1 and H2 can be evaluated.\", \"must_pass\": true}, {\"id\": \"req_hypotheses_supported_flags\", \"type\": \"discussion\", \"description\": \"results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` boolean AND a `details` string ≥ 40 characters quoting the numerical evidence (specific accuracy / false-positive-rate / coverage values + their source artifact) used to reach the verdict.\", \"must_pass\": true}, {\"id\": \"req_oracle_and_selectors\", \"type\": \"artifact\", \"description\": \"The submission implements or simulates all four selectors — text-only recommender, diagnostics-augmented recommender, oracle / rule-based reference selector, and an always-parametric baseline — and runs the selected procedures to record downstream inference (p-values, coverage, or Type I error).\", \"must_pass\": true}, {\"id\": \"req_method_validity_writeup\", \"type\": \"discussion\", \"description\": \"The report clearly distinguishes the analysis goal, data-generating condition, oracle-acceptable method set, selected method, diagnostic evidence, and downstream inference metric. Nice-to-have, not blocking.\", \"must_pass\": false}, {\"id\": \"req_llm_access_disclosed\", \"type\": \"discussion\", \"description\": \"The report states whether an external LLM API was used or whether a templated / heuristic / stored-output recommender stood in for it. Required for honest reporting but not for scientific correctness.\", \"must_pass\": false}]", "rubric": "{\"id\": \"s03-root\", \"requirements\": \"A credible controlled simulation study evaluating whether LLM-assisted or LLM-like statistical model selection chooses appropriate procedures under assumption violations. The submission should generate statistical-analysis tasks with known reference decisions, compare text-only and diagnostics-augmented recommenders against an oracle or rule-based selector, evaluate downstream inference validity, and connect the numeric findings to the three hypotheses.\", \"judging_note\": \"Score on statistical validity, reproducible task generation, appropriate reference decisions, and honest discussion of downstream inference. Do not require access to an external LLM API; a templated, heuristic, local, or stored-output recommender can receive full credit if it supports the benchmark question. Penalize generic recommendation text that is not tied to diagnostics or data-generating conditions.\", \"weight\": 1, \"sub_tasks\": [{\"id\": \"s03-code\", \"requirements\": \"The code implements a controlled benchmark for statistical procedure selection under several data-generating conditions and selector designs.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"s03-code-task-generator\", \"requirements\": \"The submission generates multiple statistical-analysis tasks, including two-sample mean comparison, correlation analysis, and linear effect estimation, with known null or alternative settings and fixed random seeds.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Dataset and Model Acquisition\"}, {\"id\": \"s03-code-assumption-conditions\", \"requirements\": \"The generated tasks cover meaningful assumption regimes, including approximately normal equal-variance data and at least three violation regimes such as heteroskedasticity, skewness, outliers, nonlinear monotone relationships, or dependence.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Experimental Setup\"}, {\"id\": \"s03-code-selectors\", \"requirements\": \"The submission implements or simulates the required selectors: a text-only recommender, a diagnostics-augmented recommender, an oracle or rule-based reference selector, and a simple parametric baseline.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s03-code-diagnostics\", \"requirements\": \"The diagnostics-augmented condition computes relevant diagnostic information, such as skewness, variance ratio, normality checks, outlier indicators, sample size, monotonicity, or heteroskedasticity evidence, and makes those diagnostics available to the selector.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Method Implementation\"}, {\"id\": \"s03-code-downstream-inference\", \"requirements\": \"The code runs the selected statistical procedures or models and records downstream quantities such as p-values, confidence intervals, Type I error, coverage, or effect estimates where applicable.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Development\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"s03-exec\", \"requirements\": \"Execution produces machine-readable selector and inference metrics adequate to evaluate statistical reliability.\", \"weight\": 2, \"sub_tasks\": [{\"id\": \"s03-exec-metrics\", \"requirements\": \"Execution produces a machine-readable metrics artifact, such as results/metrics.json, containing selection accuracy, false positive rate or Type I error, confidence interval coverage where applicable, assumption-violation detection rate, and explanation grounding by selector and data condition.\", \"weight\": 17.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s03-exec-repetitions\", \"requirements\": \"Reported metrics are aggregated over multiple simulation repetitions, seeds, task instances, or sample sizes, with enough repetition to make selector differences meaningful.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s03-exec-task-coverage\", \"requirements\": \"The executed benchmark includes more than one task type and more than one assumption-violation condition, rather than evaluating a single isolated example.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Evaluation, Metrics & Benchmarking\"}, {\"id\": \"s03-exec-artifacts\", \"requirements\": \"Execution artifacts include selector decisions, oracle-acceptable method labels, diagnostic summaries, and at least one plot or table comparing selectors across assumption regimes.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Code Execution\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}, {\"id\": \"s03-paper\", \"requirements\": \"The final paper or report addresses the three model-selection reliability hypotheses with quantitative evidence and a clear statistical narrative.\", \"weight\": 3, \"sub_tasks\": [{\"id\": \"s03-paper-h1\", \"requirements\": \"The report evaluates whether the text-only recommender over-selects standard parametric procedures under assumption violations and supports the conclusion with selector-decision metrics.\", \"weight\": 12.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s03-paper-h2\", \"requirements\": \"The report compares diagnostics-augmented and text-only selection and states whether computed diagnostics improve procedure selection accuracy or assumption-violation detection.\", \"weight\": 15.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s03-paper-h3\", \"requirements\": \"The report analyzes whether incorrect procedure selection leads to invalid inference, such as inflated false positive rates, poor coverage, or misleading effect estimates under specific violations.\", \"weight\": 15.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s03-paper-method-validity\", \"requirements\": \"The writeup clearly distinguishes the analysis goal, data-generating condition, oracle-acceptable method set, selected method, diagnostic evidence, and downstream inference metric.\", \"weight\": 7.5, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}, {\"id\": \"s03-paper-writeup\", \"requirements\": \"The README or paper describes task generation, selector designs, diagnostic features, oracle rules, key numeric results, limitations, and per-hypothesis outcomes with appropriate caveats on simulation scope and any use or non-use of external LLM APIs.\", \"weight\": 10.0, \"sub_tasks\": [], \"task_category\": \"Result Analysis\", \"finegrained_task_category\": \"Logging, Analysis & Presentation\"}], \"task_category\": null, \"finegrained_task_category\": null}], \"task_category\": null, \"finegrained_task_category\": null}", "rubric_num_leaves": 14, "manifest_file": "tasks/statistics/manifests/S03.yaml", "rubric_file": "tasks/statistics/rubrics/S03.json"} diff --git a/tasks/biology/manifests/B01.yaml b/tasks/biology/manifests/B01.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dca7a7d70c4477cf4f14656162d8fb219a0f0c29 --- /dev/null +++ b/tasks/biology/manifests/B01.yaml @@ -0,0 +1,164 @@ +# ============================================================================= +# B01 — E. coli succinate-production strain optimization via FBA + knockout +# ----------------------------------------------------------------------------- +# First biology topic for ARC-Bench. Tests the full Biology-Agent integration +# end-to-end: domain detection → biology_metabolic profile → biology prompt +# bank → BiologyAgentSandbox at stage 12 (EXPERIMENT_RUN) → COBRApy/BIGG +# pipeline executed inside Claude Code → flux/essentiality artifacts → judged +# against the upgraded rubric (code/exec/results/repro). +# ============================================================================= + +id: B01 +title: "E. coli succinate-production strain optimization via constraint-based knockout screen" +arxiv_id: null +venue: "ARC-Bench Biology 2026" +paper_asset: null + +synthesis: | + Succinate is a top-12 platform chemical and a recurring case study in + metabolic engineering. The standard genome-scale model E. coli iAF1260 + (2382 reactions, 1668 metabolites, 1261 genes) provides a well-validated + testbed for in-silico strain design: from a fixed glucose-minimal medium + one can predict (a) the maximum aerobic biomass growth rate, (b) the + maximum theoretical succinate secretion flux once growth is held at a feasible + fraction of optimum, (c) the set of single- and double-gene knockouts + that decouple biomass from succinate secretion enough to push the strain + toward a high-secretion non-growth-coupled phenotype. + + A credible CPU-scale study of this topic (a) loads iAF1260 from BIGG and + validates aerobic biomass > 0.7 1/h on glucose-minimal medium, (b) runs + parsimonious FBA (pFBA) at the wild-type optimum and reports the central + carbon flux distribution, (c) sweeps the production envelope (biomass vs + succinate secretion) using flux variability analysis (FVA) on the + succinate exchange across a grid of biomass-fraction-of-optimum values, + (d) runs a single-gene knockout screen (≤100 candidate genes drawn from + central carbon metabolism, fermentation, and TCA cycle) and ranks each KO + by post-KO succinate secretion flux at 50% of WT growth, (e) reports the top three + KO strains with mechanistic explanation tied to the network topology. + + The research question is: *which single-gene knockouts of central carbon + metabolism in E. coli iAF1260 produce the largest predicted succinate + secretion flux while preserving at least 50% of wild-type biomass growth, and what + is the mechanistic role of each in shifting flux toward succinate?* + +hypotheses: + - id: H1 + statement: "Wild-type aerobic E. coli iAF1260 on glucose-minimal medium predicts a biomass growth rate within ±10% of 0.736 1/h (the published BIGG iAF1260 reference value)." + measurable: true + - id: H2 + statement: "The biomass-vs-succinate production envelope, computed via FVA at biomass fractions from 0.1 to 1.0 of WT optimum, is monotonically non-increasing in succinate as biomass approaches the WT optimum (i.e., succinate is growth-competing under aerobic glucose conditions)." + measurable: true + - id: H3 + statement: "At least 3 of the top-5 single-gene knockouts identified by the screen as maximizing succinate secretion flux at 50% WT-growth disrupt canonical competing by-product or respiration pathways described in the metabolic-engineering literature (e.g., pyruvate-formate-lyase pflB, lactate dehydrogenase ldhA, alcohol dehydrogenase adhE, acetate kinase ackA, phosphate acetyltransferase pta, or their isozymes), or are accompanied by a mechanistic explanation for why the knockout redirects flux toward succinate under the chosen medium." + measurable: true + +experiment_design: + research_question: "Which single-gene knockouts of central carbon metabolism in E. coli iAF1260 produce the largest predicted succinate secretion flux while preserving at least 50% of wild-type biomass growth, and what is the mechanistic role of each in shifting flux toward succinate?" + conditions: + - name: "wt_aerobic_glucose" + description: "Wild-type iAF1260 on glucose-minimal medium with O2 unconstrained. Reference condition." + - name: "wt_aerobic_glucose_pfba" + description: "Same as wt_aerobic_glucose but with pFBA (parsimonious FBA) for unique flux distribution." + - name: "production_envelope_succinate" + description: "FVA on succinate exchange across biomass-fraction-of-optimum grid {0.1, 0.2, ..., 1.0}." + - name: "single_ko_screen_central_carbon" + description: "Single-gene knockouts on a curated set of ≤100 central-carbon / fermentation / TCA genes (glycolysis, PPP, TCA, anaplerotic, fermentation by-products); for each, FBA at fixed biomass = 0.5 × WT optimum; record succinate exchange flux." + baselines: + - "wt_aerobic_glucose (no perturbation) is the no-engineering baseline" + - "Published BIGG iAF1260 reference growth rate (0.736 1/h on glucose-minimal aerobic) — the model-validity gate" + metrics: + - name: "wt_growth_rate" + direction: "match_reference" + description: "Wild-type biomass exchange flux (1/h) on glucose-minimal aerobic; reference value 0.736." + - name: "max_succinate_flux_mmol_gDW_h_at_zero_growth" + direction: "maximize" + description: "Succinate exchange flux (mmol/gDW/h) at biomass = 0 (theoretical maximum)." + - name: "succinate_flux_mmol_gDW_h_at_half_growth" + direction: "maximize" + description: "Succinate exchange flux at biomass = 0.5 × WT optimum, wild-type strain." + - name: "best_ko_succinate_flux_mmol_gDW_h" + direction: "maximize" + description: "Maximum succinate exchange flux over the single-KO screen at biomass = 0.5 × WT optimum." + datasets: + - name: "iAF1260" + source: "BIGG database (http://bigg.ucsd.edu/models/iAF1260) via cobra.io.load_model('iAF1260')" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 1800 + +rubric_path: "experiments/arc_bench/config/biology/rubrics/B01.json" + +# --------------------------------------------------------------------------- +# Agent-mode requirements (consumed by researchclaw.pipeline.requirements_judge +# at stage 15 RESEARCH_DECISION). Each item: +# id — stable string identifier +# type — advisory: numeric | discussion | artifact (LLM uses freely) +# description — natural-language statement of what the agent must produce +# must_pass — true → unmet ⇒ rerun (1 retry max); false → optional +# +# These are a separate semantic layer from the rubric: rubrics score quality, +# requirements gate proceed-vs-rerun. Keep the must_pass set tight (2–4 items) +# so the agent has an unambiguous bar to clear. +# --------------------------------------------------------------------------- +requirements: + - id: req_results_json + type: artifact + description: >- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metrics (object of numeric keys), + hypotheses (object with h1/h2/h3 entries each carrying a `supported` + boolean), summary (non-empty string). + must_pass: true + + - id: req_wt_growth_value + type: numeric + description: >- + results.json metrics MUST contain a numeric value for + `wt_growth_observed_1_per_h` (or an equivalent wt_growth_*_1_per_h key) + that is non-null and finite. The wild-type growth rate must be reported, + otherwise H1 cannot be evaluated. + must_pass: true + + - id: req_top5_ko + type: discussion + description: >- + results.json structured_results MUST contain a list of the top 5 ranked + single-gene knockouts with at least {gene_id, gene_name, + ko_succinate_flux_mmol_gDW_h, + is_canonical} fields each. The list need not include the textbook + canonical genes — but it MUST exist and identify at least 5 candidates. + must_pass: true + + - id: req_envelope_figure + type: artifact + description: >- + A production-envelope figure (biomass-vs-succinate, or analogous) must + exist under figures/ or analysis/ in PDF or PNG format with axes labeled + and units shown. Either a single multi-curve figure or per-condition + figures both qualify. + must_pass: true + + - id: req_h1_h2_h3_supported_flags + type: discussion + description: >- + Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST + have an explicit `supported` boolean and a `details` string of at least + 40 characters describing the evidence used to reach that verdict. + must_pass: true + + - id: req_mechanistic_writeup + type: discussion + description: >- + Top-3 KO targets are discussed mechanistically — naming the pathway each + KO disables and a one-sentence rationale for why the disruption + redirects flux to succinate. Acceptable inside summary, structured_results, + or a separate writeup field. + must_pass: false # Optional: nice-to-have, not blocking + + - id: req_seed_documented + type: discussion + description: >- + The solver backend (cobra optlang interface name + version) and any RNG + seeds used (e.g. for sampling) are reported in results.json. Required for + reproducibility but not for scientific correctness. + must_pass: false # Optional diff --git a/tasks/biology/manifests/B02.yaml b/tasks/biology/manifests/B02.yaml new file mode 100644 index 0000000000000000000000000000000000000000..83a11bd3adfbd9c6fef4f4a36c8ca8c0a4a52f32 --- /dev/null +++ b/tasks/biology/manifests/B02.yaml @@ -0,0 +1,100 @@ +id: B02 +title: "E. coli acetate overflow metabolism phase map under glucose and oxygen limitation" +arxiv_id: null +venue: "ARC-Bench Biology 2026" +paper_asset: null + +synthesis: | + Overflow acetate secretion in E. coli is a canonical constraint-based + modelling phenotype. A credible CPU-scale study loads a validated E. coli + genome-scale model, fixes a glucose-minimal medium, sweeps glucose and oxygen + uptake bounds, computes growth and major secretion products, and identifies + the transition from respiratory growth to overflow/fermentative by-product + secretion. + + The research question is: under which glucose and oxygen uptake regimes does + E. coli switch from primarily respiratory growth to acetate-secreting overflow + metabolism, and how robust is that regime boundary across FBA and pFBA? + +hypotheses: + - id: H1 + statement: "The selected E. coli BIGG model grows on aerobic glucose-minimal medium with a positive biomass objective and no infeasible solver status." + measurable: true + - id: H2 + statement: "A two-dimensional glucose-vs-oxygen phase plane contains distinct regimes with high oxygen/low acetate and low oxygen/high acetate secretion." + measurable: true + - id: H3 + statement: "pFBA reduces total internal flux while preserving the same growth-rate regime boundaries to within a small tolerance." + measurable: true + +experiment_design: + research_question: "Under which glucose and oxygen uptake regimes does E. coli switch from respiratory growth to acetate overflow?" + conditions: + - name: "aerobic_glucose_reference" + description: "Wild-type E. coli iJO1366 or iAF1260 on glucose-minimal medium with oxygen available." + - name: "glucose_oxygen_phase_plane" + description: "Grid over glucose uptake and oxygen uptake bounds; compute biomass, acetate, ethanol, lactate, succinate, and CO2 exchange fluxes." + - name: "pfba_phase_plane_check" + description: "Run pFBA on the same grid or a representative subset and compare growth plus secretion regimes." + baselines: + - "High-oxygen glucose condition is the respiratory baseline." + - "Zero-oxygen glucose condition is the fermentative baseline." + metrics: + - name: "growth_rate_1_per_h" + direction: "maximize" + description: "Biomass objective flux for each nutrient grid point." + - name: "acetate_flux_mmol_gDW_h" + direction: "characterize" + description: "Acetate exchange secretion flux." + - name: "overflow_boundary_o2_bound" + direction: "estimate" + description: "Approximate oxygen bound below which acetate secretion exceeds a chosen threshold." + - name: "pfba_total_flux_norm" + direction: "minimize" + description: "Total absolute flux under pFBA." + datasets: + - name: "E. coli GSMM" + source: "BIGG iJO1366 or iAF1260 via cobra.io.load_model" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 1800 + +rubric_path: "experiments/arc_bench/config/biology/rubrics/B02.json" + +requirements: + - id: req_results_json + type: artifact + description: "A results.json file exists with primary_metric, metrics, hypotheses, summary, and structured_results." + must_pass: true + - id: req_phase_plane_table + type: artifact + description: "A machine-readable phase-plane table exists with glucose_bound, oxygen_bound, growth_rate, and acetate_flux columns." + must_pass: true + - id: req_phase_plane_figure + type: artifact + description: "A PNG or PDF heatmap/phase-plane figure exists with glucose and oxygen axes and units." + must_pass: true + - id: req_aerobic_ref_growth + type: numeric + description: >- + results.json metrics MUST contain a numeric value for + `aerobic_glucose_growth_rate_1_per_h` (or an equivalent aerobic_growth_*_1_per_h key) + that is non-null, finite, and positive. The aerobic reference growth rate must be + reported, otherwise H1 cannot be evaluated. + must_pass: true + + - id: req_h1_h2_h3_supported_flags + type: discussion + description: > + Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST + have an explicit `supported` boolean and a `details` string of at least + 40 characters describing the evidence used to reach that verdict. + must_pass: true + - id: req_mechanistic_writeup + type: discussion + description: "The overflow boundary and its mechanistic basis are discussed — why E. coli switches to acetate-secreting overflow at the identified nutrient threshold. Acceptable inside summary, phase_plane_interpretation, or a separate writeup field." + must_pass: false # Optional: nice-to-have, not blocking + - id: req_seed_documented + type: discussion + description: "The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness." + must_pass: false # Optional diff --git a/tasks/biology/manifests/B03.yaml b/tasks/biology/manifests/B03.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3482522cf19d5b881ae307982a7ca18c4cd2fef7 --- /dev/null +++ b/tasks/biology/manifests/B03.yaml @@ -0,0 +1,90 @@ +id: B03 +title: "Anaerobic E. coli lactate-overproduction knockout screen" +arxiv_id: null +venue: "ARC-Bench Biology 2026" +paper_asset: null + +synthesis: | + Lactate production under anaerobic glucose conditions is a tractable + metabolic-engineering task for constraint-based modelling. A credible study + loads an E. coli BIGG model, validates anaerobic growth, computes WT lactate + secretion, screens central-carbon and fermentation knockouts, and ranks + interventions that increase lactate secretion while preserving growth. + +hypotheses: + - id: H1 + statement: "The model supports positive anaerobic glucose growth after oxygen uptake is closed." + measurable: true + - id: H2 + statement: "The lactate production envelope shows a tradeoff between biomass growth and maximum lactate secretion." + measurable: true + - id: H3 + statement: "Top lactate-improving knockouts disable competing ethanol, formate, acetate, or succinate by-product routes, or have a specific mechanistic explanation." + measurable: true + +experiment_design: + research_question: "Which single-gene knockouts increase anaerobic lactate secretion in E. coli while preserving at least 30-50% of WT growth?" + conditions: + - name: "wt_anaerobic_glucose" + description: "E. coli iJO1366 or iAF1260 on glucose-minimal medium with oxygen uptake closed." + - name: "lactate_production_envelope" + description: "Sweep biomass fraction and optimize lactate secretion." + - name: "single_ko_screen_fermentation" + description: "Screen central-carbon and fermentation genes; rank by lactate flux at fixed growth fraction." + baselines: + - "Wild-type anaerobic glucose lactate secretion." + - "No-knockout lactate envelope." + metrics: + - name: "anaerobic_wt_growth_1_per_h" + direction: "maximize" + description: "WT biomass flux with oxygen uptake set to zero." + - name: "lactate_flux_mmol_gDW_h" + direction: "maximize" + description: "Lactate exchange secretion flux." + - name: "lactate_yield_per_glucose" + direction: "maximize" + description: "Lactate secretion divided by glucose uptake." + - name: "growth_fraction" + direction: "threshold" + description: "KO biomass relative to WT anaerobic growth." + datasets: + - name: "E. coli GSMM" + source: "BIGG iJO1366 or iAF1260 via cobra.io.load_model" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 1800 + +rubric_path: "experiments/arc_bench/config/biology/rubrics/B03.json" + +requirements: + - id: req_results_json + type: artifact + description: "results.json contains primary_metric, metrics, hypotheses, summary, and top KO structured results." + must_pass: true + - id: req_anaerobic_medium + type: numeric + description: "Oxygen uptake is explicitly closed and anaerobic WT growth is reported." + must_pass: true + - id: req_top5_ko + type: discussion + description: "Top 5 lactate-ranked knockouts are reported with gene_id, gene_name, growth_fraction, lactate_flux, and mechanism/canonical flag." + must_pass: true + - id: req_envelope_figure + type: artifact + description: "A biomass-vs-lactate production envelope figure exists with labelled axes and units." + must_pass: true + - id: req_h1_h2_h3_supported_flags + type: discussion + description: > + Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST + have an explicit `supported` boolean and a `details` string of at least + 40 characters describing the evidence used to reach that verdict. + must_pass: true + - id: req_mechanistic_writeup + type: discussion + description: "Top-3 lactate-improving KO targets are discussed mechanistically — naming the pathway each KO disables and a one-sentence rationale for why the disruption redirects anaerobic flux to lactate. Acceptable inside summary, structured_results, or a separate writeup field." + must_pass: false # Optional: nice-to-have, not blocking + - id: req_seed_documented + type: discussion + description: "The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness." + must_pass: false # Optional diff --git a/tasks/biology/manifests/B04.yaml b/tasks/biology/manifests/B04.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a93b43c2fd7483ebd3c4b44fa1e0ed61ab8b675c --- /dev/null +++ b/tasks/biology/manifests/B04.yaml @@ -0,0 +1,87 @@ +id: B04 +title: "S. cerevisiae ethanol yield under oxygen limitation and carbon-source changes" +arxiv_id: null +venue: "ARC-Bench Biology 2026" +paper_asset: null + +synthesis: | + Yeast ethanol fermentation is a natural extension beyond E. coli while still + staying inside BIGG/COBRApy constraint-based modelling. A credible study + loads iMM904 or a comparable yeast GSMM, validates growth on glucose, sweeps + oxygen availability, compares glucose and alternative carbon sources, and + quantifies ethanol secretion and yield. + +hypotheses: + - id: H1 + statement: "The yeast model grows on glucose-minimal medium and produces a feasible FBA solution." + measurable: true + - id: H2 + statement: "Ethanol secretion/yield increases as oxygen uptake is restricted relative to fully aerobic growth." + measurable: true + - id: H3 + statement: "Carbon-source swaps produce distinct growth and ethanol-yield profiles, with glucose supporting stronger fermentation than at least one alternative carbon source." + measurable: true + +experiment_design: + research_question: "How do oxygen limitation and carbon source alter predicted ethanol secretion and yield in S. cerevisiae?" + conditions: + - name: "yeast_glucose_reference" + description: "S. cerevisiae iMM904 or comparable model on glucose-minimal medium." + - name: "oxygen_sweep" + description: "Sweep oxygen uptake from anaerobic/low oxygen to aerobic while glucose uptake is fixed." + - name: "carbon_source_swap" + description: "Compare glucose, fructose, galactose, glycerol, and acetate when supported by the model." + baselines: + - "Aerobic glucose condition." + - "Anaerobic or oxygen-limited glucose condition." + metrics: + - name: "growth_rate_1_per_h" + direction: "maximize" + description: "Biomass flux." + - name: "ethanol_flux_mmol_gDW_h" + direction: "maximize" + description: "Ethanol exchange secretion flux." + - name: "ethanol_yield_per_carbon_uptake" + direction: "maximize" + description: "Ethanol secretion divided by substrate uptake." + datasets: + - name: "S. cerevisiae iMM904" + source: "BIGG iMM904 via cobra.io.load_model if available, or documented local SBML/JSON yeast model." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 1800 + +rubric_path: "experiments/arc_bench/config/biology/rubrics/B04.json" + +requirements: + - id: req_results_json + type: artifact + description: "results.json contains yeast model ID, metrics, hypotheses, and structured oxygen/carbon-source results." + must_pass: true + - id: req_oxygen_sweep + type: artifact + description: "A machine-readable oxygen-sweep table exists with oxygen bound, growth, ethanol flux, and yield." + must_pass: true + - id: req_carbon_source_table + type: artifact + description: "A carbon-source comparison table exists, including unsupported sources marked explicitly." + must_pass: true + - id: req_figure + type: artifact + description: "At least one oxygen-vs-ethanol or carbon-source yield figure exists with units." + must_pass: true + - id: req_h1_h2_h3_supported_flags + type: discussion + description: > + Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST + have an explicit `supported` boolean and a `details` string of at least + 40 characters describing the evidence used to reach that verdict. + must_pass: true + - id: req_mechanistic_writeup + type: discussion + description: "The ethanol-yield response to oxygen and carbon source is discussed mechanistically — why oxygen restriction increases ethanol yield and how carbon-source metabolism shapes fermentation. Acceptable inside summary, structured_results, or a separate writeup field." + must_pass: false # Optional: nice-to-have, not blocking + - id: req_seed_documented + type: discussion + description: "The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness." + must_pass: false # Optional diff --git a/tasks/biology/manifests/B05.yaml b/tasks/biology/manifests/B05.yaml new file mode 100644 index 0000000000000000000000000000000000000000..818a95acb6cef7ba7e77f137e05cf47e1a90d9d6 --- /dev/null +++ b/tasks/biology/manifests/B05.yaml @@ -0,0 +1,90 @@ +id: B05 +title: "M. tuberculosis condition-specific essentiality and metabolic drug-target prioritisation" +arxiv_id: null +venue: "ARC-Bench Biology 2026" +paper_asset: null + +synthesis: | + Constraint-based essentiality analysis can prioritise metabolic drug-target + hypotheses in pathogen models. A credible study loads an M. tuberculosis GSMM + such as iNJ661 when available, validates biomass production under a documented + medium, performs single-gene and/or single-reaction deletion, and ranks + essential targets by growth impact and subsystem interpretability. + +hypotheses: + - id: H1 + statement: "The selected M. tuberculosis model produces positive biomass under the documented reference medium." + measurable: true + - id: H2 + statement: "Single-gene or single-reaction deletion identifies a non-empty set of essential metabolic targets under the reference condition." + measurable: true + - id: H3 + statement: "Prioritised targets are enriched in interpretable core metabolic subsystems such as cell-wall precursor, cofactor, lipid, energy, or amino-acid metabolism." + measurable: true + +experiment_design: + research_question: "Which condition-specific metabolic genes or reactions are predicted essential in M. tuberculosis and are plausible drug-target hypotheses?" + conditions: + - name: "mtb_reference_medium" + description: "M. tuberculosis iNJ661 or comparable model under a documented reference medium." + - name: "single_gene_deletion" + description: "Single-gene deletion screen when GPR rules are available." + - name: "single_reaction_deletion" + description: "Single-reaction deletion screen as a fallback or complementary target set." + baselines: + - "Wild-type reference growth." + - "Nonessential deletion growth distribution." + metrics: + - name: "wt_growth_rate_1_per_h" + direction: "maximize" + description: "Reference biomass flux." + - name: "growth_fraction_after_deletion" + direction: "minimize" + description: "Deletion biomass divided by WT biomass." + - name: "essential_target_count" + direction: "characterize" + description: "Count of genes/reactions below essentiality threshold." + - name: "subsystem_enrichment" + direction: "characterize" + description: "Subsystem distribution among essential targets." + datasets: + - name: "M. tuberculosis GSMM" + source: "BIGG iNJ661 or documented local SBML/JSON model." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 2400 + +rubric_path: "experiments/arc_bench/config/biology/rubrics/B05.json" + +requirements: + - id: req_results_json + type: artifact + description: "results.json contains model ID, WT growth, essential target counts, top targets, and hypothesis verdicts." + must_pass: true + - id: req_deletion_table + type: artifact + description: "A gene or reaction deletion table exists with target ID, status, growth, growth_fraction, and essential flag." + must_pass: true + - id: req_top_targets + type: discussion + description: "Top 10 prioritised essential targets are reported with subsystem/pathway and rationale." + must_pass: true + - id: req_figure + type: artifact + description: "At least one essentiality distribution or subsystem-enrichment figure exists." + must_pass: true + - id: req_h1_h2_h3_supported_flags + type: discussion + description: > + Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST + have an explicit `supported` boolean and a `details` string of at least + 40 characters describing the evidence used to reach that verdict. + must_pass: true + - id: req_mechanistic_writeup + type: discussion + description: "Top-3 essential target hypotheses are framed mechanistically — naming the metabolic subsystem each target belongs to and why its disruption is predicted lethal under the reference medium. Claims must be framed as computational hypotheses, not validated drugs. Acceptable inside summary, structured_results, or a separate writeup field." + must_pass: false # Optional: nice-to-have, not blocking + - id: req_seed_documented + type: discussion + description: "The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness." + must_pass: false # Optional diff --git a/tasks/biology/manifests/B06.yaml b/tasks/biology/manifests/B06.yaml new file mode 100644 index 0000000000000000000000000000000000000000..554b5e5017b05870d131d152e6cc91a9ac3f4e0b --- /dev/null +++ b/tasks/biology/manifests/B06.yaml @@ -0,0 +1,85 @@ +id: B06 +title: "E. coli carbon-source robustness and condition-dependent essential genes" +arxiv_id: null +venue: "ARC-Bench Biology 2026" +paper_asset: null + +synthesis: | + Carbon-source swaps are a direct mfa-agent capability and produce interpretable + condition-dependent phenotypes. A credible study loads an E. coli GSMM, defines + a base minimal medium, tests multiple single-carbon sources, runs essentiality + analysis on a focused gene set under each feasible source, and identifies genes + whose essentiality changes with carbon source. + +hypotheses: + - id: H1 + statement: "The model predicts positive growth on glucose and at least two additional supported carbon sources." + measurable: true + - id: H2 + statement: "Growth rates and secretion profiles differ substantially across carbon sources." + measurable: true + - id: H3 + statement: "A focused central-carbon gene set contains condition-dependent essential genes whose deletion effects vary by carbon source." + measurable: true + +experiment_design: + research_question: "Which E. coli metabolic vulnerabilities are carbon-source dependent across glucose, glycerol, acetate, succinate, and fructose-like conditions?" + conditions: + - name: "carbon_source_panel" + description: "Close background carbon uptake, open one carbon source at a time, and run FBA/pFBA." + - name: "focused_gene_essentiality_by_carbon" + description: "For feasible carbon sources, delete a focused set of central-carbon genes and record growth fractions." + baselines: + - "Glucose-minimal medium." + - "Wild-type growth for each carbon source." + metrics: + - name: "growth_rate_by_carbon" + direction: "characterize" + description: "WT biomass flux per carbon source." + - name: "major_secretions_by_carbon" + direction: "characterize" + description: "Positive exchange fluxes under pFBA." + - name: "condition_dependent_essential_count" + direction: "maximize" + description: "Genes essential under one source but not another." + datasets: + - name: "E. coli GSMM" + source: "BIGG iJO1366 or iAF1260 via cobra.io.load_model" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 2400 + +rubric_path: "experiments/arc_bench/config/biology/rubrics/B06.json" + +requirements: + - id: req_results_json + type: artifact + description: "results.json contains carbon-source growth table summary, essentiality counts, hypothesis verdicts, and summary." + must_pass: true + - id: req_carbon_table + type: artifact + description: "A carbon-source comparison table exists with source, exchange reaction, status, growth, and secretion profile fields." + must_pass: true + - id: req_essentiality_matrix + type: artifact + description: "A gene-by-carbon essentiality/growth-fraction matrix exists for feasible carbon sources." + must_pass: true + - id: req_heatmap + type: artifact + description: "A heatmap or clustered plot of condition-dependent gene essentiality exists with labelled axes." + must_pass: true + - id: req_h1_h2_h3_supported_flags + type: discussion + description: > + Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST + have an explicit `supported` boolean and a `details` string of at least + 40 characters describing the evidence used to reach that verdict. + must_pass: true + - id: req_mechanistic_writeup + type: discussion + description: "Top-3 condition-dependent essential genes are explained mechanistically — why each gene is essential under one carbon source but not another, referencing the metabolic pathway context. Acceptable inside summary, structured_results, or a separate writeup field." + must_pass: false # Optional: nice-to-have, not blocking + - id: req_seed_documented + type: discussion + description: "The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness." + must_pass: false # Optional diff --git a/tasks/biology/manifests/B07.yaml b/tasks/biology/manifests/B07.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e53f5cdcfae0c57c1623afbe4a2ae5e1033b7275 --- /dev/null +++ b/tasks/biology/manifests/B07.yaml @@ -0,0 +1,92 @@ +id: B07 +title: "Reproducible FBA protocol benchmark: FBA vs pFBA vs loopless/FVA on E. coli" +arxiv_id: null +venue: "ARC-Bench Biology 2026" +paper_asset: null + +synthesis: | + A method benchmark is well matched to mfa-agent because it tests robust use of + COBRApy APIs rather than biological novelty alone. A credible study loads a + validated E. coli GSMM, runs standard FBA, pFBA, loopless FBA when available, + and FVA at a fixed fraction of optimum, then compares growth, flux sparsity, + runtime, and reaction-level variability. + +hypotheses: + - id: H1 + statement: "FBA, pFBA, and loopless FBA preserve the same biomass optimum within solver tolerance under the same medium." + measurable: true + - id: H2 + statement: "pFBA produces a lower total absolute flux norm and/or fewer active reactions than unconstrained FBA." + measurable: true + - id: H3 + statement: "FVA reveals that only a subset of central-carbon reactions are tightly constrained near optimum while others remain variable." + measurable: true + +experiment_design: + research_question: "How do standard FBA, pFBA, loopless FBA, and FVA differ in flux parsimony and variability while preserving E. coli growth predictions?" + conditions: + - name: "standard_fba" + description: "Maximize biomass on aerobic glucose-minimal medium." + - name: "pfba" + description: "Maximize biomass then minimize total absolute flux." + - name: "loopless_fba" + description: "Run loopless solution when available and compare objective/fluxes." + - name: "fva_95_percent_growth" + description: "Run FVA at 95% of optimum and classify rigid vs flexible reactions." + baselines: + - "Standard FBA biomass optimum." + - "COBRApy solver status and runtime." + metrics: + - name: "growth_rate_1_per_h" + direction: "match" + description: "Biomass flux for each method." + - name: "total_abs_flux" + direction: "minimize" + description: "Sum of absolute reaction fluxes." + - name: "active_reaction_count" + direction: "minimize" + description: "Number of reactions with absolute flux above tolerance." + - name: "fva_width" + direction: "characterize" + description: "Maximum minus minimum FVA interval per reaction." + datasets: + - name: "E. coli GSMM" + source: "BIGG iJO1366 or iAF1260 via cobra.io.load_model" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 1800 + +rubric_path: "experiments/arc_bench/config/biology/rubrics/B07.json" + +requirements: + - id: req_results_json + type: artifact + description: "results.json contains method-level metrics, hypotheses, summary, and solver/version metadata." + must_pass: true + - id: req_method_table + type: artifact + description: "A method-comparison table exists with method, status, growth, total_abs_flux, active_reactions, and runtime." + must_pass: true + - id: req_fva_table + type: artifact + description: "An FVA table exists with reaction ID, minimum, maximum, width, and rigid/flexible classification." + must_pass: true + - id: req_figures + type: artifact + description: "At least one labelled figure compares methods or FVA variability." + must_pass: true + - id: req_h1_h2_h3_supported_flags + type: discussion + description: > + Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST + have an explicit `supported` boolean and a `details` string of at least + 40 characters describing the evidence used to reach that verdict. + must_pass: true + - id: req_mechanistic_writeup + type: discussion + description: "The protocol comparison is summarised with a method recommendation — which method is most reproducible and why, referencing flux-norm and FVA evidence. Acceptable inside summary, method_comparison, or a separate writeup field." + must_pass: false # Optional: nice-to-have, not blocking + - id: req_seed_documented + type: discussion + description: "The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness." + must_pass: false # Optional diff --git a/tasks/biology/rubrics/B01.json b/tasks/biology/rubrics/B01.json new file mode 100644 index 0000000000000000000000000000000000000000..127a2f4349e4683f250642dc1a942498693155c3 --- /dev/null +++ b/tasks/biology/rubrics/B01.json @@ -0,0 +1,166 @@ +{ + "id": "B01", + "requirements": "A credible constraint-based experiment on E. coli iAF1260 that (a) loads the BIGG model and validates wild-type growth, (b) computes a flux distribution under the standard objective, (c) characterises the biomass-vs-succinate production envelope, (d) runs a single-gene knockout screen targeted at central carbon metabolism, and (e) ties the top-ranked knockouts to the network mechanism. Partial but well-motivated evidence deserves partial credit; rigid wording or naming should not penalize a substantively correct experiment.", + "judging_note": "Score on biological substance and quantitative correctness, not exact threshold satisfaction. Reproducing canonical FBA numbers (WT growth ~0.7, well-known knockouts like pflB/ldhA/adhE/ackA-pta) is strong evidence; novel-but-coherent design choices are acceptable.", + "weight": 1, + "sub_tasks": [ + { + "id": "b01-code", + "requirements": "The constraint-based pipeline is implemented with COBRApy and a BIGG genome-scale model.", + "weight": 2, + "sub_tasks": [ + { + "id": "b01-code-model", + "requirements": "The submission loads E. coli iAF1260 (or a comparable validated genome-scale metabolic model) from BIGG via COBRApy, with the medium constraints (glucose uptake, O2 availability) explicitly set and the biomass objective explicitly named.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "b01-code-fba", + "requirements": "FBA and pFBA are invoked correctly to obtain wild-type growth and a unique flux distribution; output flux table covers exchange + central-carbon reactions at minimum.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "b01-code-envelope", + "requirements": "The biomass-vs-succinate production envelope is computed by sweeping a fraction-of-optimum biomass constraint and running FVA (or two-step FBA) on the succinate exchange at each grid point.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "b01-code-koscreen", + "requirements": "The single-gene knockout screen iterates over a curated central-carbon / fermentation / TCA gene set (≤100), applies model.genes..knock_out() (or equivalent reaction-bound deletion), and records succinate flux at fixed biomass = 0.5 × WT optimum.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b01-exec", + "requirements": "Execution produces the FBA / FVA / KO numbers needed to evaluate the hypotheses without crashing.", + "weight": 2, + "sub_tasks": [ + { + "id": "b01-exec-validity", + "requirements": "Solver returns optimal status for the wild-type FBA and at least 90% of the KO conditions; no negative biomass or non-physical fluxes (succinate secretion negative under aerobic glucose, etc.) appear without explanation.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b01-exec-physics", + "requirements": "Mass balance, charge balance, and biomass-positive checks pass for the loaded model; this is the biology-validity gate analogous to physics-validity (gauge invariance, unitarity) in the physics rubric.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b01-exec-results", + "requirements": "A machine-readable results artifact (results.json or simulations/*.csv) records WT growth rate, the production-envelope grid (biomass fraction → succinate secretion flux in mmol/gDW/h), and a per-KO succinate-flux table. If yield is reported, define it separately as succinate secretion flux divided by glucose uptake flux.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b01-results", + "requirements": "The results analysis addresses the three hypotheses with quantitative evidence and a clear narrative tied to network mechanism.", + "weight": 3, + "sub_tasks": [ + { + "id": "b01-result-h1-quant", + "requirements": "Quantitative test of H1: predicted WT aerobic growth rate is reported and compared to the published BIGG reference 0.736 1/h. Score 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b01-result-h2-quant", + "requirements": "Quantitative test of H2: production envelope shows succinate is growth-competing — succinate flux is non-increasing as biomass approaches WT optimum. Score 100% if monotonic over ≥8 of 10 grid points, 67% if ≥6 of 10, 33% if ≥4 of 10.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b01-result-h3-quant", + "requirements": "Quantitative test of H3: among the top-5 KO ranked by succinate secretion flux at 0.5 × WT growth, count how many disrupt canonical competing by-product or respiration pathways for succinate-overproduction logic, such as {pflB, ldhA, adhE, ackA, pta, acetate/ethanol/lactate branch competitors, oxygen-respiration competitors}, or include a specific mechanistic explanation for redirecting flux toward succinate under the chosen medium. Score 100% if ≥3 of 5 meet this canonical-or-mechanistic criterion, 67% if 2 of 5, 33% if 1 of 5, 0% otherwise.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b01-result-figure", + "requirements": "At least one publication-quality figure is produced — typically the production envelope (biomass on x, succinate secretion flux on y, with WT and best KO overlaid) and/or the KO essentiality / flux heatmap. Axes labeled with units (1/h, mmol/gDW/h), legend present.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b01-result-writeup", + "requirements": "The README or results writeup discusses each hypothesis outcome, names the top knockouts with their gene/enzyme identity, and gives a one-paragraph mechanistic interpretation (which competing pathways the KOs disable, why this redirects flux to succinate).", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b01-repro", + "requirements": "Reproducibility: the model, run cards, and seeds are checked into artifacts so a fresh clone can rerun and obtain matching numbers.", + "weight": 2, + "sub_tasks": [ + { + "id": "b01-repro-model", + "requirements": "The loaded GSMM is either persisted (models/iAF1260.json) or pinned by version + BIGG URL in the writeup; a downstream user can locate the exact model object used.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b01-repro-runcards", + "requirements": "The medium definition (exchange bounds), objective function, FVA fraction-of-optimum, and KO gene list are saved to a config / params file (not only inline in code).", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b01-repro-seeds", + "requirements": "Solver settings (LP backend name + version, tolerance) are recorded; if any sampling step is used, the RNG seed is logged. A second run reproduces the central WT growth rate to within solver tolerance.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/biology/rubrics/B02.json b/tasks/biology/rubrics/B02.json new file mode 100644 index 0000000000000000000000000000000000000000..4bbf08b320d966185bc8e703c386e63c31af60af --- /dev/null +++ b/tasks/biology/rubrics/B02.json @@ -0,0 +1,166 @@ +{ + "id": "B02", + "requirements": "A credible constraint-based E. coli acetate-overflow phase-plane study that loads a validated BIGG model, sets glucose and oxygen medium bounds, sweeps a two-dimensional nutrient grid, reports growth and by-product secretion, and interprets the acetate overflow boundary.", + "judging_note": "Score biological substance and quantitative reproducibility over exact thresholds. iJO1366, iAF1260, or a comparable curated E. coli GSMM are acceptable.", + "weight": 1, + "sub_tasks": [ + { + "id": "b02-code", + "requirements": "The COBRApy implementation defines the model, medium, phase-plane grid, FBA/pFBA runs, and secretion extraction.", + "weight": 2, + "sub_tasks": [ + { + "id": "b02-code-model", + "requirements": "Loads a validated E. coli BIGG model, explicitly sets glucose and oxygen exchange bounds, and names the biomass objective.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "b02-code-grid", + "requirements": "Constructs a two-dimensional glucose-by-oxygen grid with at least 8 values per axis and restores model state between conditions.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "b02-code-products", + "requirements": "Extracts biomass plus acetate, ethanol, lactate, succinate, CO2, and O2/glucose exchange fluxes where present.", + "weight": 5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "b02-code-pfba", + "requirements": "Runs pFBA on the full grid or a justified representative subset and records total absolute flux.", + "weight": 4, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b02-exec", + "requirements": "Execution produces stable phase-plane artifacts without infeasible or missing-condition failures dominating the study.", + "weight": 2, + "sub_tasks": [ + { + "id": "b02-exec-status", + "requirements": "At least 90% of grid points solve to optimal or biologically expected zero-growth status with explicit handling.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b02-exec-physics", + "requirements": "Mass-balance check passes for the loaded model; no non-physical secretion fluxes appear under aerobic conditions without explicit justification (e.g. acetate secretion is zero under high-oxygen/low-glucose conditions in the reference FBA); biomass flux is positive under aerobic glucose reference. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.", + "weight": 7.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b02-exec-artifacts", + "requirements": "Writes results.json and a CSV/TSV phase-plane table with nutrient bounds, growth, and secretion fluxes.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b02-results", + "requirements": "The analysis identifies and explains acetate-overflow regimes with figures and quantitative criteria.", + "weight": 3, + "sub_tasks": [ + { + "id": "b02-result-h1", + "requirements": "Reports reference aerobic glucose growth and confirms model feasibility and positive biomass production. Score 100% if a positive finite biomass flux is reported for aerobic glucose with optimal solver status; 67% if growth is positive but solver status is not verified; 33% if only feasibility is stated without a numeric growth value; 0% otherwise.", + "weight": 8, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b02-result-h2", + "requirements": "Quantifies an acetate secretion boundary or threshold across oxygen/glucose regimes and distinguishes respiratory vs overflow conditions. Score 100% if an acetate-overflow boundary is quantified (O2 or glucose uptake threshold stated) with ≥2 distinct secretion regimes visible in the phase-plane table; 67% if overflow is identified without a quantified boundary; 33% if only one secretion regime is described; 0% otherwise.", + "weight": 12, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b02-result-h3", + "requirements": "Compares FBA and pFBA growth/secretion regimes and explains where they agree or differ. Score 100% if FBA and pFBA growth rates are numerically compared and total flux norms are reported for both; 67% if only growth rates are compared without flux-norm comparison; 33% if pFBA was run but not compared to FBA; 0% otherwise.", + "weight": 8, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b02-result-figure", + "requirements": "Produces at least one labelled heatmap or contour plot for growth and acetate secretion with units.", + "weight": 8, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b02-result-writeup", + "requirements": "The README or results writeup discusses each hypothesis outcome, characterises the acetate-overflow regime boundary with quantitative support, and gives a one-paragraph mechanistic interpretation of why E. coli shifts from respiratory to acetate-secreting overflow metabolism at the identified nutrient threshold.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b02-repro", + "requirements": "The model ID, medium, grid values, solver, and thresholds are recorded for rerun reproducibility.", + "weight": 2, + "sub_tasks": [ + { + "id": "b02-repro-model", + "requirements": "The loaded E. coli GSMM is either persisted (models/.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object used.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b02-repro-config", + "requirements": "Saves model ID, objective reaction, nutrient grid, and secretion threshold in config or results metadata.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b02-repro-solver", + "requirements": "Records solver backend, tolerance, COBRApy version, and rerun consistency for the aerobic reference point.", + "weight": 5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} \ No newline at end of file diff --git a/tasks/biology/rubrics/B03.json b/tasks/biology/rubrics/B03.json new file mode 100644 index 0000000000000000000000000000000000000000..74afe615911e03e2e1652df04f40ba9543412fda --- /dev/null +++ b/tasks/biology/rubrics/B03.json @@ -0,0 +1,166 @@ +{ + "id": "B03", + "requirements": "A credible anaerobic E. coli lactate-overproduction study using COBRApy that validates anaerobic growth, computes a lactate production envelope, screens a focused central-carbon/fermentation knockout set, and explains top candidates mechanistically.", + "judging_note": "Accept iJO1366, iAF1260, or comparable curated E. coli models. Score coherent anaerobic setup and mechanistic KO interpretation over exact gene names.", + "weight": 1, + "sub_tasks": [ + { + "id": "b03-code", + "requirements": "COBRApy code implements anaerobic medium, lactate objective/envelope, and knockout screening.", + "weight": 2, + "sub_tasks": [ + { + "id": "b03-code-medium", + "requirements": "Loads an E. coli BIGG model, sets glucose uptake, closes oxygen uptake, and names biomass and lactate exchange reactions.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "b03-code-envelope", + "requirements": "Computes biomass-vs-lactate production envelope by constraining biomass fractions and optimizing or FVA-bounding lactate secretion.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "b03-code-ko", + "requirements": "Screens a focused set of no more than 100 central-carbon and fermentation genes with model state restored between knockouts.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "b03-code-yield", + "requirements": "Computes lactate yield as lactate secretion divided by absolute glucose uptake, separate from flux.", + "weight": 4, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b03-exec", + "requirements": "Execution produces valid anaerobic WT, envelope, and KO tables.", + "weight": 2, + "sub_tasks": [ + { + "id": "b03-exec-validity", + "requirements": "WT anaerobic FBA is optimal with positive biomass and at least 85% of KO simulations solve or are explicitly labelled infeasible/lethal.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b03-exec-physics", + "requirements": "Mass-balance check passes for the loaded anaerobic model; no aerobic by-products (O2 uptake or CO2 via respiration) appear under strictly anaerobic conditions without explanation; biomass flux is positive under anaerobic glucose reference. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.", + "weight": 7.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b03-exec-artifacts", + "requirements": "Writes results.json plus machine-readable envelope and KO-ranking tables.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b03-results", + "requirements": "Results quantify lactate production, growth tradeoffs, and KO mechanisms.", + "weight": 3, + "sub_tasks": [ + { + "id": "b03-result-h1", + "requirements": "Reports anaerobic WT growth and confirms oxygen uptake is zero or bounded closed. Score 100% if positive anaerobic WT biomass flux is reported with O2 exchange confirmed ≤0 mmol/gDW/h; 67% if growth is positive but O2 status is not explicitly confirmed; 33% if anaerobic feasibility is stated without a numeric growth value; 0% otherwise.", + "weight": 8, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b03-result-h2", + "requirements": "Shows lactate secretion changes across biomass fractions and identifies whether production is growth-coupled, competing, or partially coupled. Score 100% if lactate secretion flux is non-decreasing as biomass fraction decreases across ≥8 of 10 envelope grid points; 67% if non-decreasing over ≥6 of 10 points; 33% if ≥4 of 10 points; 0% otherwise.", + "weight": 10, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b03-result-h3", + "requirements": "Top 5 KO list includes growth fraction, lactate flux/yield, and pathway-level rationale for at least top 3 targets. Score 100% if ≥3 of the top-5 KOs disrupt canonical competing by-product pathways (acetate, ethanol, formate, or succinate routes) or include a specific mechanistic explanation; 67% if 2 of 5 meet this criterion; 33% if 1 of 5; 0% otherwise.", + "weight": 12, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b03-result-figure", + "requirements": "Produces a labelled lactate envelope or KO ranking figure with units.", + "weight": 8, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b03-result-writeup", + "requirements": "The README or results writeup discusses each hypothesis outcome, names the top lactate-improving knockouts with their gene/enzyme identity, and gives a one-paragraph mechanistic interpretation (which competing pathways the KOs disable, why this redirects anaerobic flux to lactate).", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b03-repro", + "requirements": "Medium, model, gene set, solver, and thresholds are reproducibly recorded.", + "weight": 2, + "sub_tasks": [ + { + "id": "b03-repro-model", + "requirements": "The loaded E. coli GSMM is either persisted (models/.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object and reproduce the anaerobic medium setup.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b03-repro-runcard", + "requirements": "Saves anaerobic medium bounds, biomass threshold, KO gene set, and lactate reaction ID.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b03-repro-solver", + "requirements": "Records COBRApy/solver versions and confirms rerun consistency for WT growth.", + "weight": 5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} \ No newline at end of file diff --git a/tasks/biology/rubrics/B04.json b/tasks/biology/rubrics/B04.json new file mode 100644 index 0000000000000000000000000000000000000000..b37c47ef234c701f4d20a4c99caf3da09c169df6 --- /dev/null +++ b/tasks/biology/rubrics/B04.json @@ -0,0 +1,166 @@ +{ + "id": "B04", + "requirements": "A credible S. cerevisiae ethanol-yield study that loads a yeast GSMM, validates growth, sweeps oxygen uptake, swaps carbon sources, and reports ethanol secretion/yield with interpretable figures.", + "judging_note": "The main challenge is adapting the pipeline beyond E. coli while remaining within COBRApy/BIGG capabilities. Accept iMM904 or another documented yeast GSMM.", + "weight": 1, + "sub_tasks": [ + { + "id": "b04-code", + "requirements": "The code implements yeast model loading, medium setup, oxygen sweep, carbon-source swap, and ethanol-yield calculations.", + "weight": 2, + "sub_tasks": [ + { + "id": "b04-code-model", + "requirements": "Loads iMM904 or comparable yeast model, identifies biomass, glucose, oxygen, and ethanol exchange reactions, and validates positive reference growth.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "b04-code-o2", + "requirements": "Sweeps oxygen uptake across at least 8 values while keeping substrate uptake defined and restoring state between runs.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "b04-code-carbon", + "requirements": "Compares at least 4 carbon sources where model reactions exist and explicitly records unsupported sources.", + "weight": 5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "b04-code-yield", + "requirements": "Calculates ethanol yield using absolute substrate uptake and separates yield from raw secretion flux.", + "weight": 5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b04-exec", + "requirements": "Execution produces oxygen-sweep and carbon-source artifacts without crashing.", + "weight": 2, + "sub_tasks": [ + { + "id": "b04-exec-status", + "requirements": "Reference glucose condition is optimal and at least 80% of supported oxygen/carbon conditions solve or are clearly marked infeasible.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b04-exec-physics", + "requirements": "Mass-balance check passes for the yeast GSMM; no non-physical fluxes appear under aerobic glucose reference (e.g. ethanol secretion should be low under full aerobic conditions); biomass flux is positive under yeast glucose-minimal reference. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.", + "weight": 7.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b04-exec-artifacts", + "requirements": "Writes results.json, oxygen_sweep.csv, and carbon_source_comparison.csv or equivalent structured outputs.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b04-results", + "requirements": "Results quantify oxygen and substrate effects on ethanol secretion/yield.", + "weight": 3, + "sub_tasks": [ + { + "id": "b04-result-h1", + "requirements": "Reports yeast reference growth with model ID and medium definition. Score 100% if positive yeast biomass flux and ethanol secretion are both reported under aerobic glucose-minimal medium with optimal solver status and a named model ID; 67% if growth is positive but ethanol is not reported; 33% if feasibility is stated without numeric values; 0% otherwise.", + "weight": 7, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b04-result-h2", + "requirements": "Quantifies how ethanol flux or yield changes as oxygen decreases and identifies the low-oxygen fermentation regime. Score 100% if ethanol flux or yield is non-decreasing as oxygen uptake decreases across ≥6 of 8 oxygen-sweep points; 67% if non-decreasing over ≥4 of 8 points; 33% if the trend is described without a quantitative grid; 0% otherwise.", + "weight": 12, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b04-result-h3", + "requirements": "Compares growth and ethanol yield across carbon sources with a biological interpretation. Score 100% if at least 3 carbon sources are compared with distinct numeric ethanol yields and a biological interpretation; 67% if 2 sources are compared with numeric differences; 33% if the comparison is qualitative or only one carbon source produces positive growth; 0% otherwise.", + "weight": 10, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b04-result-figure", + "requirements": "Produces labelled figures for oxygen sweep and/or carbon-source yield comparison.", + "weight": 8, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b04-result-writeup", + "requirements": "The README or results writeup discusses each hypothesis outcome, names the oxygen regime and carbon source that maximise ethanol yield, and gives a one-paragraph mechanistic interpretation of why oxygen restriction switches yeast from respiratory growth to fermentative ethanol production.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b04-repro", + "requirements": "Model source, medium, oxygen grid, carbon-source reactions, solver, and versions are documented.", + "weight": 2, + "sub_tasks": [ + { + "id": "b04-repro-model", + "requirements": "The loaded yeast GSMM is either persisted (models/.json) or pinned by BIGG model ID, version, and source URL in the writeup; a downstream user can locate the exact model object used.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b04-repro-config", + "requirements": "Saves reaction IDs, bounds, grid values, and carbon-source list in config or results metadata.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b04-repro-version", + "requirements": "Records COBRApy, solver, model source/version, and rerun consistency for reference growth.", + "weight": 5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} \ No newline at end of file diff --git a/tasks/biology/rubrics/B05.json b/tasks/biology/rubrics/B05.json new file mode 100644 index 0000000000000000000000000000000000000000..3d6fb32edfa47ecb73ae2960b5956a5aa344b4b1 --- /dev/null +++ b/tasks/biology/rubrics/B05.json @@ -0,0 +1,166 @@ +{ + "id": "B05", + "requirements": "A credible M. tuberculosis essentiality study using COBRApy that validates a pathogen GSMM, runs single-gene and/or single-reaction deletions, identifies essential targets, and prioritises interpretable drug-target hypotheses.", + "judging_note": "Accept gene deletions, reaction deletions, or both depending on model GPR support. Penalize unsupported biological claims more than absence of external drug databases.", + "weight": 1, + "sub_tasks": [ + { + "id": "b05-code", + "requirements": "The code loads the pathogen model, validates medium, runs deletion screens, and annotates essential targets.", + "weight": 2, + "sub_tasks": [ + { + "id": "b05-code-model", + "requirements": "Loads iNJ661 or a documented M. tuberculosis GSMM, defines medium/objective, and validates positive WT biomass.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "b05-code-deletion", + "requirements": "Runs single-gene deletion when GPR rules are usable, or single-reaction deletion as an explicit fallback/complement.", + "weight": 7, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "b05-code-threshold", + "requirements": "Defines essentiality threshold such as growth <5% or <10% of WT and applies it consistently.", + "weight": 5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b05-code-annotation", + "requirements": "Extracts subsystem, reaction name, gene name, GPR, or other model-native annotations for top targets.", + "weight": 5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b05-exec", + "requirements": "Deletion runs complete with interpretable status handling and structured outputs.", + "weight": 2, + "sub_tasks": [ + { + "id": "b05-exec-status", + "requirements": "WT reference condition is optimal and at least 85% of deletion simulations solve or are explicitly marked infeasible/lethal.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b05-exec-physics", + "requirements": "Mass-balance check passes for the loaded pathogen GSMM; WT biomass is positive under the documented medium; deletion table contains no negative growth values; model GPR rules produce logically consistent essential gene predictions. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.", + "weight": 7.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b05-exec-artifacts", + "requirements": "Writes results.json plus deletion table with growth, growth_fraction, status, and essential flag.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b05-results", + "requirements": "Results identify essential targets and provide conservative biological interpretation.", + "weight": 3, + "sub_tasks": [ + { + "id": "b05-result-h1", + "requirements": "Reports WT growth, medium, model ID, and validation caveats. Score 100% if positive WT biomass flux is reported under the documented medium with optimal solver status and model ID named; 67% if growth is positive but medium is not explicitly documented; 33% if feasibility is stated without a numeric value; 0% otherwise.", + "weight": 7, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b05-result-h2", + "requirements": "Reports essential target count and distribution of growth fractions after deletion. Score 100% if ≥5 essential genes/reactions are identified with growth fractions below the stated threshold and the deletion table covers ≥50 candidates; 67% if essential targets are identified but the candidate set covers fewer than 50 entries; 33% if essential targets are named without a deletion table; 0% otherwise.", + "weight": 10, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b05-result-h3", + "requirements": "Top 10 targets include subsystem/pathway and one-sentence rationale; claims are framed as modelling hypotheses, not validated drugs. Score 100% if ≥3 of the top-10 prioritised targets belong to named core metabolic subsystems (cell-wall precursor, cofactor, lipid, energy, or amino-acid biosynthesis) with at least a one-sentence pathway rationale; 67% if 2 of 10 with rationale; 33% if 1 of 10 with rationale; 0% otherwise.", + "weight": 12, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b05-result-figure", + "requirements": "Produces a labelled essentiality histogram, target ranking plot, or subsystem enrichment chart.", + "weight": 8, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b05-result-writeup", + "requirements": "The README or results writeup discusses each hypothesis outcome, names the top prioritised essential targets with subsystem/enzyme identity, and gives a one-paragraph mechanistic rationale for why each target's disruption is lethal. Claims are explicitly framed as computational hypotheses requiring wet-lab validation.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b05-repro", + "requirements": "Model, medium, deletion type, threshold, solver, and annotations are reproducibly recorded.", + "weight": 2, + "sub_tasks": [ + { + "id": "b05-repro-model", + "requirements": "The loaded M. tuberculosis GSMM is either persisted (models/.json) or pinned by BIGG model ID, version, or local file hash in the writeup; a downstream user can locate the exact model object used.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b05-repro-config", + "requirements": "Saves model source, medium bounds, objective, deletion mode, and essentiality threshold.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b05-repro-version", + "requirements": "Records COBRApy/solver versions and confirms rerun consistency for WT growth and essential count.", + "weight": 5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} \ No newline at end of file diff --git a/tasks/biology/rubrics/B06.json b/tasks/biology/rubrics/B06.json new file mode 100644 index 0000000000000000000000000000000000000000..1ddcfc2cb7f82d22682d7d395e3f712362000ee9 --- /dev/null +++ b/tasks/biology/rubrics/B06.json @@ -0,0 +1,166 @@ +{ + "id": "B06", + "requirements": "A credible E. coli carbon-source robustness study that loads a validated GSMM, swaps carbon sources, reports growth/secretion profiles, and identifies condition-dependent essential genes from a focused gene set.", + "judging_note": "Score explicit medium handling and condition-dependent interpretation. It is acceptable if some carbon sources are unsupported, provided they are recorded and not silently dropped.", + "weight": 1, + "sub_tasks": [ + { + "id": "b06-code", + "requirements": "The code implements carbon-source medium swaps, FBA/pFBA, and focused essentiality comparison.", + "weight": 2, + "sub_tasks": [ + { + "id": "b06-code-model", + "requirements": "Loads E. coli BIGG model, identifies biomass and candidate carbon exchange reactions, and defines a minimal medium policy.", + "weight": 7, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "b06-code-swap", + "requirements": "Closes background carbon uptake and opens one carbon source at a time with documented uptake bounds.", + "weight": 7, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "b06-code-secretion", + "requirements": "Runs FBA/pFBA per source and records major positive exchange fluxes.", + "weight": 5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "b06-code-essentiality", + "requirements": "Runs focused single-gene deletions for feasible carbon sources and computes growth fractions relative to each source-specific WT.", + "weight": 7, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b06-exec", + "requirements": "Execution produces carbon-source and essentiality artifacts.", + "weight": 2, + "sub_tasks": [ + { + "id": "b06-exec-validity", + "requirements": "Glucose reference is optimal and at least two non-glucose sources are either feasible or clearly marked unsupported/infeasible.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b06-exec-physics", + "requirements": "Mass-balance check passes for the E. coli GSMM under each tested carbon source; no carbon source produces negative biomass or non-physical secretion without explicit handling; model state is restored between carbon-source conditions. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.", + "weight": 7.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b06-exec-artifacts", + "requirements": "Writes results.json, carbon-source table, and gene-by-condition growth-fraction matrix.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b06-results", + "requirements": "Results compare carbon sources and identify condition-dependent vulnerabilities.", + "weight": 3, + "sub_tasks": [ + { + "id": "b06-result-h1", + "requirements": "Reports feasible carbon-source count and growth rates, including unsupported-source handling. Score 100% if ≥3 carbon sources including glucose support positive biomass growth with explicit exchange bounds documented; 67% if 2 sources support positive growth; 33% if only glucose is feasible with explicit confirmation that other sources are infeasible or unsupported; 0% otherwise.", + "weight": 8, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b06-result-h2", + "requirements": "Compares secretion profiles and growth rates across sources with quantitative differences. Score 100% if growth rates differ by >10% between at least 2 feasible carbon sources AND major secretion fluxes differ qualitatively across sources; 67% if growth rate differences are reported without secretion-profile comparison; 33% if differences are noted qualitatively without quantification; 0% otherwise.", + "weight": 9, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b06-result-h3", + "requirements": "Identifies genes with large growth-fraction changes across carbon sources and explains pathway context for top hits. Score 100% if ≥3 genes with condition-dependent essentiality changes (essential under one carbon source but not another) are identified with pathway context; 67% if 2 genes are identified; 33% if 1 gene is identified with pathway context; 0% otherwise.", + "weight": 12, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b06-result-figure", + "requirements": "Produces labelled bar plots and/or heatmaps for growth, secretion, and condition-dependent essentiality.", + "weight": 8, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b06-result-writeup", + "requirements": "The README or results writeup discusses each hypothesis outcome, names supported and unsupported carbon sources, identifies condition-dependent essential genes, and gives a one-paragraph mechanistic interpretation linking carbon-source specific metabolism to the observed vulnerability changes.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b06-repro", + "requirements": "Carbon-source definitions, gene set, thresholds, solver, and model are documented.", + "weight": 2, + "sub_tasks": [ + { + "id": "b06-repro-model", + "requirements": "The loaded E. coli GSMM is either persisted (models/.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object and reproduce each carbon-source medium setup.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b06-repro-runcard", + "requirements": "Saves carbon exchange IDs, uptake bounds, oxygen setting, gene list, and essentiality threshold.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b06-repro-version", + "requirements": "Records COBRApy/solver versions and rerun consistency for glucose and at least one non-glucose source.", + "weight": 5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} \ No newline at end of file diff --git a/tasks/biology/rubrics/B07.json b/tasks/biology/rubrics/B07.json new file mode 100644 index 0000000000000000000000000000000000000000..b01d62bab9f0d5e4bfa4debb4386e81e1266a833 --- /dev/null +++ b/tasks/biology/rubrics/B07.json @@ -0,0 +1,166 @@ +{ + "id": "B07", + "requirements": "A reproducible FBA-method benchmark on E. coli that compares standard FBA, pFBA, loopless FBA when available, and FVA near optimum using consistent medium, metrics, runtime logging, and interpretable figures.", + "judging_note": "This is a protocol-quality task. Reward robust implementation, structured outputs, and careful handling of unavailable loopless solvers over biological novelty.", + "weight": 1, + "sub_tasks": [ + { + "id": "b07-code", + "requirements": "The code implements method comparison with consistent model state and metrics.", + "weight": 2, + "sub_tasks": [ + { + "id": "b07-code-model", + "requirements": "Loads E. coli BIGG model, sets medium and biomass objective, and validates reference FBA.", + "weight": 7, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "b07-code-methods", + "requirements": "Runs standard FBA, pFBA, and loopless FBA or records a justified loopless-unavailable status.", + "weight": 7, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "b07-code-fva", + "requirements": "Runs FVA at a documented fraction of optimum and computes interval widths and rigid/flexible labels.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "b07-code-metrics", + "requirements": "Computes total absolute flux, active reaction count, objective value, solver status, and runtime for each method.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b07-exec", + "requirements": "Execution produces complete benchmark artifacts and handles optional method failures explicitly.", + "weight": 2, + "sub_tasks": [ + { + "id": "b07-exec-status", + "requirements": "Standard FBA and pFBA solve optimally; loopless and FVA either solve or have explicit, non-crashing failure records.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b07-exec-physics", + "requirements": "Mass-balance check passes for the loaded model under the benchmark medium; FBA, pFBA, and loopless FBA all return non-negative biomass fluxes; no negative flux norms or ill-conditioned solver status appear without explicit logging. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.", + "weight": 7.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b07-exec-artifacts", + "requirements": "Writes results.json, method comparison table, FVA table, and method flux tables.", + "weight": 8, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b07-results", + "requirements": "Results compare objective preservation, parsimony, and reaction variability.", + "weight": 3, + "sub_tasks": [ + { + "id": "b07-result-h1", + "requirements": "Reports objective differences among methods and checks agreement within solver tolerance where methods succeed. Score 100% if FBA and pFBA biomass objectives agree within 1% relative error and any available loopless FBA also agrees within 1%; 67% if FBA and pFBA agree but loopless comparison is missing or failed with explicit explanation; 33% if only qualitative agreement is stated; 0% otherwise.", + "weight": 9, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "b07-result-h2", + "requirements": "Quantifies pFBA parsimony using total flux norm and active reaction count compared with standard FBA. Score 100% if pFBA total absolute flux norm is numerically lower than FBA AND active reaction count is lower under pFBA; 67% if flux norm is lower but active reaction count is not compared; 33% if pFBA parsimony is stated qualitatively without numeric comparison; 0% otherwise.", + "weight": 10, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b07-result-h3", + "requirements": "Summarizes FVA width distribution and names central-carbon reactions that are rigid or flexible near optimum. Score 100% if FVA interval widths are reported for all model reactions and ≥5 central-carbon reactions are classified as rigid (interval width < 1 mmol/gDW/h) with subsystem identification; 67% if rigid/flexible classification is reported without central-carbon specifics; 33% if FVA results are reported without classification; 0% otherwise.", + "weight": 10, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b07-result-figure", + "requirements": "Produces labelled method-comparison and/or FVA-width figures with units and clear legends.", + "weight": 8, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "b07-result-writeup", + "requirements": "The README or results writeup discusses each hypothesis outcome, summarises the method-level comparison (parsimony, variability, runtime), and gives a one-paragraph protocol recommendation stating which method is most suitable for reproducible phenotype prediction and why.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "b07-repro", + "requirements": "The benchmark is reproducible with pinned model, medium, tolerance, solver, and code path.", + "weight": 2, + "sub_tasks": [ + { + "id": "b07-repro-model", + "requirements": "The loaded E. coli GSMM is either persisted (models/.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object and reproduce the benchmark medium.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b07-repro-config", + "requirements": "Saves model ID, objective, medium bounds, FVA fraction, active-flux tolerance, and method list.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "b07-repro-version", + "requirements": "Records COBRApy, optlang, solver backend/version, and runtime environment.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} \ No newline at end of file diff --git a/tasks/biology/topics.yaml b/tasks/biology/topics.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1ddc6ec8c3a01cef6da2c471058f2762cea0f1b8 --- /dev/null +++ b/tasks/biology/topics.yaml @@ -0,0 +1,99 @@ +# ============================================================================= +# ARC-Bench Biology — constraint-based metabolic modelling topics +# ----------------------------------------------------------------------------- +# All biology topics drive the Biology-Agent integration: +# experiment.mode = biology_agent → BiologyAgentSandbox → Claude Code +# with /home/shiqiu/Biology-Agent skills + agents installed into ~/.claude/. +# ============================================================================= + +topics: + - id: B01 + topic: "E. coli succinate-production strain optimization via constraint-based knockout screen on iAF1260" + domains: ["systems-biology", "metabolic-engineering", "constraint-based-modelling"] + metric_key: "primary_metric" + metric_direction: "maximize" + bigg_model: "iAF1260" + source_skills: + - "Biology-Agent/skills/gsmm-builder" + - "Biology-Agent/skills/gsmm-validator" + - "Biology-Agent/skills/fba-simulator" + - "Biology-Agent/skills/flux-analyzer" + - "Biology-Agent/skills/mfa-pipeline-orchestrator" + + - id: B02 + topic: "E. coli acetate overflow metabolism phase map under glucose and oxygen limitation" + domains: ["systems-biology", "constraint-based-modelling", "phase-plane-analysis"] + metric_key: "primary_metric" + metric_direction: "maximize" + bigg_model: "iJO1366" + source_skills: + - "Biology-Agent/skills/gsmm-builder" + - "Biology-Agent/skills/gsmm-validator" + - "Biology-Agent/skills/fba-simulator" + - "Biology-Agent/skills/flux-analyzer" + - "Biology-Agent/skills/mfa-pipeline-orchestrator" + + - id: B03 + topic: "Anaerobic E. coli lactate-overproduction knockout screen" + domains: ["systems-biology", "metabolic-engineering", "constraint-based-modelling"] + metric_key: "primary_metric" + metric_direction: "maximize" + bigg_model: "iJO1366" + source_skills: + - "Biology-Agent/skills/gsmm-builder" + - "Biology-Agent/skills/gsmm-validator" + - "Biology-Agent/skills/fba-simulator" + - "Biology-Agent/skills/flux-analyzer" + - "Biology-Agent/skills/mfa-pipeline-orchestrator" + + - id: B04 + topic: "S. cerevisiae ethanol yield under oxygen limitation and carbon-source changes" + domains: ["systems-biology", "metabolic-engineering", "constraint-based-modelling"] + metric_key: "primary_metric" + metric_direction: "maximize" + bigg_model: "iMM904" + source_skills: + - "Biology-Agent/skills/gsmm-builder" + - "Biology-Agent/skills/gsmm-validator" + - "Biology-Agent/skills/fba-simulator" + - "Biology-Agent/skills/flux-analyzer" + - "Biology-Agent/skills/mfa-pipeline-orchestrator" + + - id: B05 + topic: "M. tuberculosis condition-specific essentiality and metabolic drug-target prioritisation" + domains: ["systems-biology", "drug-target-prioritisation", "constraint-based-modelling"] + metric_key: "primary_metric" + metric_direction: "maximize" + bigg_model: "iNJ661" + source_skills: + - "Biology-Agent/skills/gsmm-builder" + - "Biology-Agent/skills/gsmm-validator" + - "Biology-Agent/skills/fba-simulator" + - "Biology-Agent/skills/flux-analyzer" + - "Biology-Agent/skills/mfa-pipeline-orchestrator" + + - id: B06 + topic: "E. coli carbon-source robustness and condition-dependent essential genes" + domains: ["systems-biology", "essentiality-analysis", "constraint-based-modelling"] + metric_key: "primary_metric" + metric_direction: "maximize" + bigg_model: "iJO1366" + source_skills: + - "Biology-Agent/skills/gsmm-builder" + - "Biology-Agent/skills/gsmm-validator" + - "Biology-Agent/skills/fba-simulator" + - "Biology-Agent/skills/flux-analyzer" + - "Biology-Agent/skills/mfa-pipeline-orchestrator" + + - id: B07 + topic: "Reproducible FBA protocol benchmark: FBA vs pFBA vs loopless/FVA on E. coli" + domains: ["systems-biology", "method-benchmarking", "constraint-based-modelling"] + metric_key: "primary_metric" + metric_direction: "maximize" + bigg_model: "iJO1366" + source_skills: + - "Biology-Agent/skills/gsmm-builder" + - "Biology-Agent/skills/gsmm-validator" + - "Biology-Agent/skills/fba-simulator" + - "Biology-Agent/skills/flux-analyzer" + - "Biology-Agent/skills/mfa-pipeline-orchestrator" diff --git a/tasks/meta_paper_quality.json b/tasks/meta_paper_quality.json new file mode 100644 index 0000000000000000000000000000000000000000..6d6103205c16a80c1dceefabca85ef82910704c7 --- /dev/null +++ b/tasks/meta_paper_quality.json @@ -0,0 +1,211 @@ +{ + "id": "meta-paper-quality-root", + "requirements": "Paper-quality meta-rubric applied to ALL ARC-Bench topics (ML / P / B). Grades the final deliverables — paper_final.md, charts/*, code/*, submission/README.md — for paper writing quality, code orchestration, visual layout, and content accuracy. This rubric is SEPARATE from the per-topic science rubric and is graded MANUALLY by a Claude Code agent invoked via experiments/arc_bench/scripts/judge_paper_manual.sh. The bench pipeline does NOT auto-run this gate. The combined overall score = (per-topic-science × 100 + meta-paper-quality × 86) / (100 + 86) ≈ 54% science + 46% meta.", + "judging_note": "Score on substance, not surface polish. A 750-word abstract with shallow content is worse than a 200-word abstract with a clear claim+method+result. Penalize fabrication (numbers in writeup not in artifacts) heavily, hedge claims to match evidence, and credit modular code even if it could be shorter. For figures, the test is communicative function: can a reader understand the message from the figure alone? When ambiguous, assign partial credit (0.33 / 0.5 / 0.67) rather than forcing binary 0/1.", + "weight": 1, + "scoring_target": "deliverables", + "input_artifacts": [ + "stage-22/paper_final.md", + "stage-19/paper_revised.md", + "stage-17/paper_draft.md", + "deliverables/paper_final.md", + "deliverables/charts/", + "deliverables/code/", + "submission/README.md", + "submission/code/", + "stage-14/charts/", + "stage-14/experiment_summary.json" + ], + "sub_tasks": [ + { + "id": "meta-paper-content", + "requirements": "Paper writing quality across the IMRAD structure (Introduction / Methods / Results / Discussion). Grades clarity, structure, faithfulness to results, and academic conventions — NOT surface polish or word count.", + "weight": 5, + "sub_tasks": [ + { + "id": "mpq-abstract", + "requirements": "Abstract captures the four required moves: (1) what problem/question, (2) what method/approach was taken, (3) what was found numerically, (4) what the contribution is. Length 150-300 words. NO numbers should appear in the abstract that don't appear in the results section. Subscore 1.0 if all four moves present and grounded; 0.67 if 3 of 4; 0.33 if 2 of 4; 0 otherwise.", + "weight": 4, + "sub_tasks": [], + "task_category": "Paper Quality", + "finegrained_task_category": "Writing - Abstract" + }, + { + "id": "mpq-introduction", + "requirements": "Introduction situates the work: prior approaches, the specific gap addressed, and the contribution. Avoids 'in this paper we' boilerplate without substance. Cites real prior work (≥3 citations) relevant to the topic. Subscore on coherence of the motivation chain — can a reader who knows the field tell what's new vs prior art?", + "weight": 4, + "sub_tasks": [], + "task_category": "Paper Quality", + "finegrained_task_category": "Writing - Introduction" + }, + { + "id": "mpq-method-clarity", + "requirements": "Methods section enables independent reproduction by someone with domain knowledge but no prior context. Names: (a) datasets / models used with versions or sources, (b) algorithms / settings / hyperparameters in numbers, (c) evaluation protocol (seeds, splits, metrics). Acceptable: equations, pseudocode, or precise prose. Unacceptable: 'we used standard techniques' without specifics.", + "weight": 6, + "sub_tasks": [], + "task_category": "Paper Quality", + "finegrained_task_category": "Writing - Methods" + }, + { + "id": "mpq-results-grounded", + "requirements": "Every numeric claim in the Results section is traceable to a value in the artifacts (experiment_summary.json, results.json, CSV outputs). No invented numbers. Comparisons (X > Y, A improves over B) are backed by stated differences and uncertainty. Tables and figures are referenced from the prose. Subscore: count the numeric claims, mark how many are traceable; subscore = traceable / total.", + "weight": 8, + "sub_tasks": [], + "task_category": "Paper Quality", + "finegrained_task_category": "Writing - Results" + }, + { + "id": "mpq-discussion", + "requirements": "Discussion section addresses: (a) what the results imply / mechanistic interpretation, (b) at least one threat to validity or limitation honestly stated (small N, seed variance, dataset bias, methodological caveat), (c) suggested follow-up work. Penalize 'no limitations' or generic 'future work' boilerplate.", + "weight": 4, + "sub_tasks": [], + "task_category": "Paper Quality", + "finegrained_task_category": "Writing - Discussion" + }, + { + "id": "mpq-citations", + "requirements": "Citations are valid (real DOIs / arXiv IDs / venue names) and formatted consistently. Verify against references.bib: every cited key resolves, no hallucinated authors or titles. Sample 3-5 citations and check existence (a quick web lookup or arXiv ID validation). Bibliography format is internally consistent (all entries follow the same style).", + "weight": 4, + "sub_tasks": [], + "task_category": "Paper Quality", + "finegrained_task_category": "Writing - References" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "meta-code-orchestration", + "requirements": "Code orchestration quality of the experiment package. Looks at stage-22/code/ or submission/code/ — how the actual experimental code is organized, documented, and made reusable. Distinct from the science rubric's code leaves (which check whether the method is implemented at all).", + "weight": 3, + "sub_tasks": [ + { + "id": "mco-modular", + "requirements": "Code is split into logical modules with clear responsibilities (data loading, model definition, training/inference, evaluation, plotting), NOT a single monolithic main.py. Each module is <500 lines. Imports are explicit (no `from X import *`). Subscore: 1.0 if cleanly split into ≥3 named modules; 0.67 if 2 modules; 0.33 if monolithic but reasonable internal structure; 0 if spaghetti.", + "weight": 5, + "sub_tasks": [], + "task_category": "Code Orchestration", + "finegrained_task_category": "Code Quality - Modularity" + }, + { + "id": "mco-reproducible", + "requirements": "A reader could run the experiment from the code package alone. Means: (a) a README or main.py docstring explains the entry point, (b) dependencies are listed (requirements.txt, setup.py, or imports clearly inferable), (c) random seeds are settable / documented, (d) data sources are specified (sklearn datasets, BIGG model id, etc.). NO hardcoded absolute paths to the original author's machine.", + "weight": 6, + "sub_tasks": [], + "task_category": "Code Orchestration", + "finegrained_task_category": "Code Quality - Reproducibility" + }, + { + "id": "mco-readable", + "requirements": "Identifier names communicate intent (no `x1`, `tmp`, `foo`). Function/class docstrings explain purpose for non-trivial code. Public API has type hints for at least the function signatures. Limited inline comments (only WHY when non-obvious), NOT line-by-line narration. Mix of these signals → 0.0 to 1.0.", + "weight": 4, + "sub_tasks": [], + "task_category": "Code Orchestration", + "finegrained_task_category": "Code Quality - Readability" + }, + { + "id": "mco-no-dead-code", + "requirements": "No commented-out code blocks (more than 3 contiguous commented lines). No unused imports. No functions/classes defined but never called. No TODO/FIXME markers without ownership. Penalize 'kitchen-sink' files that import and define dozens of things to use only a few.", + "weight": 3, + "sub_tasks": [], + "task_category": "Code Orchestration", + "finegrained_task_category": "Code Quality - Hygiene" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "meta-visual-layout", + "requirements": "Figure quality of the deliverable charts (stage-22/deliverables/charts/, submission/code/*.png|*.pdf). Grades whether each figure communicates its message effectively to a reader who has not read the prose.", + "weight": 3, + "sub_tasks": [ + { + "id": "mvl-axes-labeled", + "requirements": "Every figure has both axes labeled with quantity + unit (e.g., 'Growth rate (1/h)', 'Cross section (pb)', 'Accuracy (%)'). Tick labels are readable at the figure's printed size. Subscore: fraction of figures (in the deliverable charts dir) that pass.", + "weight": 4, + "sub_tasks": [], + "task_category": "Visual Layout", + "finegrained_task_category": "Figure - Axes" + }, + { + "id": "mvl-legend-present", + "requirements": "Multi-series figures (≥2 lines, bars, or categories) have a legend that identifies each series. Single-series figures do NOT need a legend. Legend placement does not occlude data. Color choices distinguishable by colorblind readers (avoid pure red+green together without a marker shape difference).", + "weight": 3, + "sub_tasks": [], + "task_category": "Visual Layout", + "finegrained_task_category": "Figure - Legend" + }, + { + "id": "mvl-caption-quality", + "requirements": "Each figure has a caption that (a) names what is plotted, (b) names the dataset / condition / model used, (c) states the key takeaway in one sentence. A caption that just says 'Figure 1: results' is insufficient. Captions are in the paper text near the figure reference.", + "weight": 4, + "sub_tasks": [], + "task_category": "Visual Layout", + "finegrained_task_category": "Figure - Caption" + }, + { + "id": "mvl-figure-relevance", + "requirements": "Each figure earns its place — it shows information that supports a claim in the paper. No decorative figures, no figures duplicated across stages (the same chart appearing twice from different stage outputs), no figures showing trivially obvious results without value. Subscore on the fraction of figures that pass this 'earned place' test.", + "weight": 4, + "sub_tasks": [], + "task_category": "Visual Layout", + "finegrained_task_category": "Figure - Relevance" + }, + { + "id": "mvl-color-accessibility", + "requirements": "Color palette is colorblind-safe (avoid red+green only); important distinctions are reinforced by shape, line style, or hatching in addition to color. Background does not interfere with foreground (no dark text on dark background). Resolution is at least 150 DPI for raster figures.", + "weight": 2, + "sub_tasks": [], + "task_category": "Visual Layout", + "finegrained_task_category": "Figure - Accessibility" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "meta-content-accuracy", + "requirements": "Faithfulness of the writeup to the underlying artifacts. The single biggest fabrication risk in autonomous paper generation is invented numbers; this bucket carries the largest weight.", + "weight": 4, + "sub_tasks": [ + { + "id": "mca-no-fabrication", + "requirements": "STRICT: every numeric value in the paper (abstract, methods, results, tables) must trace to a value in the artifacts (results.json, experiment_summary.json, CSV outputs, figure data). Sample 5-10 numbers across the paper; mark each as traceable / not-traceable; subscore = traceable_count / sampled_count. Any single invented number for a key metric (e.g. final accuracy, final growth rate) caps the leaf at 0.5 regardless of other traceable numbers.", + "weight": 8, + "sub_tasks": [], + "task_category": "Content Accuracy", + "finegrained_task_category": "Faithfulness - Numbers" + }, + { + "id": "mca-correct-units", + "requirements": "Units are consistent and physically correct throughout. ML: percentages stay as percent vs fractions consistently; runtime in seconds vs minutes consistently. HEP: GeV vs MeV not mixed, cross sections in pb vs fb consistent. Biology: mmol/gDW/h is the standard; do not mix with mM. Penalize 'unit-less' numbers where a unit is needed.", + "weight": 4, + "sub_tasks": [], + "task_category": "Content Accuracy", + "finegrained_task_category": "Faithfulness - Units" + }, + { + "id": "mca-claim-strength", + "requirements": "Strength of claims matches strength of evidence. 'X significantly outperforms Y' requires a statistical test result; 'X tends to be better than Y' requires a directional difference; 'X may be useful' requires only intuition. Penalize uncalibrated 'state of the art', 'novel', 'first to show' without specific support. Hedging language ('appears', 'suggests', 'is consistent with') should appear when the evidence is suggestive.", + "weight": 5, + "sub_tasks": [], + "task_category": "Content Accuracy", + "finegrained_task_category": "Faithfulness - Claim Calibration" + }, + { + "id": "mca-internal-consistency", + "requirements": "The same number / claim appears identically across the paper. Abstract says 'accuracy 92.3%'; Results says 'accuracy 92.3%' (not 92.5%); Conclusion says 'achieved 92.3%'. Tables match prose. Figure captions match figure content. Penalize ANY internal contradiction even if both versions might be 'plausible'.", + "weight": 4, + "sub_tasks": [], + "task_category": "Content Accuracy", + "finegrained_task_category": "Faithfulness - Internal Consistency" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/manifests/ML01.yaml b/tasks/ml/manifests/ML01.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3748fef818701b6ee69d7ce8c825b2bf04e0202e --- /dev/null +++ b/tasks/ml/manifests/ML01.yaml @@ -0,0 +1,89 @@ +# ============================================================================ +# T01 — Dropout regularization strategies for shallow MLPs +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML01 +title: "Dropout regularization strategies on shallow tabular MLPs" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Dropout-style regularization is a cornerstone of neural-net training for + overfitting control, but its impact on tabular classification with shallow + MLPs remains subtle. Standard element-wise dropout is the default; spatial + (feature-wise) dropout treats correlated input features as a block; MC / + variational dropout keeps dropout active at test time and averages over + samples. For tabular data with small feature counts and mild non-linearity, + these variants can produce equal test-set accuracy yet very different + probability calibration — a known gotcha that makes accuracy-only + evaluation misleading. + + A credible CPU-scale study of this topic (a) compares at least three + dropout variants against a no-dropout control on multiple sklearn + classification benchmarks, (b) reports both accuracy and calibration + metrics (ECE, NLL, Brier), (c) averages over ≥5 seeds with statistical + tests, and (d) checks for ablation integrity (that different dropout + variants actually produce different output distributions, not just the + same network). The research question is: *does the choice of dropout + variant matter more for calibration than for accuracy on tabular data?* + +hypotheses: + - id: H1 + statement: "Variational / MC dropout produces lower Expected Calibration Error (ECE) than standard element-wise dropout on at least 2 of 3 tabular classification benchmarks, averaged over ≥5 seeds." + measurable: true + - id: H2 + statement: "Test-set accuracy differs by <2 absolute percentage points between dropout variants on datasets where all methods exceed 95% accuracy — i.e. accuracy is non-discriminative and calibration is needed to distinguish methods." + measurable: true + - id: H3 + statement: "The no-dropout control is strictly worse than at least one dropout variant in ECE on at least 2 of 3 datasets." + measurable: true + +experiment_design: + research_question: "Does the choice of dropout variant (standard / spatial / variational) matter more for probability calibration than for test accuracy on tabular classification with shallow MLPs?" + conditions: + - name: "no_dropout" + description: "Shallow MLP (2 hidden layers, 64 units, ReLU) trained with no dropout. Control condition." + - name: "standard_dropout_p30" + description: "Same MLP with standard element-wise dropout at p=0.3 between hidden layers." + - name: "standard_dropout_p50" + description: "Same MLP with standard element-wise dropout at p=0.5." + - name: "spatial_dropout_p30" + description: "Same MLP with feature-blockwise dropout at p=0.3 applied to the input layer." + - name: "mc_dropout_p30_T20" + description: "Same MLP with standard p=0.3 dropout left active at test time; prediction is the mean of T=20 stochastic forward passes." + baselines: + - "no_dropout is the no-regularization baseline" + metrics: + - name: "test_accuracy" + direction: "maximize" + description: "Fraction correctly classified on held-out 20% test split, mean over 5 seeds." + - name: "ece" + direction: "minimize" + description: "Expected Calibration Error (15 bins) on the test split, mean over 5 seeds." + - name: "nll" + direction: "minimize" + description: "Mean per-example negative log-likelihood on the test split." + - name: "brier" + direction: "minimize" + description: "Mean Brier score on the test split." + datasets: + - name: "breast_cancer" + source: "sklearn.datasets.load_breast_cancer" + - name: "wine" + source: "sklearn.datasets.load_wine" + - name: "digits" + source: "sklearn.datasets.load_digits" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 240 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML01.json" diff --git a/tasks/ml/manifests/ML02.yaml b/tasks/ml/manifests/ML02.yaml new file mode 100644 index 0000000000000000000000000000000000000000..22eee9ac5119d5c74bd186c7e25b63e7d06c8e50 --- /dev/null +++ b/tasks/ml/manifests/ML02.yaml @@ -0,0 +1,98 @@ +# ============================================================================ +# T02 — Ensemble-method comparison for non-linear regression under noise +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML02 +title: "Bagging vs boosting vs stacking for noisy non-linear regression" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Ensemble learning is often presented as a broadly reliable way to improve + predictive performance, but different ensemble families can react very + differently to observation noise in regression. Bagging primarily reduces + variance by averaging many weakly correlated models. Boosting is sequential + and can aggressively fit residuals, which can help on structured non-linear + signals but may overfit high-noise targets if regularization is not tuned. + Stacking combines heterogeneous base learners through a meta-model and may + benefit from diversity, but it also introduces extra fitting stages that can + be sensitive to data size and leakage handling. + + A focused CPU-scale benchmark can probe these tradeoffs using synthetic and + lightweight sklearn regression datasets where non-linearity and noise are + controllable. By varying noise level explicitly in generated data, the study + can test whether relative ranking among bagging, boosting, and stacking is + stable or changes materially as signal-to-noise degrades. This design also + encourages careful evaluation with repeated random seeds and test-set metrics + that capture both average error and fit quality. + + A credible implementation should compare at least one representative for each + family (e.g., RandomForestRegressor for bagging, GradientBoostingRegressor + for boosting, StackingRegressor with diverse base estimators for stacking), + include a single-model baseline, and report RMSE-centered conclusions. Since + this benchmark is intended for fast autonomous execution, datasets and models + must remain small enough to run in minutes on one CPU core while still + producing clear numeric contrasts across noise regimes. + + The key outcome is not reproducing a known paper result but determining when + each ensemble strategy is most robust for noisy non-linear regression in a + constrained practical setting. + + *How does predictive robustness (RMSE) of bagging, boosting, and stacking change as regression noise increases on small non-linear benchmarks?* + +hypotheses: + - id: H1 + statement: "On the high-noise synthetic dataset (noise >= 25), bagging (RandomForestRegressor) achieves lower mean RMSE than boosting (GradientBoostingRegressor), averaged over at least 5 seeds." + measurable: true + - id: H2 + statement: "On at least 2 of 3 evaluated datasets, at least one ensemble method (bagging, boosting, or stacking) improves RMSE by >= 10% relative to the single DecisionTreeRegressor baseline." + measurable: true + - id: H3 + statement: "StackingRegressor attains the best (lowest) mean RMSE on at least 1 of 3 datasets while not being worst on more than 1 dataset, averaged over at least 5 seeds." + measurable: true + +experiment_design: + research_question: "How does RMSE performance of bagging, boosting, and stacking vary with increasing noise in non-linear regression tasks under CPU-only constraints?" + conditions: + - name: "decision_tree_baseline" + description: "Single DecisionTreeRegressor (max_depth tuned over a small grid) as non-ensemble baseline." + - name: "bagging_random_forest" + description: "RandomForestRegressor representing bagging-style averaging across trees." + - name: "boosting_gbrt" + description: "GradientBoostingRegressor representing sequential boosting on residuals." + - name: "stacking_heterogeneous" + description: "StackingRegressor with base learners {kNN regressor, ridge regression, shallow random forest} and a linear meta-regressor." + baselines: + - "decision_tree_baseline is the single-model baseline" + - "bagging_random_forest serves as a strong classical ensemble reference" + metrics: + - name: "rmse" + direction: "minimize" + description: "Root Mean Squared Error on held-out test split, averaged over seeds." + - name: "mae" + direction: "minimize" + description: "Mean Absolute Error on held-out test split, averaged over seeds." + - name: "r2" + direction: "maximize" + description: "Coefficient of determination on held-out test split, averaged over seeds." + datasets: + - name: "friedman1_low_noise" + source: "sklearn.datasets.make_friedman1 (n_samples=1200, n_features=10, noise=1.0)" + - name: "friedman1_high_noise" + source: "sklearn.datasets.make_friedman1 (n_samples=1200, n_features=10, noise=30.0)" + - name: "diabetes" + source: "sklearn.datasets.load_diabetes" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 420 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML02.json" diff --git a/tasks/ml/manifests/ML03.yaml b/tasks/ml/manifests/ML03.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c5e1a75a2645b76b63148cb140236d11a2204e22 --- /dev/null +++ b/tasks/ml/manifests/ML03.yaml @@ -0,0 +1,95 @@ +# ============================================================================ +# T03 — Gradient-free optimization on non-convex benchmark functions +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML03 +title: "CPU comparison of Nelder-Mead, Powell, and CMA-ES on non-convex functions" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Gradient-free optimization methods are widely used when derivatives are + unavailable, unreliable, or expensive to compute. In low-to-medium + dimensional non-convex landscapes, direct-search methods such as + Nelder-Mead and Powell are frequently used because they are easy to call + through scipy.optimize. Population-based approaches such as CMA-ES can be + more robust to local minima and ill-conditioning, but they introduce extra + hyperparameters and potentially higher per-iteration cost. + + A CPU-bounded benchmark can clarify practical trade-offs by evaluating these + methods under a fixed function-evaluation budget on standard synthetic + objective functions with known global minima. Rather than asking which method + is universally best, the relevant question is whether one method reaches + better objective values more reliably within the same budget and how much + runtime overhead that reliability costs. + + A credible experiment should evaluate at least three optimization conditions + (Nelder-Mead, Powell, CMA-ES) across multiple non-convex test functions, + using repeated random starts and common stopping/budget rules. Reporting only + final objective value is incomplete; runtime and success-rate-to-threshold + should be included to capture both quality and efficiency. Statistical + summaries over seeds are required because single runs are high variance. + + The resulting evidence should produce explicit pass/fail verdicts for each + hypothesis: whether CMA-ES improves best-found objective value, whether + Powell offers faster wall-clock convergence than CMA-ES at similar budgets, + and whether Nelder-Mead underperforms on multimodal landscapes. This keeps + the study measurable and feasible within a short single-core runtime window. + + *Under equal function-evaluation budgets on CPU, which gradient-free optimizer (Nelder-Mead, Powell, CMA-ES) gives the best trade-off between final objective quality, runtime, and success rate on non-convex benchmark functions?* + +hypotheses: + - id: H1 + statement: "CMA-ES achieves lower mean best objective value than both Nelder-Mead and Powell on at least 2 of 3 benchmark functions, averaged over >=10 random starts with the same evaluation budget." + measurable: true + - id: H2 + statement: "Powell has lower median wall-clock runtime than CMA-ES on at least 2 of 3 benchmark functions while finishing within the same function-evaluation cap." + measurable: true + - id: H3 + statement: "On the multimodal Rastrigin function, Nelder-Mead attains a lower success rate (fraction of runs reaching f(x) <= 1e-2) than CMA-ES by at least 0.20 absolute." + measurable: true + +experiment_design: + research_question: "Under equal evaluation budgets, how do Nelder-Mead, Powell, and CMA-ES compare on objective quality, runtime, and success probability for non-convex synthetic objectives?" + conditions: + - name: "nelder_mead" + description: "scipy.optimize.minimize with method='Nelder-Mead', random initial point per run, maxfev budget enforced." + - name: "powell" + description: "scipy.optimize.minimize with method='Powell', same initialization protocol and maxfev cap." + - name: "cma_es" + description: "CMA-ES implementation (lightweight numpy/scipy variant) with population updates under the same total function-evaluation budget." + - name: "random_search_baseline" + description: "Uniform random search within bounded domain using the same number of objective evaluations as other methods." + baselines: + - "random_search_baseline as a budget-matched non-adaptive baseline" + metrics: + - name: "primary_metric" + direction: "minimize" + description: "Mean best objective value found at termination (lower is better), aggregated over random starts." + - name: "median_runtime_sec" + direction: "minimize" + description: "Median wall-clock runtime per run in seconds for each (method, function)." + - name: "success_rate_eps" + direction: "maximize" + description: "Fraction of runs reaching objective value <= 1e-2 by termination." + datasets: + - name: "rastrigin_10d" + source: "synthetic numpy implementation of Rastrigin function in 10 dimensions" + - name: "rosenbrock_10d" + source: "synthetic numpy implementation of Rosenbrock function in 10 dimensions" + - name: "ackley_10d" + source: "synthetic numpy implementation of Ackley function in 10 dimensions" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 480 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML03.json" diff --git a/tasks/ml/manifests/ML04.yaml b/tasks/ml/manifests/ML04.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4fb4ff14b8531fda872adf45894f25f7258a562d --- /dev/null +++ b/tasks/ml/manifests/ML04.yaml @@ -0,0 +1,95 @@ +# ============================================================================ +# T04 — Feature scaling choices for k-nearest neighbors under distribution shift +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML04 +title: "Effect of standard, min-max, and robust scaling on KNN classification" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + K-nearest neighbors (KNN) is highly sensitive to feature scale because + distance calculations implicitly weight dimensions by their numeric ranges. + In practice, preprocessing decisions such as StandardScaler, MinMaxScaler, + or RobustScaler can alter neighborhood structure enough to change + classification performance as much as model hyperparameters do. Despite this, + scaling choice is often treated as a default rather than as an experimental + factor. + + The core methodological question is not whether scaling helps versus no + scaling (it usually does), but whether specific scaling families are better + matched to particular data distributions. Standard scaling assumes roughly + Gaussian-like behavior, min-max scaling compresses to bounded ranges and can + be strongly affected by extrema, and robust scaling centers/scales by median + and IQR to reduce outlier influence. + + A CPU-friendly, credible benchmark should compare these scalers with a fixed + KNN classifier across multiple datasets that differ in outlier prevalence and + marginal feature distributions, while averaging over multiple train/test + splits. Because KNN is lightweight on small-to-medium sklearn datasets, the + study can include both predictive quality and stability metrics without + exceeding strict runtime constraints. + + The experiment should report test accuracy as the primary outcome, plus + macro-F1 and split-to-split variability, and include at least one outlier- + heavy synthetic dataset to stress robust scaling behavior. The goal is to + produce falsifiable conclusions about when scaler choice materially changes + KNN outcomes. + + *How does the choice among standard, min-max, and robust feature scaling change KNN classification performance and stability across datasets with different distribution and outlier characteristics?* + +hypotheses: + - id: H1 + statement: "RobustScaler + KNN achieves higher mean test accuracy than StandardScaler + KNN on the outlier-heavy synthetic dataset by at least 0.03 absolute accuracy, averaged over ≥5 random splits." + measurable: true + - id: H2 + statement: "Across the evaluated datasets, at least one real dataset shows an absolute test-accuracy gap of ≥0.02 between the best and worst of {StandardScaler, MinMaxScaler, RobustScaler}, indicating scaler choice materially affects KNN performance." + measurable: true + - id: H3 + statement: "No_scaling baseline is not the top-accuracy condition on at least 2 of 3 datasets when compared against the three scaling methods with the same KNN hyperparameters." + measurable: true + +experiment_design: + research_question: "How does scaler choice (standard, min-max, robust) affect KNN classification accuracy and stability across datasets with different feature distributions and outlier profiles?" + conditions: + - name: "no_scaling_knn" + description: "KNeighborsClassifier with fixed k and distance metric on raw features; control condition." + - name: "standard_scaler_knn" + description: "Pipeline(StandardScaler, KNeighborsClassifier) with the same KNN hyperparameters as control." + - name: "minmax_scaler_knn" + description: "Pipeline(MinMaxScaler, KNeighborsClassifier) with identical KNN settings." + - name: "robust_scaler_knn" + description: "Pipeline(RobustScaler, KNeighborsClassifier) with identical KNN settings." + baselines: + - "no_scaling_knn is the preprocessing-free baseline" + metrics: + - name: "test_accuracy" + direction: "maximize" + description: "Mean held-out accuracy over at least 5 random stratified splits per dataset." + - name: "macro_f1" + direction: "maximize" + description: "Macro-averaged F1 on held-out data, averaged over splits." + - name: "accuracy_std" + direction: "minimize" + description: "Standard deviation of test accuracy across random splits as a stability indicator." + datasets: + - name: "wine" + source: "sklearn.datasets.load_wine" + - name: "breast_cancer" + source: "sklearn.datasets.load_breast_cancer" + - name: "synthetic_outlier_classification" + source: "sklearn.datasets.make_classification with injected feature outliers on a subset of samples" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 180 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML04.json" diff --git a/tasks/ml/manifests/ML05.yaml b/tasks/ml/manifests/ML05.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9959f5c16af9abd63035ba71f84703e80ff90713 --- /dev/null +++ b/tasks/ml/manifests/ML05.yaml @@ -0,0 +1,95 @@ +# ============================================================================ +# T05 — Dimensionality reduction for preserving cluster structure +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML05 +title: "Dimensionality reduction methods for preserving cluster structure in synthetic high-dimensional data" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Dimensionality reduction is frequently used before visualization and + clustering, but different methods optimize different objectives and can + distort neighborhood and global geometry in distinct ways. PCA is linear and + emphasizes variance preservation, while t-SNE and UMAP are nonlinear manifold + methods designed to preserve local structure. In practice, users often infer + cluster quality from 2D embeddings without checking whether separability in + the embedding reflects true high-dimensional structure. + + A CPU-scale investigation can probe this mismatch by generating synthetic + datasets where ground-truth cluster labels are known and controllable. By + varying cluster overlap, anisotropy, and manifold geometry (e.g., Gaussian + blobs, anisotropic transforms, and noisy moons embedded in high dimensions), + one can evaluate whether each reducer preserves cluster structure under + different regimes instead of relying on a single toy example. + + A credible experiment should compare PCA, t-SNE, and UMAP under a common + pipeline: reduce to 2 dimensions, then score structure using metrics such as + silhouette score and adjusted Rand index after a fixed clustering backend + (e.g., KMeans with true k). Runtime should also be tracked because nonlinear + methods may yield better structure at substantially higher computational cost, + which matters in constrained environments. + + The goal is not to claim a universally best reducer, but to quantify tradeoffs + between structure preservation and efficiency in settings where intrinsic + geometry differs. This produces actionable guidance for method choice based on + data regime rather than defaults. + + *Which dimensionality reduction method (PCA, t-SNE, or UMAP) best preserves cluster structure across distinct synthetic high-dimensional regimes when evaluated by silhouette score, ARI, and runtime?* + +hypotheses: + - id: H1 + statement: "On nonlinear manifold data (noisy two-moons embedded into 50D), at least one nonlinear reducer (t-SNE or UMAP) achieves silhouette_score at least 0.10 higher than PCA after 2D reduction and KMeans clustering, averaged over >=5 seeds." + measurable: true + - id: H2 + statement: "On linearly separable Gaussian blobs in 50D, PCA achieves silhouette_score within 0.05 of the best nonlinear method (t-SNE or UMAP), averaged over >=5 seeds." + measurable: true + - id: H3 + statement: "PCA has the lowest mean wall-clock reduction time and is at least 3x faster than both t-SNE and UMAP on at least 2 of 3 datasets." + measurable: true + +experiment_design: + research_question: "Which reducer best balances cluster-structure preservation and compute cost across linear and nonlinear synthetic high-dimensional datasets?" + conditions: + - name: "pca_2d" + description: "PCA with n_components=2 using sklearn.decomposition.PCA." + - name: "tsne_2d" + description: "t-SNE with n_components=2, perplexity in [20, 40], learning_rate='auto', init='pca'." + - name: "umap_2d" + description: "UMAP-like reducer: use umap.UMAP(n_components=2, n_neighbors in [15, 30], min_dist in [0.0, 0.3]) if available; if unavailable, use sklearn.manifold.SpectralEmbedding(n_components=2) as a documented fallback proxy." + - name: "identity_no_reduction" + description: "Baseline that applies KMeans directly in the original high-dimensional space (no dimensionality reduction)." + baselines: + - "identity_no_reduction as no-DR clustering baseline" + - "pca_2d as linear DR baseline" + metrics: + - name: "silhouette_score" + direction: "maximize" + description: "Silhouette score computed on the 2D embedding (or original space for identity baseline) using predicted KMeans labels; primary metric." + - name: "ari" + direction: "maximize" + description: "Adjusted Rand Index between KMeans labels and known synthetic ground-truth labels." + - name: "fit_transform_time_sec" + direction: "minimize" + description: "Wall-clock time for dimensionality reduction fit_transform only (seconds)." + datasets: + - name: "gaussian_blobs_50d" + source: "sklearn.datasets.make_blobs with 4 centers, n_features=50, cluster_std=1.2" + - name: "anisotropic_blobs_50d" + source: "make_blobs in 10D expanded/projection to 50D then linear anisotropic transform" + - name: "embedded_moons_50d" + source: "sklearn.datasets.make_moons(noise=0.08) embedded into 50D by random linear projection plus Gaussian noise" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 600 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML05.json" diff --git a/tasks/ml/manifests/ML06.yaml b/tasks/ml/manifests/ML06.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ac456da00a2928ab89a2d2abcdf384c1f5f7cdfe --- /dev/null +++ b/tasks/ml/manifests/ML06.yaml @@ -0,0 +1,100 @@ +# ============================================================================ +# T06 — Adaptive learning-rate schedules for logistic-regression convergence +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML06 +title: "Adaptive learning-rate schedules for logistic-regression convergence on binary classification" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Logistic regression is often treated as a solved baseline, yet its practical + training behavior still depends strongly on optimization details. In + first-order methods, the learning-rate schedule can dominate time-to-quality: + a constant step size may converge slowly or oscillate, while adaptive + schedules can accelerate early progress or stabilize late optimization. + Because logistic regression has a smooth convex objective, it offers a clean + setting to compare schedules without confounds from deep-network + non-convexity. + + This topic studies four schedule families in a unified mini-batch gradient + descent implementation: step decay, exponential decay, cosine annealing, and + cosine warm restarts. The focus is not raw final accuracy alone, but + optimization efficiency measured by epochs needed to reach a predefined + validation-loss threshold, plus robustness across datasets and random seeds. + A fixed-learning-rate baseline is required so any claimed speedup is + attributable to schedule design. + + A credible CPU-scale experiment should run on multiple binary classification + datasets from sklearn, with feature standardization, identical model + parameterization, and synchronized stopping criteria. Reporting should include + convergence epochs, final validation log loss, and test ROC-AUC so that faster + convergence is not achieved at the expense of generalization. Since warm + restarts can re-increase the learning rate, trajectory logging is important to + verify that restart behavior is actually implemented. + + The key decision criterion is whether adaptive schedules reduce optimization + effort in a consistent, measurable way. The study should conclude with clear + per-hypothesis verdicts and practical limitations (dataset size, threshold + choice, and sensitivity to initial learning rate). + + *Do adaptive learning-rate schedules (especially cosine variants) reduce logistic-regression convergence epochs versus fixed/monotone decay schedules while maintaining comparable binary-classification performance?* + +hypotheses: + - id: H1 + statement: "Cosine annealing or cosine warm restarts achieves at least 20% lower mean convergence_epochs than fixed learning rate on at least 2 of 3 evaluated binary datasets, averaged over >=5 seeds." + measurable: true + - id: H2 + statement: "Among adaptive schedules (step decay, exponential decay, cosine annealing, warm restarts), the best schedule's final validation log loss is no worse than 0.01 absolute compared with the worst schedule on each dataset (i.e., speed differences exceed quality differences)." + measurable: true + - id: H3 + statement: "Warm restarts reaches convergence in fewer epochs than monotone exponential decay on at least 2 of 3 datasets, without reducing test ROC-AUC by more than 0.01 absolute on those datasets." + measurable: true + +experiment_design: + research_question: "Which learning-rate schedule yields the fastest convergence for mini-batch logistic regression on binary sklearn tasks, and do faster schedules preserve validation/test performance?" + conditions: + - name: "fixed_lr" + description: "Mini-batch logistic regression trained with constant learning rate (baseline)." + - name: "step_decay" + description: "Learning rate multiplied by gamma at fixed epoch milestones (e.g., every 20 epochs)." + - name: "exponential_decay" + description: "Learning rate updated each epoch as lr_t = lr0 * exp(-k * t)." + - name: "cosine_annealing" + description: "Learning rate follows cosine decay from lr0 to lr_min over total epochs without restarts." + - name: "cosine_warm_restarts" + description: "Cosine schedule with periodic restarts (SGDR-style) resetting to higher learning rate at restart boundaries." + baselines: + - "fixed_lr is the primary optimization baseline" + - "exponential_decay is a monotone adaptive baseline for comparison to warm restarts" + metrics: + - name: "convergence_epochs" + direction: "minimize" + description: "Epoch index at which validation log loss first drops below a preset threshold (or max_epochs if never reached), averaged over seeds." + - name: "val_log_loss" + direction: "minimize" + description: "Final validation logistic loss after training, mean over seeds." + - name: "test_roc_auc" + direction: "maximize" + description: "ROC-AUC on held-out test set, mean over seeds." + datasets: + - name: "breast_cancer" + source: "sklearn.datasets.load_breast_cancer" + - name: "synthetic_binary_medium" + source: "sklearn.datasets.make_classification (n_samples=2000, n_features=30, n_informative=12, class_sep=1.0)" + - name: "synthetic_binary_noisy" + source: "sklearn.datasets.make_classification (n_samples=2500, n_features=40, n_informative=10, flip_y=0.08, class_sep=0.7)" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 420 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML06.json" diff --git a/tasks/ml/manifests/ML07.yaml b/tasks/ml/manifests/ML07.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8619fb27318ea83d59ca5c70219f332a1af4f041 --- /dev/null +++ b/tasks/ml/manifests/ML07.yaml @@ -0,0 +1,92 @@ +# ============================================================================ +# T07 — Text feature extraction strategies for linear document classifiers +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML07 +title: "Text feature extraction trade-offs: TF-IDF vs count vs hashing with NB/SVM" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Sparse bag-of-words pipelines remain strong baselines for document + classification, especially under strict CPU and latency constraints. + Three common feature extractors — CountVectorizer, TfidfVectorizer, and + HashingVectorizer — define different bias/variance and efficiency trade-offs. + Count vectors preserve raw frequency signals useful for generative models; + TF-IDF often improves linear margin classifiers by downweighting common + tokens; hashing avoids vocabulary construction and can reduce memory and + fit-time at the cost of collisions. + + Classifier choice interacts strongly with the representation. Multinomial + Naive Bayes (MNB) is typically paired with nonnegative count-like features, + while linear SVM variants often benefit from TF-IDF normalization. In + practical workflows, teams frequently need to choose between slightly better + macro-F1 and much faster runtime. This makes a pure "best score" benchmark + incomplete unless paired with timing and robustness checks. + + A credible CPU-scale study should compare multiple extractor+classifier + combinations on at least two document datasets available directly through + sklearn (or trivially synthesized text), use fixed train/test splits with + repeated seeds where relevant, and report both predictive quality and + efficiency metrics. The experiment should explicitly test whether TF-IDF + + linear SVM provides the strongest macro-F1 while hashing offers meaningful + speed advantages with limited degradation. + + *Does TF-IDF with linear SVM deliver the best macro-F1 on sklearn text benchmarks, and is HashingVectorizer a worthwhile speed/accuracy trade-off versus vocabulary-based features?* + +hypotheses: + - id: H1 + statement: "TF-IDF + linear SVM achieves the highest macro-F1 among implemented conditions on at least 2 of 3 evaluated text datasets." + measurable: true + - id: H2 + statement: "HashingVectorizer-based pipelines reduce vectorization+fit wall-clock time by at least 20% versus the corresponding TF-IDF pipeline on at least 2 of 3 datasets." + measurable: true + - id: H3 + statement: "Multinomial Naive Bayes with count vectors attains macro-F1 within 0.05 absolute of TF-IDF + linear SVM on at least 1 of 3 datasets." + measurable: true + +experiment_design: + research_question: "Across sklearn-accessible document datasets, what are the accuracy-efficiency trade-offs between count, TF-IDF, and hashing features when paired with Multinomial Naive Bayes and linear SVM classifiers?" + conditions: + - name: "count_mnb" + description: "CountVectorizer (word unigrams, min_df=2) + MultinomialNB(alpha=1.0)." + - name: "tfidf_mnb" + description: "TfidfVectorizer (word unigrams, min_df=2, norm=l2) + MultinomialNB(alpha=1.0)." + - name: "tfidf_linear_svm" + description: "TfidfVectorizer (word unigrams, min_df=2, norm=l2) + LinearSVC(C=1.0)." + - name: "hashing_linear_svm" + description: "HashingVectorizer (word unigrams, n_features=2^18, alternate_sign=False) + LinearSVC(C=1.0)." + baselines: + - "count_mnb as the classic sparse-text baseline" + - "tfidf_mnb as a same-classifier feature-ablation baseline" + metrics: + - name: "macro_f1" + direction: "maximize" + description: "Macro-averaged F1 score on held-out test split." + - name: "accuracy" + direction: "maximize" + description: "Overall test accuracy for comparability with prior text benchmarks." + - name: "fit_predict_time_sec" + direction: "minimize" + description: "End-to-end wall-clock time for vectorization, model fit, and test prediction." + datasets: + - name: "20newsgroups_4class" + source: "sklearn.datasets.fetch_20newsgroups with 4 categories (subset=train/test, remove=(\"headers\",\"footers\",\"quotes\"))" + - name: "20newsgroups_8class" + source: "sklearn.datasets.fetch_20newsgroups with 8 categories (subset=train/test, remove=(\"headers\",\"footers\",\"quotes\"))" + - name: "synthetic_topic_docs" + source: "Trivially synthesized corpus via templated sentence generation for 4 classes, 2000 documents total" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 600 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML07.json" diff --git a/tasks/ml/manifests/ML08.yaml b/tasks/ml/manifests/ML08.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fa73dc2529797055c251d3b1418bbfd06bf7bc56 --- /dev/null +++ b/tasks/ml/manifests/ML08.yaml @@ -0,0 +1,102 @@ +# ============================================================================ +# T08 — Class-imbalance handling strategies for binary classification +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML08 +title: "Impact of imbalance-handling strategies on sklearn binary classifiers" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Class imbalance is one of the most common practical failure modes in binary + classification: a model can achieve high raw accuracy by predicting the + majority class while missing the minority class almost entirely. In + scikit-learn workflows, practitioners often choose among simple data-level + resampling (random oversampling, random undersampling), algorithm-level + weighting (class_weight="balanced"), or synthetic methods like SMOTE. + However, these strategies trade off minority recall, precision, and overall + discrimination differently, and their behavior varies with dataset geometry. + + A compact CPU-friendly study can still be rigorous if it compares multiple + imbalance-handling strategies under the same base learner and preprocessing + pipeline, evaluates with metrics appropriate for skewed labels (balanced + accuracy, F1, PR-AUC), and averages over several random seeds. The focus is + not on maximizing one benchmark score but on understanding whether methods + that improve minority detection do so consistently across datasets, and at + what precision cost. + + To keep implementation feasible in a short autonomous run, the experiment can + use sklearn-native datasets and synthetic imbalance generated from a standard + source dataset, with a shared train/test protocol and fixed model family + (e.g., logistic regression). SMOTE can be implemented in a lightweight way + using sklearn.neighbors to interpolate minority-neighbor pairs, avoiding any + external dependency. + + The key analytical goal is to test whether weighted learning or synthetic + oversampling provides the most reliable gain in balanced accuracy over a + no-treatment baseline, and whether naive random oversampling tends to hurt + precision-oriented metrics relative to class weighting. + + *Which imbalance-handling strategy (SMOTE, random oversampling, class weights, undersampling) gives the most consistent balanced-accuracy improvement for binary sklearn classification under a fixed model and protocol?* + +hypotheses: + - id: H1 + statement: "At least one of {class_weight_balanced, smote} achieves higher balanced_accuracy than no_handling by >=0.03 absolute on at least 2 of 3 datasets (mean over >=5 seeds)." + measurable: true + - id: H2 + statement: "Random undersampling yields the highest minority recall among compared strategies on at least 2 of 3 datasets, but does not achieve the best PR-AUC on those same datasets." + measurable: true + - id: H3 + statement: "Random oversampling does not outperform class_weight_balanced in balanced_accuracy on more than 1 of 3 datasets (seed-averaged comparison)." + measurable: true + +experiment_design: + research_question: "Which imbalance-handling strategy provides the most consistent gains in balanced accuracy for binary classification with fixed sklearn models under controlled train/test splits?" + conditions: + - name: "no_handling" + description: "Baseline logistic regression trained on imbalanced data with no resampling and default class weights." + - name: "class_weight_balanced" + description: "Same logistic regression with class_weight='balanced'." + - name: "random_oversampling" + description: "Training-set minority class randomly oversampled with replacement to match majority count." + - name: "random_undersampling" + description: "Training-set majority class randomly undersampled without replacement to match minority count." + - name: "smote_k5" + description: "Training-set minority class augmented with synthetic samples via kNN interpolation (k=5) until class balance is reached." + baselines: + - "no_handling is the primary baseline" + - "class_weight_balanced serves as an algorithm-level baseline against data-level resampling" + metrics: + - name: "balanced_accuracy" + direction: "maximize" + description: "Mean of sensitivity and specificity on held-out test split, averaged over seeds." + - name: "minority_recall" + direction: "maximize" + description: "Recall for the positive/minority class on test data." + - name: "average_precision" + direction: "maximize" + description: "Area under precision-recall curve (PR-AUC / AP) on test data." + - name: "f1" + direction: "maximize" + description: "Binary F1 score for the minority class at default threshold 0.5." + datasets: + - name: "breast_cancer_imbalanced" + source: "sklearn.datasets.load_breast_cancer with induced imbalance by downsampling positive class in training folds" + - name: "make_classification_90_10" + source: "sklearn.datasets.make_classification(n_samples=4000, n_features=20, weights=[0.9,0.1], random_state=seed)" + - name: "make_classification_95_05" + source: "sklearn.datasets.make_classification(n_samples=5000, n_features=25, weights=[0.95,0.05], class_sep=1.0, random_state=seed)" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 360 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML08.json" diff --git a/tasks/ml/manifests/ML09.yaml b/tasks/ml/manifests/ML09.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d912a6d04c8cee6747a02e0d4e148c9ef8939389 --- /dev/null +++ b/tasks/ml/manifests/ML09.yaml @@ -0,0 +1,101 @@ +# ============================================================================ +# T09 — Comparing Bayesian optimization vs grid search vs random search for +# hyperparameter tuning of random forests on UCI benchmark datasets +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML09 +title: "Bayesian optimization vs grid vs random search for random-forest tuning" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Hyperparameter tuning often dominates practical AutoML performance, yet in + CPU-limited settings the search strategy itself can matter as much as the + model family. For random forests, common tunables (number of trees, + max_depth, min_samples_split, min_samples_leaf, max_features) define a mixed + discrete-continuous space where exhaustive grids can be wasteful, + random search can be surprisingly strong, and Bayesian optimization can use + surrogate modeling to focus evaluations on promising regions. + + A concise benchmark can test this tradeoff by fixing a single model class + (RandomForestClassifier), fixed CV protocol, and comparable evaluation budget + across search methods. Grid search should use a compact but principled grid; + random search should sample from the same parameter ranges; Bayesian + optimization can be implemented with sklearn's Gaussian-process based + optimizer (e.g., BayesSearchCV if available is not allowed here, so a custom + lightweight GP + acquisition loop or scipy/sklearn surrogate approach is + expected). The key is fairness: equal or near-equal numbers of objective + evaluations and identical data splits. + + Since this benchmark must run in under 15 minutes on one CPU core, + datasets should be small-to-medium sklearn/UCI-style classification tasks + already available in sklearn (e.g., wine, breast_cancer, digits). The + analysis should report best cross-validation score, held-out test accuracy, + and search efficiency (score gain per evaluation or wall-clock). + Multi-seed repeats are desirable to reduce noise, but can be kept modest to + remain within runtime limits. + + The experiment should answer whether Bayesian optimization delivers better + best-found configurations under tight evaluation budgets, or whether simpler + baselines (especially random search) are effectively equivalent for these + tabular tasks. Interpretation should explicitly separate optimization quality + from compute cost. + + *Under a fixed small evaluation budget, does Bayesian optimization find better random-forest hyperparameters than grid and random search on sklearn UCI-style classification benchmarks?* + +hypotheses: + - id: H1 + statement: "With an equal budget of 20 hyperparameter evaluations per dataset, Bayesian optimization achieves higher mean best_cv_score than random search on at least 2 of 3 datasets." + measurable: true + - id: H2 + statement: "Random search matches or exceeds grid search in best_cv_score on at least 2 of 3 datasets under the same maximum number of evaluated configurations." + measurable: true + - id: H3 + statement: "The mean held-out test accuracy of the best Bayesian-tuned model is at least 0.5 percentage points higher than the best grid-tuned model on at least 1 of 3 datasets." + measurable: true + +experiment_design: + research_question: "Under a fixed evaluation budget, which tuning strategy (Bayesian optimization, grid search, random search) yields the best random-forest cross-validation and test performance on sklearn UCI-style datasets?" + conditions: + - name: "grid_search_budget20" + description: "Grid search over a compact predefined grid for RandomForestClassifier, capped at 20 evaluated configurations per dataset." + - name: "random_search_budget20" + description: "Random search over matched hyperparameter ranges with 20 sampled configurations per dataset." + - name: "bayes_opt_budget20" + description: "Bayesian optimization using a Gaussian-process surrogate and acquisition-guided proposals for 20 total evaluations per dataset." + - name: "default_rf" + description: "Untuned RandomForestClassifier with sklearn default hyperparameters as a reference baseline." + baselines: + - "default_rf as no-tuning baseline" + - "grid_search_budget20 as classical tuning baseline" + metrics: + - name: "best_cv_score" + direction: "maximize" + description: "Best mean 3-fold cross-validation accuracy discovered by each search method." + - name: "test_accuracy" + direction: "maximize" + description: "Accuracy on a held-out test split using the best hyperparameters found by each method." + - name: "search_time_sec" + direction: "minimize" + description: "Wall-clock time spent in hyperparameter search per method and dataset." + datasets: + - name: "wine" + source: "sklearn.datasets.load_wine" + - name: "breast_cancer" + source: "sklearn.datasets.load_breast_cancer" + - name: "digits" + source: "sklearn.datasets.load_digits" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 780 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML09.json" diff --git a/tasks/ml/manifests/ML10.yaml b/tasks/ml/manifests/ML10.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1d0d9f6c4874d77bd66afd117ce69fdc6ba2ea68 --- /dev/null +++ b/tasks/ml/manifests/ML10.yaml @@ -0,0 +1,98 @@ +# ============================================================================ +# T10 — Cross-validation strategy effects on small-sample model selection +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML10 +title: "Cross-validation strategy reliability for small-sample model selection" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + With small datasets, model selection can become highly sensitive to the + validation protocol. Practitioners often choose among k-fold, + stratified k-fold, repeated k-fold, and leave-one-out cross-validation + (LOOCV), but these strategies trade off bias, variance, and compute in + different ways. A method that appears best under one CV scheme may not be + best under another, especially when class balance is imperfect and sample + counts are low. + + This topic studies CV strategy as the independent variable while keeping + candidate models fixed and lightweight. The key quantity is estimation bias: + the gap between CV-estimated score used for model selection and the realized + test score of the selected model on a held-out test set. In small-sample + settings, minimizing this gap is often more important than maximizing raw CV + score, because over-optimistic selection can produce brittle deployments. + + A credible CPU-scale experiment should evaluate multiple sklearn datasets + reduced to small training sizes, run several random seeds, and compare at + least four CV strategies under the same model grid and scoring rule. The + study should report not only estimation bias but also variance across seeds + and model-selection stability. Baselines should include standard k-fold and + stratified k-fold; repeated stratified k-fold and LOOCV serve as contrasting + alternatives with different variance/compute characteristics. + + The goal is not to prove one universally superior protocol, but to quantify + when more elaborate CV schemes improve reliability enough to justify their + cost under strict CPU budgets. + + *Which cross-validation strategy yields the lowest and most stable model-selection estimation bias for small-sample classification tasks under a fixed candidate-model grid?* + +hypotheses: + - id: H1 + statement: "RepeatedStratifiedKFold (5 folds × 3 repeats) achieves lower mean absolute estimation bias than plain KFold (5 folds) on at least 2 of 3 evaluated small-sample datasets, averaged over ≥5 random seeds." + measurable: true + - id: H2 + statement: "StratifiedKFold (5 folds) yields lower across-seed standard deviation of selected-model test accuracy than plain KFold (5 folds) on at least 2 of 3 datasets." + measurable: true + - id: H3 + statement: "LOOCV does not outperform RepeatedStratifiedKFold in mean absolute estimation bias by more than 0.01 absolute score on any dataset, while requiring greater wall-clock time." + measurable: true + +experiment_design: + research_question: "For small-sample classification model selection, how do KFold, StratifiedKFold, RepeatedStratifiedKFold, and LOOCV compare in estimation bias, stability, and compute cost?" + conditions: + - name: "kfold_5" + description: "Model selection via 5-fold KFold (shuffle=True) on the training split." + - name: "stratified_kfold_5" + description: "Model selection via 5-fold StratifiedKFold (shuffle=True) on the training split." + - name: "repeated_stratified_5x3" + description: "Model selection via RepeatedStratifiedKFold with 5 folds and 3 repeats." + - name: "loocv" + description: "Model selection via Leave-One-Out cross-validation on the training split." + baselines: + - "kfold_5 as the non-stratified baseline" + - "stratified_kfold_5 as the standard small-sample baseline" + metrics: + - name: "estimation_bias" + direction: "minimize" + description: "Absolute difference between best CV mean score and held-out test score of the selected model, aggregated over seeds." + - name: "selected_model_test_accuracy" + direction: "maximize" + description: "Held-out test accuracy of the model selected by each CV strategy, mean over seeds." + - name: "selection_stability" + direction: "maximize" + description: "Fraction of seeds selecting the modal hyperparameter configuration (higher = more stable)." + - name: "wall_clock_sec" + direction: "minimize" + description: "Runtime per CV strategy over all seeds and datasets on CPU." + datasets: + - name: "breast_cancer_small" + source: "sklearn.datasets.load_breast_cancer (subsample training set to n<=120)" + - name: "wine_small" + source: "sklearn.datasets.load_wine (subsample training set to n<=100)" + - name: "synthetic_imbalanced_small" + source: "sklearn.datasets.make_classification (n_samples=160, weights=[0.75,0.25], class_sep tuned for moderate difficulty)" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 600 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML10.json" diff --git a/tasks/ml/manifests/ML11.yaml b/tasks/ml/manifests/ML11.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ac77c9ae7721110e05eddc4df95cd9341cb4bb4a --- /dev/null +++ b/tasks/ml/manifests/ML11.yaml @@ -0,0 +1,98 @@ +# ============================================================================ +# T11 — Unsupervised outlier detection on tabular datasets with injected anomalies +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML11 +title: "Benchmarking classical unsupervised outlier detectors under controlled anomaly injection" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Classical unsupervised anomaly-detection methods remain widely used in + production tabular pipelines because they are interpretable enough, + CPU-efficient, and available in standard libraries. Yet their relative + behavior can change sharply as anomaly prevalence and feature scaling vary. + IsolationForest is often robust in heterogeneous feature spaces, while + distance/density methods like LocalOutlierFactor can be strong when local + neighborhoods are informative but may degrade in higher dimensions. + + A practical benchmark should avoid external downloads and still provide + realistic control over anomaly rate. One reliable strategy is to start from + clean sklearn classification datasets and inject synthetic anomalies into the + test split by sampling uniformly beyond feature-wise quantile ranges of the + training data. This yields known binary ground truth for evaluation while + preserving a realistic inlier distribution. + + A credible CPU-scale study here should compare IsolationForest, + LocalOutlierFactor (novelty mode), OneClassSVM, and EllipticEnvelope under a + shared preprocessing pipeline (e.g., StandardScaler), evaluate ROC-AUC and + PR-AUC across at least two datasets and multiple seeds, and include a simple + baseline such as random scoring. Since contamination is rarely known exactly + in practice, the benchmark should also probe at least two injected anomaly + rates. + + The main decision-relevant question is not whether any method can detect easy + anomalies, but which method is consistently best across datasets and + contamination levels under strict CPU constraints. A concise, quantitative + hypothesis-driven comparison is therefore the goal. + + *Which classical unsupervised outlier detector is most robust across tabular datasets and injected anomaly rates when measured by ROC-AUC and PR-AUC under a single-core CPU budget?* + +hypotheses: + - id: H1 + statement: "IsolationForest achieves higher mean ROC-AUC than OneClassSVM on at least 2 of 3 datasets when averaged over anomaly rates {0.05, 0.10} and ≥3 seeds." + measurable: true + - id: H2 + statement: "At anomaly rate 0.10, LocalOutlierFactor attains the best PR-AUC (rank 1) on at least 1 of 3 datasets." + measurable: true + - id: H3 + statement: "Each implemented detector (IsolationForest, LocalOutlierFactor, OneClassSVM, EllipticEnvelope) outperforms a random-score baseline in ROC-AUC by at least 0.10 on the pooled mean across all evaluated datasets/rates." + measurable: true + +experiment_design: + research_question: "Which among IsolationForest, LocalOutlierFactor, OneClassSVM, and EllipticEnvelope is most robust across datasets and injected anomaly rates for unsupervised outlier detection under CPU constraints?" + conditions: + - name: "isolation_forest" + description: "sklearn IsolationForest with contamination set to injected rate, 200 trees, default feature subsampling." + - name: "local_outlier_factor_novelty" + description: "sklearn LocalOutlierFactor in novelty=True mode, n_neighbors in [20, 35], contamination set to injected rate." + - name: "one_class_svm_rbf" + description: "sklearn OneClassSVM with RBF kernel, nu matched to injected rate, gamma='scale'." + - name: "elliptic_envelope" + description: "sklearn EllipticEnvelope with contamination set to injected rate and robust covariance estimation." + - name: "random_score_baseline" + description: "Baseline assigning i.i.d. uniform random anomaly scores to test points." + baselines: + - "random_score_baseline" + - "one_class_svm_rbf as a classical margin-based reference" + metrics: + - name: "roc_auc" + direction: "maximize" + description: "ROC-AUC of anomaly scores against injected ground-truth labels on the test set, averaged over seeds." + - name: "pr_auc" + direction: "maximize" + description: "Average precision / PR-AUC for anomaly class on the test set, averaged over seeds." + - name: "fit_predict_time_sec" + direction: "minimize" + description: "Wall-clock seconds for model fit plus test scoring per (dataset, rate, seed), reported as mean." + datasets: + - name: "breast_cancer" + source: "sklearn.datasets.load_breast_cancer" + - name: "wine" + source: "sklearn.datasets.load_wine" + - name: "digits" + source: "sklearn.datasets.load_digits" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 480 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML11.json" diff --git a/tasks/ml/manifests/ML12.yaml b/tasks/ml/manifests/ML12.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c46cfe03918590b9ec8cd14b6b917b0d824d6e65 --- /dev/null +++ b/tasks/ml/manifests/ML12.yaml @@ -0,0 +1,96 @@ +# ============================================================================ +# T12 — Clustering algorithm comparison on synthetic geometric datasets +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML12 +title: "Comparing clustering algorithms on synthetic non-convex and anisotropic shapes" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Clustering algorithms encode very different geometric assumptions. K-means + prefers spherical, equal-variance groups; agglomerative clustering can adapt + to hierarchical structure depending on linkage; DBSCAN finds dense regions + and can recover non-convex shapes while labeling outliers as noise; spectral + clustering can separate manifolds when graph affinity is appropriate. + Because these assumptions interact strongly with data geometry, a single + benchmark score often hides systematic failure modes. + + Synthetic datasets are ideal for a fast but meaningful comparison because we + can generate known structures with ground-truth labels: intertwined moons, + concentric circles, and anisotropic Gaussian blobs. These patterns expose + strengths and weaknesses that are difficult to isolate in real-world data. + With labeled synthetic data, external clustering metrics like adjusted rand + index (ARI) and normalized mutual information (NMI) directly quantify + partition recovery quality. + + A credible CPU-scale study should compare several algorithms under a shared + protocol: standardized inputs, fixed train-size generation per seed, and + explicit handling of methods that output noise labels. It should include at + least one centroid baseline (k-means) and one density method (DBSCAN), then + test whether non-linear methods systematically outperform centroid methods on + non-convex shapes while avoiding large regressions on anisotropic blobs. + + The practical goal is not to crown one universally best algorithm, but to + establish data-shape-dependent guidance. If shape complexity changes which + method wins, practitioners should choose clustering tools by geometric prior + rather than habit. + + *How strongly does dataset geometry (non-convex vs anisotropic) determine which clustering algorithm achieves the best ground-truth agreement under a fixed CPU-budget protocol?* + +hypotheses: + - id: H1 + statement: "On the two non-convex datasets (moons and circles), at least one of {DBSCAN, SpectralClustering} achieves mean ARI that is at least 0.15 higher than k-means, averaged over ≥5 seeds." + measurable: true + - id: H2 + statement: "On the anisotropic-blobs dataset, agglomerative clustering (ward linkage) achieves ARI greater than or equal to k-means in the seed-averaged result (difference >= 0.00)." + measurable: true + - id: H3 + statement: "No single algorithm ranks first in ARI on all evaluated datasets; i.e., the best-ARI method differs on at least one dataset from the global winner." + measurable: true + +experiment_design: + research_question: "How strongly does dataset geometry (non-convex vs anisotropic) determine which clustering algorithm achieves the best agreement with ground-truth labels under a fixed CPU-budget protocol?" + conditions: + - name: "kmeans_k2_or_k3" + description: "KMeans baseline with n_clusters set to dataset ground-truth class count; n_init>=10." + - name: "agglomerative_ward" + description: "AgglomerativeClustering with ward linkage and n_clusters equal to ground-truth class count." + - name: "dbscan_tuned_eps" + description: "DBSCAN with eps selected from a small grid per dataset (e.g., 0.1 to 0.5) and fixed min_samples (e.g., 5)." + - name: "spectral_rbf" + description: "SpectralClustering with RBF affinity and n_clusters equal to ground-truth class count." + baselines: + - "kmeans_k2_or_k3 as centroid-based baseline" + - "agglomerative_ward as hierarchical baseline" + metrics: + - name: "adjusted_rand_score" + direction: "maximize" + description: "Adjusted Rand Index against ground-truth labels, averaged over seeds." + - name: "normalized_mutual_info" + direction: "maximize" + description: "Normalized Mutual Information against ground-truth labels." + - name: "silhouette_score" + direction: "maximize" + description: "Internal cohesion/separation score computed from predicted labels (excluding noise-only failures)." + datasets: + - name: "moons" + source: "sklearn.datasets.make_moons (n_samples~800, noise~0.08)" + - name: "circles" + source: "sklearn.datasets.make_circles (n_samples~800, noise~0.06, factor~0.5)" + - name: "anisotropic_blobs" + source: "sklearn.datasets.make_blobs followed by linear transformation to create anisotropy" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 420 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML12.json" diff --git a/tasks/ml/manifests/ML13.yaml b/tasks/ml/manifests/ML13.yaml new file mode 100644 index 0000000000000000000000000000000000000000..afb257217cca6e64ad1a973f2e1f4b6e6654fb21 --- /dev/null +++ b/tasks/ml/manifests/ML13.yaml @@ -0,0 +1,95 @@ +# ============================================================================ +# T13 — Kernel choice effects for Gaussian Process regression +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML13 +title: "Evaluating kernel choices for Gaussian Process regression on synthetic 1-D and 5-D functions" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Gaussian Process (GP) regression performance depends strongly on the kernel, + which encodes assumptions about function smoothness and local structure. + Practitioners often default to the RBF kernel because it is easy to optimize + and widely available, but this choice can underperform when the target + function has roughness patterns that are better matched by Matern families or + when polynomial trends dominate part of the signal. Since kernel + misspecification can be subtle, a short benchmark should compare predictive + uncertainty quality as well as point error. + + A CPU-scale study can be done entirely with sklearn GaussianProcessRegressor + on synthetic datasets where the data-generating process is controlled. In 1-D, + a mildly nonstationary smooth function with additive Gaussian noise can expose + differences in extrapolation and uncertainty tails. In 5-D, a mixed + sinusoidal-plus-quadratic target can stress anisotropy and interactions while + remaining fast enough for repeated fitting under multiple random seeds. + + To make claims measurable, the experiment should evaluate at least four kernel + choices (RBF, polynomial, Matern-3/2, Matern-5/2) under the same train/test + splits, optimizer restarts, and noise model assumptions. Because the topic's + key metric is negative log-likelihood, uncertainty calibration quality should + be primary; RMSE and R^2 provide supporting evidence for point prediction. + Seed averaging is important because synthetic splits and optimizer + initialization can change marginal likelihood optimization outcomes. + + The core question is whether smoother kernels (RBF, Matern-5/2) consistently + outperform rougher or mismatched kernels (Matern-3/2, polynomial) on test NLL + across both low- and moderate-dimensional synthetic tasks, and whether ranking + by NLL aligns with ranking by RMSE. + + *Which GP kernel among RBF, polynomial, Matern-3/2, and Matern-5/2 yields the best uncertainty-aware generalization (lowest test NLL) on synthetic 1-D and 5-D noisy regression functions?* + +hypotheses: + - id: H1 + statement: "Across the two synthetic datasets, at least one of {RBF, Matern-5/2} achieves the lowest mean test NLL on each dataset (averaged over >=5 seeds)." + measurable: true + - id: H2 + statement: "The polynomial kernel has mean test NLL at least 10% worse than the best kernel on at least 1 of the 2 datasets." + measurable: true + - id: H3 + statement: "Kernel ranking by mean test NLL and by mean RMSE is not identical on at least 1 dataset, indicating uncertainty quality and point error disagree." + measurable: true + +experiment_design: + research_question: "How do RBF, polynomial, Matern-3/2, and Matern-5/2 kernels compare for Gaussian Process regression on synthetic 1-D and 5-D noisy functions when judged primarily by test NLL?" + conditions: + - name: "gp_rbf" + description: "GaussianProcessRegressor with ConstantKernel * RBF + WhiteKernel, hyperparameters optimized by log-marginal-likelihood." + - name: "gp_poly_deg2" + description: "GaussianProcessRegressor with ConstantKernel * DotProduct^2 (implemented via polynomial feature mapping + DotProduct kernel) + WhiteKernel as a polynomial-trend proxy." + - name: "gp_matern32" + description: "GaussianProcessRegressor with ConstantKernel * Matern(nu=1.5) + WhiteKernel." + - name: "gp_matern52" + description: "GaussianProcessRegressor with ConstantKernel * Matern(nu=2.5) + WhiteKernel." + baselines: + - "gp_rbf as the common default-kernel baseline" + - "gp_poly_deg2 as a mismatched-trend baseline" + metrics: + - name: "test_nll" + direction: "minimize" + description: "Mean negative log predictive density on held-out test data using GP predictive mean and variance, averaged over seeds." + - name: "rmse" + direction: "minimize" + description: "Root mean squared error on test targets, averaged over seeds." + - name: "r2" + direction: "maximize" + description: "Coefficient of determination on test targets, averaged over seeds." + datasets: + - name: "synthetic_1d_sinmix" + source: "Generated with numpy: x in [-3,3], y = sin(2x) + 0.3x + epsilon, epsilon~N(0, 0.15^2)." + - name: "synthetic_5d_mixed" + source: "Generated with numpy: X in [-2,2]^5, y = sin(x1) + 0.5*x2^2 - 0.7*x3 + 0.3*x4*x5 + epsilon, epsilon~N(0, 0.2^2)." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 360 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML13.json" diff --git a/tasks/ml/manifests/ML14.yaml b/tasks/ml/manifests/ML14.yaml new file mode 100644 index 0000000000000000000000000000000000000000..58404950da5bc290738dfc8a4679df9668d9eb1e --- /dev/null +++ b/tasks/ml/manifests/ML14.yaml @@ -0,0 +1,96 @@ +# ============================================================================ +# T14 — Comparing conformal prediction procedures on heteroscedastic regression +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML14 +title: "Comparing split-conformal, Mondrian conformal, and CQR-style intervals on heteroscedastic regression" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Conformal prediction provides finite-sample coverage guarantees with minimal + assumptions, making it attractive for uncertainty quantification in practical + regression. However, standard split-conformal intervals are often globally + calibrated and can be inefficient when noise is heteroscedastic: they may + over-cover low-noise regions and under-cover high-noise regions. This + motivates conditional variants such as Mondrian conformal (group-conditional + calibration) and CQR-style approaches that adapt interval width through + quantile modeling before conformal correction. + + In small, CPU-only settings, one can still run a meaningful comparison by + using sklearn-compatible regressors and synthetic data where heteroscedastic + structure is controlled. A rigorous setup should evaluate not only marginal + coverage but also efficiency (mean interval width) and conditional behavior + (coverage gap across strata of predicted difficulty or input regions). These + diagnostics reveal when methods achieve nominal coverage by producing overly + wide intervals versus genuinely adapting to varying noise. + + A credible benchmark should include at least one homoscedastic control and at + least one heteroscedastic dataset, then compare split-conformal, a Mondrian + variant (binning by an auxiliary score such as |x| or predicted scale), and a + CQR-style method based on lower/upper quantile regressors plus conformal + adjustment. Repeated random splits (multiple seeds) are needed because + conformal procedures can vary with calibration sample composition. + + The key question is whether adaptive procedures can reduce conditional + miscoverage while maintaining near-target marginal coverage and reasonable + interval width under strict runtime constraints. + + *Do Mondrian and CQR-style conformal procedures achieve lower conditional coverage error than vanilla split-conformal at comparable marginal coverage on heteroscedastic regression tasks?* + +hypotheses: + - id: H1 + statement: "On heteroscedastic datasets, at least one adaptive method (Mondrian or CQR-style) attains a lower absolute coverage gap to the 90% target than split-conformal by at least 0.02 on at least 2 of 3 datasets, averaged over \u22655 seeds." + measurable: true + - id: H2 + statement: "At matched target 90% coverage, CQR-style conformal yields mean interval width no larger than split-conformal on at least 2 of 3 datasets (difference \u2264 0.00)." + measurable: true + - id: H3 + statement: "Mondrian conformal reduces worst-bin conditional miscoverage (max over 4 bins of |bin_coverage-0.90|) by at least 0.03 versus split-conformal on at least 2 of 3 datasets." + measurable: true + +experiment_design: + research_question: "Do adaptive conformal procedures (Mondrian, CQR-style) improve coverage quality and efficiency over split-conformal for heteroscedastic regression under a fixed 90% target coverage?" + conditions: + - name: "split_conformal_rf" + description: "Vanilla split-conformal regression using RandomForestRegressor point predictor and absolute residual conformity scores on a held-out calibration split." + - name: "mondrian_conformal_rf_4bins" + description: "Mondrian split-conformal with the same base predictor, calibration and test points partitioned into 4 bins by an auxiliary score (e.g., fitted value or |x0| proxy), with per-bin quantiles." + - name: "cqr_gbr" + description: "CQR-style method using GradientBoostingRegressor quantile models (alpha=0.05, 0.95) and split-conformal correction on calibration residuals of quantile bands." + - name: "naive_quantile_no_conformal" + description: "Non-conformal baseline using raw quantile regression interval [q0.05, q0.95] without conformal adjustment." + baselines: + - "split_conformal_rf is the primary conformal baseline" + - "naive_quantile_no_conformal is the non-conformal baseline" + metrics: + - name: "coverage_gap" + direction: "minimize" + description: "Absolute difference between empirical marginal coverage and target 0.90 on the test split, averaged over seeds." + - name: "mean_interval_width" + direction: "minimize" + description: "Average prediction interval width on test samples, averaged over seeds." + - name: "worst_bin_miscoverage" + direction: "minimize" + description: "Maximum across 4 bins of absolute deviation between bin coverage and 0.90, measuring conditional coverage disparity." + datasets: + - name: "hetero_sine" + source: "synthetic: y = sin(2\u03c0x) + (0.1 + 0.5|x|)\u03b5, x~Uniform(-1,1), n\u22482000" + - name: "hetero_friedman1" + source: "synthetic: sklearn.datasets.make_friedman1 with multiplicative noise scale depending on x0" + - name: "diabetes" + source: "sklearn.datasets.load_diabetes (tabular regression benchmark)" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 420 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML14.json" diff --git a/tasks/ml/manifests/ML15.yaml b/tasks/ml/manifests/ML15.yaml new file mode 100644 index 0000000000000000000000000000000000000000..215b2fee682e4f8d6f2b68e617ea294b20257600 --- /dev/null +++ b/tasks/ml/manifests/ML15.yaml @@ -0,0 +1,96 @@ +# ============================================================================ +# T15 — Filter vs embedded feature selection under irrelevant-feature injection +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML15 +title: "Filter-based vs embedded L1 feature selection with injected noise features" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Feature selection on tabular classification problems is often presented as a + choice between simple univariate filters and embedded sparse models. + Univariate filters such as chi-squared, ANOVA F-score, and mutual + information are computationally cheap and easy to apply in a pipeline, but + they evaluate each feature independently and can miss interactions. Embedded + L1-regularized logistic regression performs selection during model fitting, + potentially yielding feature subsets that better align with predictive + structure when many irrelevant variables are present. + + A practical stress test is to inject synthetic irrelevant features into + otherwise standard benchmark datasets. This setup creates controlled feature + dilution while preserving original labels and base difficulty. Under such + dilution, methods that can ignore noise should maintain accuracy and avoid + selecting many synthetic features. Conversely, methods sensitive to spurious + univariate associations may degrade as noise dimension increases. + + A credible CPU-only study should compare at least three filter selectors + (mutual_info_classif, chi2, f_classif) and an embedded L1 selector in a + shared downstream classifier pipeline, evaluate on multiple sklearn + classification datasets, and report both predictive performance and selection + quality. Including multiple random seeds is important because both noise + injection and train/test splits can change apparent selector rankings. + + The key aim is not to reproduce a single published table, but to determine + whether embedded sparsity offers robustness advantages over filters as + irrelevant dimensions grow, and whether that robustness generalizes across + datasets with different feature scales and class structures. + + *Does embedded L1-logistic feature selection retain predictive performance and reject injected irrelevant features more reliably than univariate filter methods under controlled feature-noise injection?* + +hypotheses: + - id: H1 + statement: "With 5x injected irrelevant features (relative to original feature count), L1-logistic embedded selection achieves higher mean test_accuracy than each filter method (mutual_info_classif, chi2, f_classif) on at least 2 of 3 datasets, averaged over >=5 seeds." + measurable: true + - id: H2 + statement: "At fixed selected-feature budget k=20 (or all features if p<20), L1-logistic selects a lower fraction of injected irrelevant features than the average of the three filter methods on all evaluated datasets." + measurable: true + - id: H3 + statement: "From no-injection to 5x-injection settings, mean test_accuracy drop for L1-logistic is <= 3 percentage points on at least 2 of 3 datasets." + measurable: true + +experiment_design: + research_question: "Does embedded L1-logistic feature selection outperform univariate filter methods in robustness to injected irrelevant features on sklearn tabular classification datasets?" + conditions: + - name: "filter_mutual_info_k20" + description: "SelectKBest(mutual_info_classif, k=20 or p if p<20) followed by LogisticRegression classifier." + - name: "filter_chi2_k20" + description: "MinMax scaling to nonnegative domain, then SelectKBest(chi2, k=20 or p if p<20) followed by LogisticRegression classifier." + - name: "filter_f_classif_k20" + description: "SelectKBest(f_classif, k=20 or p if p<20) followed by LogisticRegression classifier." + - name: "embedded_l1_logistic" + description: "L1-penalized LogisticRegression (saga/liblinear) used as embedded selector; keep top-20 by absolute coefficient magnitude (or nonzero if <=20), retrain LogisticRegression on selected subset." + baselines: + - "filter_f_classif_k20 as a standard univariate linear-statistic baseline" + - "filter_mutual_info_k20 as a nonlinear dependency baseline" + metrics: + - name: "test_accuracy" + direction: "maximize" + description: "Held-out test accuracy averaged over >=5 seeds for each dataset and injection level." + - name: "noise_feature_selection_rate" + direction: "minimize" + description: "Fraction of selected features that come from injected irrelevant columns." + - name: "accuracy_drop_0x_to_5x_pp" + direction: "minimize" + description: "Absolute percentage-point drop in test_accuracy from 0x to 5x injection." + datasets: + - name: "breast_cancer" + source: "sklearn.datasets.load_breast_cancer" + - name: "wine" + source: "sklearn.datasets.load_wine" + - name: "digits" + source: "sklearn.datasets.load_digits" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 420 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML15.json" diff --git a/tasks/ml/manifests/ML16.yaml b/tasks/ml/manifests/ML16.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d3c238e8c61be5618f00f4c43deaaf069d594bf6 --- /dev/null +++ b/tasks/ml/manifests/ML16.yaml @@ -0,0 +1,95 @@ +# ============================================================================ +# T16 — Comparing stochastic and adversarial bandit algorithms under drift +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML16 +title: "Bandit algorithm robustness under stationary and drifting reward regimes" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Multi-armed bandit algorithms encode different assumptions about reward + generation and non-stationarity. Epsilon-greedy is simple and adaptable with + persistent exploration; UCB1 is optimism-driven and often strong in + stationary stochastic settings; Thompson sampling can be highly sample + efficient when model assumptions are matched; Exp3 is designed for + adversarial settings and may trade off stochastic efficiency for robustness. + In small CPU-constrained studies, these differences are often discussed + qualitatively but not tested with matched synthetic environments. + + A meaningful benchmark should evaluate both Bernoulli and Gaussian reward + arms, because posterior/model assumptions and noise scale can materially + change outcomes. It should also include stationary and drifting regimes: + methods tuned for fixed means can degrade when the best arm changes over + time. Regret, not just final reward, is the right primary metric because it + captures online learning efficiency throughout the horizon. + + A credible experiment here implements the four algorithms with consistent + action/reward interfaces, runs repeated simulations over multiple seeds, and + reports cumulative regret trajectories and endpoint statistics. To keep the + study CPU-friendly, horizons and arm counts should be modest (e.g., 5-10 arms, + 500-2000 steps), with vectorized numpy updates where possible. + + The key analytical value is comparative: identify where UCB1/Thompson excel + in stationary stochastic settings, and whether epsilon-greedy or Exp3 is more + resilient under drift. The objective is not reproducing a single paper, but + testing algorithm-environment fit under controlled synthetic shifts. + + *How do epsilon-greedy, UCB1, Thompson sampling, and Exp3 trade off cumulative regret across Bernoulli vs Gaussian arms and stationary vs drifting reward regimes?* + +hypotheses: + - id: H1 + statement: "In stationary Bernoulli environments, either UCB1 or Thompson sampling achieves at least 10% lower mean cumulative regret than epsilon-greedy at horizon T on at least 2 of 3 datasets (averaged over >=20 seeds)." + measurable: true + - id: H2 + statement: "In drifting environments, epsilon-greedy or Exp3 achieves lower mean cumulative regret than UCB1 on at least 2 of 3 datasets at horizon T (averaged over >=20 seeds)." + measurable: true + - id: H3 + statement: "Across all evaluated datasets, no single algorithm is best (lowest mean cumulative regret) on every dataset, indicating environment-dependent performance." + measurable: true + +experiment_design: + research_question: "How do epsilon-greedy, UCB1, Thompson sampling, and Exp3 compare in cumulative regret across stationary versus drifting synthetic bandit tasks with Bernoulli and Gaussian rewards?" + conditions: + - name: "epsilon_greedy_eps0.1" + description: "Epsilon-greedy with constant epsilon=0.1 and sample-mean value estimates." + - name: "ucb1" + description: "UCB1 with exploration bonus sqrt(2 log t / n_a)." + - name: "thompson_sampling" + description: "Thompson sampling using Beta-Bernoulli updates for Bernoulli arms and Gaussian posterior sampling (known variance assumption) for Gaussian arms." + - name: "exp3_gamma0.07" + description: "Exp3 with gamma=0.07 and importance-weighted reward estimates." + baselines: + - "epsilon_greedy_eps0.1 as simple stochastic baseline" + - "ucb1 as canonical optimism baseline" + metrics: + - name: "cumulative_regret" + direction: "minimize" + description: "Mean cumulative regret at final horizon T relative to oracle best arm per round, averaged over seeds." + - name: "instantaneous_regret_auc" + direction: "minimize" + description: "Area under per-round regret curve over time (lower is better)." + - name: "best_arm_selection_rate" + direction: "maximize" + description: "Fraction of rounds selecting the current optimal arm (for drift: time-varying optimum)." + datasets: + - name: "bernoulli_stationary_k10" + source: "synthetic: 10 Bernoulli arms with fixed means sampled in [0.05, 0.95] and sorted gap >=0.05" + - name: "bernoulli_drift_k10" + source: "synthetic: 10 Bernoulli arms with piecewise-constant means; best arm switches at predefined changepoints" + - name: "gaussian_drift_k8" + source: "synthetic: 8 Gaussian arms (sigma=1) with linearly drifting means and periodic rank reversals" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 420 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML16.json" diff --git a/tasks/ml/manifests/ML17.yaml b/tasks/ml/manifests/ML17.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6b8382b29145811b877be53dcc528f5354b80647 --- /dev/null +++ b/tasks/ml/manifests/ML17.yaml @@ -0,0 +1,97 @@ +# ============================================================================ +# T17 — Comparing topic models (LDA, NMF, LSA) on 20newsgroups subsets +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML17 +title: "Topic-model comparison on small 20newsgroups subsets: LDA vs NMF vs LSA" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Topic modeling methods are often compared using either intrinsic quality + measures (such as topic coherence) or extrinsic utility (such as how well + document representations align with known labels), but the two views can + disagree. Latent Dirichlet Allocation (LDA), Non-negative Matrix + Factorization (NMF), and Latent Semantic Analysis (LSA/SVD) each induce + different assumptions over term-document structure and may therefore rank + differently depending on metric choice. + + On CPU-constrained benchmarks, a practical study should use a compact text + corpus and a controlled preprocessing pipeline so that runtime stays short + while still yielding meaningful distinctions. A small subset of + 20newsgroups is ideal because it has accessible labels for external + clustering evaluation and enough lexical diversity for coherence analysis. + Using multiple subset difficulties (well-separated vs more confusable + categories) helps test robustness of conclusions. + + A credible experiment compares LDA, NMF, and LSA under matched topic counts + and vectorization settings, then reports both c_v-like coherence and + adjusted rand index (ARI) from document-cluster assignments against true + newsgroup labels. Because exact c_v implementations are not native in + sklearn, an explicit approximation based on sliding-window co-occurrence + and normalized PMI should be documented and applied consistently across + methods. Repeated runs for stochastic models are needed to avoid + over-interpreting single-seed noise. + + The core question is whether better intrinsic coherence implies better + label alignment on small real-world corpora, and which model offers the + best trade-off under strict CPU budgets. + + *Do LDA, NMF, and LSA produce different rankings on coherence versus ARI on small 20newsgroups subsets, and does NMF provide the strongest coherence-structure trade-off under CPU limits?* + +hypotheses: + - id: H1 + statement: "NMF achieves higher mean coherence_cv than LSA on at least 2 of 3 evaluated 20newsgroups subsets when using the same number of topics and preprocessing pipeline." + measurable: true + - id: H2 + statement: "Across the three methods (LDA, NMF, LSA), the method with the highest coherence_cv is also the highest-ARI method on no more than 1 of the 3 subsets, indicating weak metric agreement." + measurable: true + - id: H3 + statement: "LDA achieves mean ARI at least 0.03 higher than LSA on at least 2 of 3 subsets when document clusters are obtained by argmax topic assignment." + measurable: true + +experiment_design: + research_question: "How do LDA, NMF, and LSA compare on coherence_cv versus document-cluster ARI over small 20newsgroups subsets, and do intrinsic/extrinsic rankings diverge?" + conditions: + - name: "lda_k4" + description: "LatentDirichletAllocation with n_components=4, max_iter=20, learning_method='batch', CountVectorizer features, repeated over 3 seeds." + - name: "nmf_k4" + description: "NMF with n_components=4, init='nndsvda', solver='cd', max_iter=300 on TF-IDF features, repeated over 3 seeds." + - name: "lsa_k4" + description: "TruncatedSVD (LSA) with n_components=4 on TF-IDF features; document-topic embeddings clustered via argmax after non-negative shift or via KMeans(k=4) with fixed seed." + - name: "nmf_k6_sensitivity" + description: "NMF sensitivity condition with n_components=6 to test topic-count robustness for coherence and ARI trends." + baselines: + - "lsa_k4 as a linear-algebra baseline without probabilistic topic assumptions" + - "lda_k4 as a probabilistic-topic baseline" + metrics: + - name: "coherence_cv" + direction: "maximize" + description: "Approximate c_v coherence computed from top words per topic using sliding-window co-occurrence and NPMI aggregation, averaged across topics and seeds." + - name: "ari" + direction: "maximize" + description: "Adjusted Rand Index between predicted document clusters (topic argmax or equivalent) and true newsgroup labels." + - name: "runtime_sec" + direction: "minimize" + description: "Wall-clock training + inference time per condition/dataset cell on single CPU core." + datasets: + - name: "20ng_easy4" + source: "sklearn.datasets.fetch_20newsgroups with categories=['sci.space','rec.autos','comp.graphics','talk.politics.misc']" + - name: "20ng_related4" + source: "sklearn.datasets.fetch_20newsgroups with categories=['comp.graphics','comp.os.ms-windows.misc','comp.sys.ibm.pc.hardware','comp.sys.mac.hardware']" + - name: "20ng_science4" + source: "sklearn.datasets.fetch_20newsgroups with categories=['sci.space','sci.med','sci.electronics','sci.crypt']" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 720 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML17.json" diff --git a/tasks/ml/manifests/ML18.yaml b/tasks/ml/manifests/ML18.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bba773e8605410d451d887cf1e59a33c00a2d96f --- /dev/null +++ b/tasks/ml/manifests/ML18.yaml @@ -0,0 +1,95 @@ +# ============================================================================ +# T18 — Post-hoc probability calibration for sklearn classifiers +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML18 +title: "Post-hoc calibration methods for sklearn classifiers on tabular benchmarks" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Many widely used scikit-learn classifiers optimize discrimination rather + than probability quality. Random forests, gradient boosting models, and + RBF-kernel SVMs can achieve strong accuracy while output probabilities are + miscalibrated, especially on small or moderately imbalanced tabular data. + This matters in decision settings where thresholds, risk ranking, or cost- + sensitive actions depend on reliable confidence estimates. + + Post-hoc calibration methods are attractive because they can be layered onto + existing models without retraining the base learner. Platt scaling fits a + sigmoid map, isotonic regression fits a non-parametric monotone map, and + temperature scaling applies a single-parameter logit rescaling (implemented + for binary tasks via optimization on held-out logits/probabilities). These + methods differ in flexibility and overfitting risk, so their relative value + may depend on classifier family and dataset size. + + A credible CPU-scale study should compare uncalibrated outputs versus at + least three calibrators across multiple sklearn tabular datasets, using a + proper train/calibration/test protocol and repeated seeds. Because this is a + calibration-centric question, expected calibration error (ECE) and log loss + should be primary metrics, with accuracy used as a guardrail to ensure that + calibration does not degrade classification utility. + + The goal is not to reproduce a single paper result but to test whether any + one calibrator is consistently superior across heterogeneous models and + datasets under tight compute constraints. The key uncertainty is whether + method ranking is stable enough to justify a default choice. + + *Which post-hoc calibration method (Platt, isotonic, temperature) most reliably improves ECE across RF/GBM/SVM-RBF on small tabular sklearn benchmarks without materially harming accuracy?* + +hypotheses: + - id: H1 + statement: "At least one post-hoc calibrator (Platt, isotonic, or temperature) reduces ECE by at least 10% relative to the uncalibrated model for at least 2 of 3 classifiers on at least 2 of 3 datasets, averaged over >=3 seeds." + measurable: true + - id: H2 + statement: "Isotonic regression achieves lower mean ECE than Platt scaling on at least 2 of 3 datasets when calibration-set size is >=150 samples." + measurable: true + - id: H3 + statement: "Applying post-hoc calibration changes test accuracy by no more than 1.0 absolute percentage point (mean over seeds) for each classifier-dataset pair." + measurable: true + +experiment_design: + research_question: "Which post-hoc calibration method (Platt, isotonic, temperature) most consistently improves probability calibration for RF/GBM/SVM-RBF on sklearn tabular datasets while preserving accuracy?" + conditions: + - name: "uncalibrated" + description: "Base classifier probabilities/decision scores used directly with no calibration." + - name: "platt_scaling" + description: "Sigmoid calibration fit on a held-out calibration split (CalibratedClassifierCV with method='sigmoid' or equivalent)." + - name: "isotonic_regression" + description: "Isotonic calibration fit on a held-out calibration split (CalibratedClassifierCV with method='isotonic' or equivalent)." + - name: "temperature_scaling" + description: "Single temperature parameter optimized on calibration split to minimize NLL; applied to logits/scores before probability mapping." + baselines: + - "uncalibrated outputs are the primary baseline" + - "platt_scaling serves as a classic parametric calibration baseline" + metrics: + - name: "ece" + direction: "minimize" + description: "Expected Calibration Error (10-15 bins) on the held-out test split, averaged over seeds." + - name: "log_loss" + direction: "minimize" + description: "Negative log-likelihood on test probabilities." + - name: "test_accuracy" + direction: "maximize" + description: "Classification accuracy on held-out test split to verify discrimination is preserved." + datasets: + - name: "breast_cancer" + source: "sklearn.datasets.load_breast_cancer" + - name: "wine" + source: "sklearn.datasets.load_wine" + - name: "digits_binary" + source: "sklearn.datasets.load_digits with target transformed to binary (e.g., digit<5 vs >=5)" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 480 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML18.json" diff --git a/tasks/ml/manifests/ML19.yaml b/tasks/ml/manifests/ML19.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f44f8e5b6534d2ce49a6bfcc428d5b4a25e67fd5 --- /dev/null +++ b/tasks/ml/manifests/ML19.yaml @@ -0,0 +1,95 @@ +# ============================================================================ +# T19 — Semi-supervised strategies on small tabular datasets +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML19 +title: "Comparing graph- and pseudo-label-based semi-supervised learning on small tabular datasets" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Semi-supervised learning is attractive when labels are scarce but unlabeled + examples are cheap. In sklearn, LabelPropagation and LabelSpreading provide + graph-based transductive approaches, while SelfTrainingClassifier offers a + pseudo-labeling wrapper around a standard supervised base learner. These + methods rely on different assumptions: graph smoothness over neighborhoods + versus confidence-thresholded iterative self-labeling. On small tabular + datasets, their relative behavior can change quickly as the labeled fraction + drops from moderate (20%) to very low (10%). + + A useful CPU-scale study should compare these methods under the same splits, + feature preprocessing, and random seeds, with explicit control of labeled + fraction. Because the key claim of semi-supervision is label efficiency, the + experiment should include a supervised-only baseline trained on the same + labeled subset and evaluated on a fixed held-out test set. This makes gains + attributable to unlabeled-data usage rather than favorable splits. + + The study should report not only test accuracy but also balanced accuracy and + macro-F1, since small tabular datasets can have class imbalance and accuracy + alone may hide minority-class degradation. Repeating runs across several seeds + is necessary because which points are labeled strongly affects outcomes at low + label rates. + + Practically, the benchmark should stay lightweight: sklearn datasets only, + no downloads, and model choices that complete within minutes on a single CPU + core. The focus is not SOTA performance but robust relative comparisons across + label regimes and datasets. + + *How do LabelPropagation, LabelSpreading, and SelfTrainingClassifier compare in label-efficiency versus a supervised-only baseline when only 10–20% of training labels are available on small sklearn tabular datasets?* + +hypotheses: + - id: H1 + statement: "At 10% labeled data, at least one semi-supervised method (LabelPropagation, LabelSpreading, or SelfTrainingClassifier) achieves test accuracy at least 3 percentage points higher than the supervised-only baseline on at least 2 of 3 datasets, averaged over ≥5 seeds." + measurable: true + - id: H2 + statement: "Across datasets at 10% labeled data, LabelSpreading with RBF kernel attains mean test accuracy greater than or equal to LabelPropagation with RBF kernel in at least 2 of 3 datasets (seed-averaged)." + measurable: true + - id: H3 + statement: "For SelfTrainingClassifier, increasing labeled fraction from 10% to 20% improves mean test accuracy by at least 1 absolute percentage point on at least 2 of 3 datasets." + measurable: true + +experiment_design: + research_question: "How do graph-based (LabelPropagation/LabelSpreading) and pseudo-labeling (SelfTrainingClassifier) methods compare to supervised-only training under 10% and 20% labeled-data regimes on small tabular benchmarks?" + conditions: + - name: "supervised_logreg_labeled_only" + description: "LogisticRegression trained only on the labeled subset of the training split; unlabeled points ignored." + - name: "label_propagation_rbf" + description: "LabelPropagation with RBF kernel; unlabeled training labels set to -1 and transductive predictions used for test inference." + - name: "label_spreading_rbf" + description: "LabelSpreading with RBF kernel under the same masked-label protocol." + - name: "self_training_logreg" + description: "SelfTrainingClassifier wrapping LogisticRegression with confidence threshold (e.g., 0.8), fit on mixed labeled/unlabeled training data." + baselines: + - "supervised_logreg_labeled_only is the primary baseline" + - "10% labeled regime serves as a lower-label baseline versus 20% within each method" + metrics: + - name: "test_accuracy" + direction: "maximize" + description: "Classification accuracy on held-out test split, averaged over seeds." + - name: "balanced_accuracy" + direction: "maximize" + description: "Balanced accuracy on held-out test split, averaged over seeds." + - name: "macro_f1" + direction: "maximize" + description: "Macro-averaged F1 score on held-out test split, averaged over seeds." + datasets: + - name: "breast_cancer" + source: "sklearn.datasets.load_breast_cancer" + - name: "wine" + source: "sklearn.datasets.load_wine" + - name: "digits" + source: "sklearn.datasets.load_digits" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 420 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML19.json" diff --git a/tasks/ml/manifests/ML20.yaml b/tasks/ml/manifests/ML20.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7c63e4cb3f1bdbde7aef5bc0087f4ecb4ccb9e63 --- /dev/null +++ b/tasks/ml/manifests/ML20.yaml @@ -0,0 +1,102 @@ +# ============================================================================ +# T20 — Benchmarking classical time-series forecasters on synthetic seasonal series +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML20 +title: "Classical forecaster robustness on synthetic seasonal time series" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Classical univariate forecasting methods remain widely used because they are + interpretable and lightweight, yet their comparative behavior can flip under + different data-generating regimes. AR models often perform well when dynamics + are mostly autoregressive and weakly seasonal; ARIMA can absorb trend and + differencing structure; SARIMAX can explicitly represent seasonal lag effects; + ETS captures error-trend-seasonal decomposition; and Theta is often a strong + low-variance baseline. In small CPU-constrained settings, practitioners need + practical guidance about which method is robust to changing signal-to-noise + and seasonality strength. + + A synthetic benchmark is appropriate here because we can precisely control + trend slope, seasonal amplitude, and observation noise while keeping compute + modest. By generating multiple families of monthly-like series with known + periodicity (e.g., period 12), we can evaluate whether model rankings are + stable or regime-dependent. This avoids overfitting conclusions to one + real-world dataset and enables direct stress testing under high-noise versus + high-seasonality conditions. + + A credible study should compare at least four of {AR, ARIMA, SARIMAX, ETS, + Theta} on 2–3 synthetic dataset families, using rolling-origin or holdout + forecasts with a fixed horizon. It should report scale-robust error metrics + (especially sMAPE), include at least one baseline (e.g., seasonal naive), and + aggregate over multiple random seeds. The analysis should explicitly test + whether one method is consistently best overall or whether the best choice + depends on data regime. + + The key outcome is actionable selection logic: if seasonal structure is strong + and noise is low, do seasonal/state-space methods dominate; and if noise is + high, do simpler methods become competitive? These are concrete decisions an + autonomous forecasting pipeline can apply when only limited data and CPU are + available. + + *How do AR, ARIMA, SARIMAX, ETS, and Theta compare in sMAPE robustness across synthetic seasonal series with controlled trend, seasonality amplitude, and noise levels?* + +hypotheses: + - id: H1 + statement: "SARIMAX or ETS achieves the lowest mean sMAPE on at least 2 of 3 synthetic dataset families when seasonality amplitude is high (amplitude >= 8) and noise is low (sigma <= 1.0), averaged over >=5 seeds." + measurable: true + - id: H2 + statement: "Under high-noise settings (sigma >= 3.0), the sMAPE gap between the best advanced method (ARIMA/SARIMAX/ETS/Theta) and a seasonal_naive baseline is <= 5 percentage points on at least 2 of 3 dataset families." + measurable: true + - id: H3 + statement: "No single method among {AR, ARIMA, SARIMAX, ETS, Theta} ranks first in mean sMAPE on all evaluated dataset families, indicating regime-dependent winners." + measurable: true + +experiment_design: + research_question: "Which classical forecasting method is most robust in sMAPE across controlled synthetic regimes of trend, seasonality amplitude, and noise?" + conditions: + - name: "ar_lag12" + description: "Autoregressive model with lag order selected from {3,6,12} by AIC on training split." + - name: "arima_auto_small" + description: "Non-seasonal ARIMA with (p,d,q) searched over small grid p,q in [0,2], d in [0,1], selected by AIC." + - name: "sarimax_seasonal12" + description: "Seasonal ARIMA/SARIMAX with seasonal period 12 and small grid over (p,d,q)x(P,D,Q), selected by AIC." + - name: "ets_additive" + description: "Exponential smoothing with additive trend/seasonality (period 12), damped trend optional by AIC." + - name: "theta" + description: "ThetaModel forecast with default theta decomposition for univariate series." + baselines: + - "seasonal_naive (forecast y_t = y_{t-12}) as a simple seasonal baseline" + - "naive_last (random walk) as a non-seasonal baseline" + metrics: + - name: "smape" + direction: "minimize" + description: "Symmetric Mean Absolute Percentage Error on forecast horizon, averaged over seeds and series instances." + - name: "mae" + direction: "minimize" + description: "Mean Absolute Error on forecast horizon." + - name: "fit_time_sec" + direction: "minimize" + description: "Per-method wall-clock fit+forecast runtime in seconds." + datasets: + - name: "syn_low_noise_high_season" + source: "Synthetic monthly series: length 180, period 12, trend slope in [0.0,0.2], season amplitude in [8,12], gaussian noise sigma in [0.5,1.0]." + - name: "syn_medium_noise_medium_season" + source: "Synthetic monthly series: length 180, period 12, trend slope in [0.1,0.4], season amplitude in [4,8], gaussian noise sigma in [1.5,2.5]." + - name: "syn_high_noise_low_season" + source: "Synthetic monthly series: length 180, period 12, trend slope in [0.0,0.3], season amplitude in [1,4], gaussian noise sigma in [3.0,4.0]." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 720 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML20.json" diff --git a/tasks/ml/manifests/ML21.yaml b/tasks/ml/manifests/ML21.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1ef4af2557fc38d0862ffc2598f483d74d807d27 --- /dev/null +++ b/tasks/ml/manifests/ML21.yaml @@ -0,0 +1,97 @@ +# ============================================================================ +# T21 — Comparing causal structure learning algorithms on small linear-SEM DAGs +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML21 +title: "PC vs GES vs NOTEARS-linear for recovering small Gaussian linear-SEM DAGs" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Causal structure learning methods often claim asymptotic guarantees, but in + practical settings researchers usually face small graphs, finite samples, and + imperfect hyperparameter choices. For Gaussian linear structural equation + models (SEMs), three widely discussed paradigms are constraint-based (PC), + score-based (GES), and continuous optimization (NOTEARS-linear). Even when + data are generated from the model class these methods assume, finite-sample + behavior can differ sharply in edge recovery and orientation errors. + + A compact CPU-scale benchmark can isolate these differences by generating + synthetic DAGs with known ground truth over 5-15 nodes, sampling linear-Gaussian + observations, and evaluating structural recovery against the true adjacency. + This avoids data-download overhead and lets the study vary sample size and + graph density in a controlled way. The key metric is Structural Hamming + Distance (SHD), which penalizes missing, extra, and wrongly oriented edges. + + A credible study should implement all three algorithms with transparent + assumptions: PC via partial-correlation CI tests, GES via greedy score search + (e.g., BIC), and NOTEARS-linear via a differentiable acyclicity penalty and + L1 regularization. Multiple random seeds and at least two synthetic regimes + (sparser/easier vs denser/harder) are needed to reduce variance and avoid + over-interpreting one-off runs. + + Because these algorithms expose different tuning knobs (significance level, + regularization strength, score penalties), the comparison should include one + clear default setting per method plus modest tuning on validation seeds. The + resulting analysis should report SHD and at least one precision/recall-style + edge metric, then map outcomes back to explicit hypotheses about relative + ranking and sample-efficiency. + + *On small Gaussian linear-SEM DAGs, which of PC, GES, and NOTEARS-linear most reliably minimizes structural recovery error under realistic finite-sample budgets?* + +hypotheses: + - id: H1 + statement: "At n_samples=1000, NOTEARS-linear achieves lower mean SHD than PC on at least 2 of 3 synthetic DAG regimes (averaged over >=5 random DAG/data seeds per regime)." + measurable: true + - id: H2 + statement: "At n_samples=300, GES achieves mean SHD <= PC in all evaluated regimes, and strictly lower SHD in at least 1 regime." + measurable: true + - id: H3 + statement: "The best method at n_samples=1000 reduces mean SHD by at least 20% relative to the worst method in at least 2 of 3 regimes." + measurable: true + +experiment_design: + research_question: "For 5-15 node Gaussian linear-SEM DAGs, how do PC, GES, and NOTEARS-linear compare in SHD and edge recovery across sparse/medium/dense synthetic regimes and finite sample sizes?" + conditions: + - name: "pc_partial_corr" + description: "Constraint-based PC using Gaussian partial-correlation conditional independence tests with significance level alpha (default 0.01, optional small grid)." + - name: "ges_bic" + description: "Score-based greedy equivalence search using Gaussian/BIC score with forward-backward edge operations." + - name: "notears_linear_l1" + description: "Continuous optimization NOTEARS-linear with squared loss, acyclicity constraint, and L1 sparsity penalty tuned on a small lambda grid." + - name: "pc_alpha_tuned" + description: "PC variant selecting alpha from {0.005, 0.01, 0.05} by lowest validation-seed SHD, then evaluated on held-out seeds." + baselines: + - "pc_partial_corr is the classical constraint-based baseline" + - "ges_bic is the classical score-based baseline" + metrics: + - name: "shd" + direction: "minimize" + description: "Structural Hamming Distance between learned and true DAG adjacency (lower is better), averaged over seeds." + - name: "edge_f1" + direction: "maximize" + description: "F1 score on directed edge existence/orientation against ground truth adjacency." + - name: "runtime_sec" + direction: "minimize" + description: "Per-run wall-clock time in seconds to assess CPU practicality." + datasets: + - name: "synth_linear_sem_sparse" + source: "Synthetic 8-node DAGs generated via Erdos-Renyi with expected degree ~1.5; linear weights sampled from [-2,-0.5] U [0.5,2], Gaussian noise." + - name: "synth_linear_sem_medium" + source: "Synthetic 12-node DAGs with expected degree ~2.5 using same linear-Gaussian SEM generator." + - name: "synth_linear_sem_dense" + source: "Synthetic 15-node DAGs with expected degree ~3.5 using same linear-Gaussian SEM generator." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 720 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML21.json" diff --git a/tasks/ml/manifests/ML22.yaml b/tasks/ml/manifests/ML22.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0f399bb76f047d74102bcb6c9cc01b7f2aa56c7a --- /dev/null +++ b/tasks/ml/manifests/ML22.yaml @@ -0,0 +1,101 @@ +# ============================================================================ +# T22 — Active learning query strategies for logistic regression on small pools +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML22 +title: "Active learning query strategies for logistic regression on small tabular pools" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Pool-based active learning is attractive when labels are expensive but a + modest unlabeled pool is available. For linear probabilistic models such as + logistic regression, classic query policies include uncertainty sampling, + margin sampling, query-by-committee (QBC), and expected-error reduction + style lookahead approximations. These methods are often taught as broadly + useful, but on small tabular datasets their gains can be inconsistent due to + model misspecification, class imbalance, and high variance from tiny initial + labeled sets. + + A useful CPU-bounded benchmark should compare these query strategies under a + fixed annotation budget and shared learner, rather than mixing in model + architecture changes. The core signal is label efficiency: how quickly test + performance improves as labels are acquired. Because final accuracy alone can + hide early-budget differences, the study should include both + accuracy-at-budget and a budget-curve summary metric such as area under the + learning curve. + + The experiment should therefore run multiple seeds, use at least two + sklearn-resident tabular classification datasets, and enforce identical + initialization, batch size, and stopping budget across strategies. A random + query baseline is essential to determine whether sophisticated policies + provide real value beyond chance selection. A passive full-data reference is + also useful for contextualizing attainable performance ceilings under the + same logistic-regression family. + + Practical constraints matter: expected-error reduction can be expensive if + naively recomputing retrains for every candidate. A tractable variant can + evaluate a capped candidate subset per round and use one-step hypothetical + label outcomes, preserving the spirit of expected future loss minimization + while staying within single-core runtime limits. + + *Do uncertainty, margin, QBC, and approximate expected-error reduction deliver better label efficiency than random sampling for logistic regression on small tabular pools under a fixed annotation budget?* + +hypotheses: + - id: H1 + statement: "At 30% labeling budget, at least 2 of {uncertainty_sampling, margin_sampling, qbc, expected_error_reduction} achieve higher mean test accuracy than random_sampling by ≥1.5 percentage points, averaged over ≥5 seeds." + measurable: true + - id: H2 + statement: "expected_error_reduction attains the highest mean area_under_learning_curve (AULC) on at least 1 of 3 datasets, and its AULC is not lower than random_sampling on any evaluated dataset." + measurable: true + - id: H3 + statement: "qbc outperforms uncertainty_sampling in mean test accuracy at early budget (10% labels) on at least 2 of 3 datasets." + measurable: true + +experiment_design: + research_question: "Which active learning query strategy provides the best label efficiency for logistic regression on small tabular pool-based classification tasks under fixed budgets?" + conditions: + - name: "random_sampling" + description: "Baseline pool-based active learning that queries unlabeled points uniformly at random each round." + - name: "uncertainty_sampling" + description: "Queries samples with highest predictive entropy from current logistic regression model." + - name: "margin_sampling" + description: "Queries samples with smallest top-2 class probability margin (binary: |p-0.5|)." + - name: "qbc" + description: "Query-by-committee with 5 bootstrapped logistic regression committee members; selects by vote entropy/disagreement." + - name: "expected_error_reduction" + description: "Approximate one-step expected-error reduction over a capped candidate subset per round using hypothetical labels and expected future log-loss reduction." + baselines: + - "random_sampling is the primary active-learning baseline" + - "passive_full_data logistic regression reference trained once on all labels for context" + metrics: + - name: "accuracy_at_budget" + direction: "maximize" + description: "Test accuracy at 30% labeled budget, averaged over seeds." + - name: "aulc" + direction: "maximize" + description: "Area under the test-accuracy-vs-labeled-fraction curve from initial seed set to 30% budget." + - name: "early_accuracy_10pct" + direction: "maximize" + description: "Test accuracy when 10% of pool labels have been acquired." + datasets: + - name: "breast_cancer" + source: "sklearn.datasets.load_breast_cancer" + - name: "wine" + source: "sklearn.datasets.load_wine" + - name: "digits" + source: "sklearn.datasets.load_digits" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 600 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML22.json" diff --git a/tasks/ml/manifests/ML23.yaml b/tasks/ml/manifests/ML23.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d2393d8475613681795934330908d050a02563f8 --- /dev/null +++ b/tasks/ml/manifests/ML23.yaml @@ -0,0 +1,98 @@ +# ============================================================================ +# T23 — Comparing learning-to-rank approaches on synthetic query-document data +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML23 +title: "Pointwise vs pairwise vs listwise learning-to-rank on synthetic query-document benchmarks" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + Learning-to-rank (LTR) objectives are often grouped into pointwise, pairwise, + and listwise families, each optimizing a different surrogate of retrieval + quality. Pointwise methods reduce ranking to regression/classification on + individual query-document pairs; pairwise methods optimize relative order + between document pairs; listwise methods directly shape permutations or top-k + emphasis. In production IR systems, metric alignment (especially with NDCG) + is often cited as a reason to prefer pairwise/listwise objectives, but this + claim is difficult to test cleanly on public collections with many confounds. + + A compact synthetic benchmark with known latent relevance structure allows a + controlled comparison. If query-specific utility functions generate graded + relevance labels (0-4), we can produce query groups with explicit train/test + splits and evaluate whether objectives aligned with ranking structure obtain + better NDCG@10 than a strong linear pointwise baseline. Because the data are + synthetic, we can vary noise and query heterogeneity while keeping runtime low + enough for single-core CPU execution. + + A credible study should implement three distinct training paradigms in the + same feature space: (1) pointwise linear regression on grades, (2) a + RankNet-lite pairwise logistic objective over within-query document pairs, + and (3) a ListMLE-lite listwise objective using per-query permutations sorted + by true grades. Evaluation should report NDCG@10 as primary, plus at least one + secondary ranking metric and a sanity metric such as training loss. Results + should be averaged across multiple seeds and at least two synthetic dataset + regimes. + + The key scientific goal is not reproducing a specific paper, but testing + whether increasing objective-level ranking awareness yields measurable gains + in top-k ranking quality under controlled conditions and limited compute. + + *Do pairwise and listwise lightweight objectives consistently outperform a pointwise linear baseline on NDCG@10 across synthetic query-document datasets with graded relevance?* + +hypotheses: + - id: H1 + statement: "ListMLE-lite achieves higher mean NDCG@10 than pointwise linear regression on at least 2 of 3 synthetic datasets, with an absolute improvement of at least 0.03 on each winning dataset (averaged over >=3 seeds)." + measurable: true + - id: H2 + statement: "At least one of {RankNet-lite, ListMLE-lite} outperforms pointwise linear regression in mean NDCG@10 on all evaluated datasets." + measurable: true + - id: H3 + statement: "The best ranking-aware method (max of RankNet-lite and ListMLE-lite per dataset) improves mean MAP@10 over pointwise linear by at least 0.02 on at least 2 of 3 datasets." + measurable: true + +experiment_design: + research_question: "Do pairwise/listwise lightweight ranking objectives provide consistent top-k retrieval gains over a pointwise linear baseline on synthetic grouped query-document data?" + conditions: + - name: "pointwise_linear" + description: "Linear model trained pointwise on query-document feature vectors to predict graded relevance via squared error; ranking by predicted score within each query." + - name: "ranknet_lite" + description: "Pairwise logistic ranking objective on within-query document pairs using a linear scorer; optimized with mini-batch gradient descent in numpy." + - name: "listmle_lite" + description: "Listwise ListMLE-style objective with a linear scorer, optimizing likelihood of ground-truth relevance-sorted document permutations per query." + - name: "pointwise_ridge_tuned" + description: "Pointwise linear baseline with L2 regularization tuned over a small grid to ensure a competitive non-ranking-aware baseline." + baselines: + - "pointwise_linear is the primary baseline" + - "pointwise_ridge_tuned is a strengthened linear baseline" + metrics: + - name: "ndcg_at_10" + direction: "maximize" + description: "Mean NDCG@10 across test queries; primary metric." + - name: "map_at_10" + direction: "maximize" + description: "Mean Average Precision@10 across test queries using relevance>=1 as relevant." + - name: "pairwise_accuracy" + direction: "maximize" + description: "Fraction of correctly ordered document pairs within test queries (based on graded relevance order)." + datasets: + - name: "synth_ltr_easy" + source: "Synthetic generator: 80 queries x 25 docs/query, 20 dense features, low noise, relevance grades 0-4 from latent linear utility." + - name: "synth_ltr_noisy" + source: "Synthetic generator: 100 queries x 30 docs/query, 25 features, higher Gaussian noise and feature corruption, grades 0-4." + - name: "synth_ltr_sparse" + source: "Synthetic generator: 90 queries x 20 docs/query, 40 features with sparsity mask, query-specific weight drift, grades 0-4." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 480 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML23.json" diff --git a/tasks/ml/manifests/ML24.yaml b/tasks/ml/manifests/ML24.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6682481db93d0883c1e0aeee796ee456741ae295 --- /dev/null +++ b/tasks/ml/manifests/ML24.yaml @@ -0,0 +1,98 @@ +# ============================================================================ +# T24 — Benchmarking online learning algorithms under concept drift +# ---------------------------------------------------------------------------- +# Unlike paper_replication's P01-P07, the "synthesis" here frames a research +# QUESTION rather than a known paper's method. The model must design the +# experiment (conditions, metrics, datasets) — we only commit to what a +# competent study of this topic would include and what the rubric expects. +# ============================================================================ + +id: ML24 +title: "Online binary classification under concept drift: SGD, PA, NB, and FTRL" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +# The "synthesis" plays the role of the upstream briefing: research question, +# background, why the question matters, what "a reasonable experiment" looks +# like. It deliberately does NOT pre-specify a single method to reproduce. +synthesis: | + In streaming classification, data arrive sequentially and model updates must + be cheap and immediate. Under concept drift, the relationship between + features and labels changes over time, so static train/test evaluation can + be misleading. Prequential (test-then-train) evaluation better reflects + deployment: each incoming point is first predicted, then used for an update. + This setting creates practical trade-offs among stability, adaptability, and + computational cost. + + Several lightweight online learners in sklearn-style workflows represent + different adaptation biases. SGD logistic regression can track drift via + gradient updates but may require careful learning-rate choices. Passive- + Aggressive updates can react strongly to mistakes and often adapt quickly to + abrupt shifts. Online Naive Bayes is extremely cheap and robust but may lag + when feature dependence structure changes. A simple FTRL-style proximal + logistic update (implemented with diagonal accumulators) offers adaptive + per-feature learning rates and implicit regularization. + + A credible CPU-only benchmark should compare these methods on at least one + synthetic abrupt-drift stream and one real sklearn dataset converted to a + stream with induced drift (e.g., blockwise class-prior or feature transform + shift). It should report prequential accuracy as the primary metric, plus at + least one drift-sensitive secondary metric such as post-drift recovery and + cumulative log loss. Multi-seed runs are important because stream order and + drift-point randomness can materially change outcomes. + + The study should also verify that drift actually hurts models by comparing a + no-drift control stream against drifted streams, and should discuss which + algorithms recover fastest after drift while maintaining overall accuracy. + This makes the benchmark more diagnostic than a single aggregate score. + + *Which lightweight online learner provides the best trade-off between overall prequential accuracy and adaptation speed after concept drift in binary streams?* + +hypotheses: + - id: H1 + statement: "On drifted streams, Passive-Aggressive or FTRL achieves higher prequential_accuracy than online Naive Bayes by at least 0.02 absolute on at least 2 of 3 datasets, averaged over >=5 seeds." + measurable: true + - id: H2 + statement: "For at least 2 of 3 drifted datasets, the best post-drift recovery accuracy in a fixed window of 200 samples after each drift point is achieved by either Passive-Aggressive or FTRL." + measurable: true + - id: H3 + statement: "For SGD logistic and Passive-Aggressive, prequential_accuracy on a drifted version of each stream is at least 0.03 lower than on its matched no-drift control, on at least 2 of 3 datasets." + measurable: true + +experiment_design: + research_question: "Which online method among SGD logistic, Passive-Aggressive, online Naive Bayes, and FTRL-style logistic best balances prequential accuracy and post-drift recovery on binary data streams with concept drift?" + conditions: + - name: "sgd_logistic" + description: "SGDClassifier with log_loss and partial_fit in prequential test-then-train loop." + - name: "passive_aggressive" + description: "PassiveAggressiveClassifier with partial_fit in the same loop." + - name: "online_naive_bayes" + description: "GaussianNB updated incrementally via partial_fit." + - name: "ftrl_prox_logistic" + description: "Custom numpy FTRL-proximal logistic regression with diagonal accumulator and L1/L2 regularization." + baselines: + - "online_naive_bayes as a simple low-cost baseline" + - "sgd_logistic as a standard linear online-learning baseline" + metrics: + - name: "prequential_accuracy" + direction: "maximize" + description: "Mean test-then-train accuracy over the full stream, averaged over 5 seeds." + - name: "post_drift_recovery_accuracy" + direction: "maximize" + description: "Accuracy in the first 200 samples after each drift point, averaged across drift events and seeds." + - name: "prequential_log_loss" + direction: "minimize" + description: "Cumulative mean log loss computed prequentially when probabilistic outputs are available (or clipped score-to-prob mapping)." + datasets: + - name: "synthetic_abrupt_drift" + source: "numpy synthesis (piecewise make_classification with coefficient/sign flips at fixed indices)" + - name: "breast_cancer_stream" + source: "sklearn.datasets.load_breast_cancer transformed into a repeated/shuffled stream with blockwise feature scaling shift" + - name: "spambase_like_synthetic" + source: "numpy synthesis with sparse informative features and class-prior shift across blocks" + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 420 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML24.json" diff --git a/tasks/ml/manifests/ML25.yaml b/tasks/ml/manifests/ML25.yaml new file mode 100644 index 0000000000000000000000000000000000000000..44d155bb708a262d023dff39e9e00e46129ec057 --- /dev/null +++ b/tasks/ml/manifests/ML25.yaml @@ -0,0 +1,101 @@ +# ============================================================================ +# T25 — Reservoir computing vs MLP vs Gaussian Process on Lorenz-63 +# ---------------------------------------------------------------------------- +# This is the "experimental spirit" exemplar — the ARC-Bench slot meant to +# test whether autoclaw can design a non-boilerplate study, not just shuffle +# standard sklearn classifiers. The topic forces the agent to (a) integrate +# Lorenz-63 from scratch, (b) implement a reservoir (echo-state network) +# from scratch (sklearn has none), (c) benchmark it against a matched- +# parameter MLP and a Gaussian Process at small training-data sizes, and +# (d) report a dynamics-aware metric (valid prediction time) in addition to +# raw RMSE. Everything stays CPU-friendly in <15 min. +# ============================================================================ + +id: ML25 +title: "Reservoir computing vs MLP vs GP for short-horizon Lorenz-63 forecasting" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +synthesis: | + Reservoir computing (a.k.a. echo state networks, ESN) is a lightweight + approach for learning dynamical systems: a fixed random recurrent network + projects the input into a high-dimensional reservoir state, and only a + linear readout layer is trained. For chaotic systems, ESNs have repeatedly + been reported to match or outperform trainable recurrent nets on + short-horizon trajectory prediction, despite training only a ridge + regression on the readout. + + A credible CPU-scale study of this claim must compare (i) a reservoir + network with a random sparse internal matrix and fixed spectral radius, + (ii) a parameter-matched fully-connected MLP that reads a fixed window of + past states, and (iii) a Gaussian Process regressor with an RBF kernel on + the same windowed input. The target signal is Lorenz-63 integrated at + dt=0.02 with the classical (σ=10, ρ=28, β=8/3) parameters. Training-set + sizes N ∈ {200, 500, 1000, 2000} are small enough that the standard + reservoir-computing folklore — "ESNs win when data is scarce" — can + actually be tested rather than assumed. + + The key dynamics-aware metric is the *valid prediction time* (VPT): + the first time step at which the normalized prediction error exceeds a + threshold (commonly 0.4 of the attractor standard deviation). Unlike + per-step RMSE, VPT tracks how long a model's forecast remains useful on + the chaotic attractor. Reporting both RMSE and VPT reveals whether any + observed "superiority" is numerical or dynamical. + + The research question is: *does a small reservoir (N_res ≤ 500 units) beat + a matched-parameter MLP and a Gaussian Process on valid prediction time + for Lorenz-63, and at which training-set size does the gap appear?* + +hypotheses: + - id: H1 + statement: "An echo-state network with spectral radius ≈ 0.9 and ≤ 500 reservoir units achieves a longer mean Valid Prediction Time (VPT ≥ 1.2×) than a parameter-matched MLP on Lorenz-63 at training-set sizes N ≤ 500." + measurable: true + - id: H2 + statement: "The Gaussian Process regressor is competitive with (VPT within ±20% of) the ESN at N=200 but is decisively beaten (ESN VPT at least 2× the GP's) at N=2000, reflecting the GP's O(N^3) training bottleneck and the ESN's ability to exploit more data without re-inverting a Gram matrix." + measurable: true + - id: H3 + statement: "Per-step RMSE at one-step-ahead prediction is NOT a reliable proxy for VPT: at least one condition exists where model A has lower one-step RMSE than model B but a shorter VPT, showing that per-step error understates the divergence on chaotic attractors." + measurable: true + +experiment_design: + research_question: "On Lorenz-63 short-horizon forecasting, does a small echo-state network achieve a longer valid prediction time than a parameter-matched MLP and a Gaussian Process regressor, and how does the ranking depend on training-set size?" + conditions: + - name: "esn_N200" + description: "Echo-state network with 300 reservoir units, spectral radius 0.9, input scaling 1.0, leak rate 0.3, ridge regression readout (alpha=1e-5). Trained on N=200 consecutive Lorenz-63 samples." + - name: "esn_N500" + description: "Same ESN configuration, N=500 training samples." + - name: "esn_N1000" + description: "Same ESN configuration, N=1000 training samples." + - name: "esn_N2000" + description: "Same ESN configuration, N=2000 training samples." + - name: "mlp_N{200,500,1000,2000}" + description: "Fully-connected MLP with 2 hidden layers whose total parameter count is within ±10% of the ESN readout parameter count. Reads a 5-step window of past (x,y,z) states as input. Adam optimizer, early stopping on a 10% validation split." + - name: "gp_N{200,500,1000,2000}" + description: "Gaussian Process regressor with an RBF kernel and WhiteNoise term, hyperparameters optimised by marginal likelihood maximisation. Reads the same 5-step window as the MLP. At N=2000 the GP may time out or run out of memory; record the failure." + baselines: + - "Persistence baseline: predicted next state = previous state. Establishes a floor for VPT." + - "Linear autoregressive baseline: one-step-ahead linear regression on the 5-step window. Establishes whether the non-linear methods' gains are real." + metrics: + - name: "valid_prediction_time" + direction: "maximize" + description: "First forecast step (in units of integration dt) at which the normalized L2 error exceeds 0.4 × attractor std. Averaged over 10 random initial conditions on an independent test trajectory." + - name: "one_step_rmse" + direction: "minimize" + description: "Root-mean-square error of one-step-ahead prediction on the test trajectory." + - name: "train_wall_clock_sec" + direction: "minimize" + description: "Wall-clock time to fit the model, including hyperparameter optimisation where applicable." + - name: "param_count" + direction: "report" + description: "Number of trainable parameters. Must be reported to demonstrate that the MLP is parameter-matched to the ESN." + datasets: + - name: "lorenz63_train" + source: "synthetic — integrate dxdt=σ(y-x), dy/dt=x(ρ-z)-y, dz/dt=xy-βz at dt=0.02 with σ=10, ρ=28, β=8/3. Use scipy.integrate.solve_ivp or a hand-rolled RK4." + - name: "lorenz63_test" + source: "independent Lorenz-63 trajectory (different IC, 10× longer warmup) with 10 evaluation windows for VPT averaging." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 720 + +rubric_path: "experiments/arc_bench/config/ml/rubrics/ML25.json" diff --git a/tasks/ml/rubrics/ML01.json b/tasks/ml/rubrics/ML01.json new file mode 100644 index 0000000000000000000000000000000000000000..f00b8137148008e3cf98efe4b622c1127a868fd2 --- /dev/null +++ b/tasks/ml/rubrics/ML01.json @@ -0,0 +1,117 @@ +{ + "id": "ml01-root", + "requirements": "A credible experiment studying whether dropout-variant choice affects calibration more than accuracy on tabular classification: the main dropout variants are implemented, evaluation covers multiple sklearn benchmarks with reasonable seed coverage, and the writeup ties the numeric findings to the three hypotheses in direction and magnitude.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Partial but well-motivated evidence deserves partial credit; rigid wording or name mismatches should not penalize a substantively correct experiment.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml01-code", + "requirements": "The dropout conditions are implemented in a way that allows a meaningful calibration comparison.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml01-code-variants", + "requirements": "The submission implements the main dropout variants relevant to the hypotheses \u2014 typically a no-dropout baseline, a standard element-wise dropout at one or more rates, and at least one stochastic-test-time / MC-style dropout variant \u2014 as distinct code paths within a shared MLP architecture.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml01-code-mlp", + "requirements": "The model is a shallow tabular MLP (a small number of hidden layers at modest width) trained with a standard supervised classification objective, appropriate for the sklearn benchmarks used.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "ml01-code-datasets", + "requirements": "The submission loads multiple tabular sklearn datasets (e.g., from {breast_cancer, wine, digits} or comparable benchmarks) and uses a reasonable train/test split.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml01-code-mc-pass", + "requirements": "The MC-dropout-style condition performs several stochastic forward passes at test time and averages the predicted probabilities, rather than collapsing to a single deterministic forward pass.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml01-exec", + "requirements": "Execution produces accuracy and calibration numbers per condition that are adequate to evaluate the hypotheses.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml01-exec-metrics", + "requirements": "Execution produces a machine-readable metrics artifact (e.g., results/metrics.json or stage-14/experiment_summary.json) with numeric accuracy and a calibration metric such as ECE, covering the implemented conditions on at least one dataset. Other calibration metrics (NLL, Brier) may substitute or supplement ECE.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml01-exec-seeds", + "requirements": "Reported metrics are aggregated over multiple random seeds per (condition, dataset) cell with some form of dispersion reporting (std, stderr, CI, or min/max across seeds). More seeds are better, but a small-but-honest seed count with reported variance is preferable to a single deterministic run.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml01-results", + "requirements": "The results analysis addresses the three hypotheses with quantitative evidence and a clear narrative.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml01-result-h1", + "requirements": "The submission compares calibration (ECE or analogous metric) between an MC/variational-style dropout and a standard dropout across the evaluated datasets, and conveys whether MC-style calibration tends to be better \u2014 judge whether the evidence is consistent with H1, refutes it, or is inconclusive, based on the reported numbers.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml01-result-h2", + "requirements": "The submission discusses whether accuracy differences among dropout variants are small on datasets where all methods achieve high accuracy \u2014 i.e., whether calibration is the discriminative axis rather than accuracy. Exact percentage-point thresholds are not required; a qualitative comparison grounded in the reported numbers suffices.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml01-result-h3", + "requirements": "The submission compares the no-dropout baseline against at least one dropout variant on calibration and conveys whether the baseline is clearly worse, comparable, or better.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml01-result-writeup", + "requirements": "The README or writeup describes the method and setup, presents the key accuracy and calibration numbers, and conveys per-hypothesis outcomes (supported / refuted / inconclusive) with appropriate caveats on seed count, dataset scope, or calibration-metric choice. No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML02.json b/tasks/ml/rubrics/ML02.json new file mode 100644 index 0000000000000000000000000000000000000000..3f09705754d9fd07446422a8f66aa742aa642d86 --- /dev/null +++ b/tasks/ml/rubrics/ML02.json @@ -0,0 +1,109 @@ +{ + "id": "ml02-root", + "requirements": "A credible experiment studying bagging vs boosting vs stacking for noisy non-linear regression: the main ensemble families are implemented, execution covers multiple regression datasets with reasonable seed coverage, and the analysis addresses H1/H2/H3 using RMSE-centered evidence.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Condition-name variants (e.g., 'RandomForest' vs 'bagging_random_forest') should not be penalized when the underlying method is clearly the intended one.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml02-code", + "requirements": "The regression ensemble conditions and datasets are implemented in a way that supports a fair comparison.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml02-code-conditions", + "requirements": "The submission implements the main ensemble families relevant to the hypotheses \u2014 a bagging-style model (e.g., RandomForestRegressor), a boosting-style model (e.g., GradientBoostingRegressor), and a stacking-style model (e.g., StackingRegressor) \u2014 alongside a non-ensemble baseline such as a single DecisionTreeRegressor. Equivalent well-motivated choices are acceptable.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml02-code-datasets", + "requirements": "The submission uses multiple regression datasets that allow a noise-robustness comparison \u2014 for example, friedman1 at varying noise levels and/or diabetes from sklearn \u2014 with a reasonable train/test split.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml02-code-setup", + "requirements": "The pipeline uses a consistent preprocessing and evaluation setup across conditions, with controlled random-state handling and no obvious test-set leakage into training or stacking meta-features.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml02-exec", + "requirements": "Execution logs the core metrics needed to evaluate the hypotheses.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml02-exec-metrics", + "requirements": "Execution produces a machine-readable metrics artifact containing numeric RMSE for the implemented (dataset, condition) cells, plus at least one complementary error metric (MAE or R\u00b2). Complete coverage is preferred; partial coverage should be scored proportional to how much of the hypothesis-relevant grid is populated.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml02-exec-seeds", + "requirements": "Reported dataset-condition results are aggregated over multiple random seeds with some form of dispersion reporting (std, stderr, CI, or min/max). More seeds are better, but an honest small-seed run with reported variance is preferable to a single deterministic run.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml02-results", + "requirements": "Results analysis addresses all three hypotheses with quantitative evidence.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml02-result-h1", + "requirements": "The submission compares bagging (e.g., Random Forest) vs boosting (e.g., GBRT) on RMSE in a high-noise regression regime and conveys whether bagging is clearly better, clearly worse, or comparable \u2014 judge directionally against H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml02-result-h2", + "requirements": "The submission conveys whether ensembles deliver a meaningful RMSE improvement over the single-tree baseline on most of the evaluated datasets. Exact percentage thresholds are not required; a clear comparison grounded in the reported numbers suffices.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml02-result-h3", + "requirements": "The submission ranks ensemble and baseline conditions by RMSE across the evaluated datasets and discusses whether stacking is competitive (best or near-best on some datasets, not systematically worst) \u2014 judge directionally against H3.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml02-result-writeup", + "requirements": "The README or writeup describes the methods and setup, presents the key RMSE/MAE/R\u00b2 findings, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations. No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML03.json b/tasks/ml/rubrics/ML03.json new file mode 100644 index 0000000000000000000000000000000000000000..678b11dd82f96f4564fa743520192a4d5386031e --- /dev/null +++ b/tasks/ml/rubrics/ML03.json @@ -0,0 +1,109 @@ +{ + "id": "ml03-root", + "requirements": "A credible experiment comparing Nelder-Mead, Powell, and CMA-ES on non-convex benchmark functions: the optimizers are implemented under matched evaluation budgets, execution covers multiple benchmark functions with repeated random starts, and results address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. If the submission uses different optimizer names or slightly different benchmark functions but addresses the same scientific question, credit it accordingly.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml03-code", + "requirements": "The optimization methods and benchmark setup are implemented in a way that supports a fair comparison.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml03-code-methods", + "requirements": "The submission implements distinct optimizer code paths for a local-simplex method (e.g., Nelder-Mead), a direction-set method (e.g., Powell), and an evolution-strategy method (e.g., CMA-ES or a well-motivated substitute such as a random-search baseline), rather than aliases of a single optimizer.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml03-code-functions", + "requirements": "The submission defines multiple non-convex benchmark functions (e.g., Rastrigin, Rosenbrock, Ackley at a moderate dimension such as 10D) with bounded domains and deterministic objective evaluation.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml03-code-budget", + "requirements": "A shared function-evaluation budget (maxfev or equivalent) is enforced across optimizers so comparisons are budget-matched.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml03-exec", + "requirements": "Execution uses repeated random starts and produces comparable benchmark metrics.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml03-exec-runs", + "requirements": "Execution uses multiple random starts per (optimizer, function) cell across the evaluated benchmark functions. More starts are better for reducing variance, but an honest small-repetition run with reported dispersion is preferable to a single start.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml03-exec-metrics", + "requirements": "Execution produces a machine-readable metrics artifact with numeric best-objective-value (the primary optimization metric), a wall-clock runtime measure (e.g., median runtime), and a success-rate-style metric (fraction of runs reaching a threshold f(x) \u2264 \u03b5) per optimizer and function.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml03-results", + "requirements": "The reported results address all three hypotheses and include a clear narrative.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml03-result-h1", + "requirements": "The submission compares mean best-objective-values across the optimizers for each function and conveys whether CMA-ES (or its stand-in) tends to outperform the local methods on the multimodal functions \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml03-result-h2", + "requirements": "The submission compares runtime between Powell and CMA-ES across the evaluated functions and conveys whether Powell tends to finish meaningfully faster under the matched budget.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml03-result-h3", + "requirements": "The submission compares success-rate on a hard multimodal function (e.g., Rastrigin) between a local method (e.g., Nelder-Mead) and CMA-ES and conveys whether CMA-ES has a clearly higher success rate.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml03-result-writeup", + "requirements": "The README or writeup describes the benchmark setup and metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as budget size, dimensionality, CMA-ES implementation simplifications, and seed variance. No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML04.json b/tasks/ml/rubrics/ML04.json new file mode 100644 index 0000000000000000000000000000000000000000..2f258855a33f3a490b635940bbca267478044ac9 --- /dev/null +++ b/tasks/ml/rubrics/ML04.json @@ -0,0 +1,109 @@ +{ + "id": "ml04-root", + "requirements": "A credible experiment studying how feature scaling (standard, min-max, robust, or none) affects KNN classification on datasets with different distributions: scaling conditions are implemented consistently, execution covers multiple datasets and random splits, and conclusions address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. If the submission uses alternative but well-motivated scalers or datasets that test the same scientific question, credit it accordingly.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml04-code", + "requirements": "The scaling conditions and KNN setup are implemented in a way that supports a fair comparison.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml04-code-conditions", + "requirements": "The submission implements the relevant scaling conditions \u2014 typically no-scaling, standard-scaler, min-max-scaler, and robust-scaler \u2014 as distinct code paths sharing the same KNN configuration.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml04-code-datasets", + "requirements": "The submission uses multiple datasets including at least one real sklearn dataset and at least one synthetic/outlier-heavy dataset, with a reasonable train/test split.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml04-code-consistency", + "requirements": "The implementation keeps KNN settings identical across scaling conditions and fits preprocessing only on training data within each split.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml04-exec", + "requirements": "Execution produces benchmark metrics adequate to evaluate the hypotheses.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml04-exec-metrics", + "requirements": "Execution produces a machine-readable metrics artifact with numeric test accuracy and an additional metric such as macro-F1 for each evaluated (condition, dataset) cell.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml04-exec-splits", + "requirements": "Reported metrics are aggregated over multiple random splits or seeds per (condition, dataset) with some dispersion measure (std or CI). More splits are better, but an honest small-split run with variance reported is preferable to a single split.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml04-results", + "requirements": "The analysis evaluates all three hypotheses and presents interpretable findings.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml04-result-h1", + "requirements": "The submission compares robust-scaling vs standard-scaling + KNN on the outlier-heavy dataset and conveys whether robust-scaling shows a meaningful accuracy advantage. Exact percentage-point thresholds are not required.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml04-result-h2", + "requirements": "The submission discusses whether scaler choice materially affects KNN accuracy on at least one real dataset \u2014 i.e., whether the best-vs-worst-scaler gap is non-trivial and of practical significance.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml04-result-h3", + "requirements": "The submission compares the no-scaling baseline against the scaled conditions and conveys whether no-scaling tends to be clearly suboptimal across the evaluated datasets.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml04-result-writeup", + "requirements": "The README or writeup describes setup and datasets, reports the key metric values per condition, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as dataset scope, split count, or KNN-hyperparameter sensitivity. No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML05.json b/tasks/ml/rubrics/ML05.json new file mode 100644 index 0000000000000000000000000000000000000000..25cd3913b61c8991dd0a64ab84a55db12da6a7bf --- /dev/null +++ b/tasks/ml/rubrics/ML05.json @@ -0,0 +1,109 @@ +{ + "id": "ml05-root", + "requirements": "A credible experiment studying how linear (PCA) vs non-linear (t-SNE / UMAP-like) dimensionality reduction preserves cluster structure on synthetic high-dimensional datasets: methods share a consistent evaluation pipeline, execution covers multiple datasets with repeated seeds, and conclusions address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. UMAP may be unavailable in the environment \u2014 a documented fallback (e.g., Isomap, LLE) that tests the same scientific question should be credited rather than penalized.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml05-code", + "requirements": "The dimensionality-reduction and clustering conditions are implemented in a way that supports a fair comparison.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml05-code-conditions", + "requirements": "The submission implements the main reduction conditions relevant to the hypotheses \u2014 a linear method (PCA), at least one nonlinear method (t-SNE, UMAP, or a documented substitute such as Isomap), and preferably a no-reduction identity baseline \u2014 as distinct code paths.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml05-code-data", + "requirements": "The submission generates multiple synthetic high-dimensional datasets that stress different geometries (e.g., isotropic blobs, anisotropic blobs, embedded non-linear manifolds such as moons) with reproducible seeds and retained ground-truth labels.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml05-code-pipeline", + "requirements": "A consistent post-reduction clustering backend (e.g., KMeans with k matching the true cluster count) and a shared metric-computation pipeline are applied uniformly across conditions.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml05-exec", + "requirements": "Execution outputs quantitative cluster-quality and runtime metrics.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml05-exec-metrics", + "requirements": "Execution produces a machine-readable metrics artifact with numeric cluster-quality scores (silhouette, ARI, or equivalents) and a wall-clock reduction-time measure per (dataset, condition) cell.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml05-exec-seeds", + "requirements": "Reported metrics are aggregated over multiple random seeds per (dataset, condition) with some dispersion measure. More seeds are better, but an honest small-seed run with variance reported is preferable to a single run.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml05-results", + "requirements": "Results are analyzed against the hypotheses with interpretable evidence.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml05-result-h1", + "requirements": "The submission compares nonlinear methods vs PCA on the nonlinear-manifold dataset using a cluster-quality metric (silhouette or ARI) and conveys whether the nonlinear methods produce meaningfully better cluster separation \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml05-result-h2", + "requirements": "The submission discusses PCA vs the best nonlinear method on the linearly-separable blobs dataset and conveys whether PCA is competitive (comparable quality) in that regime.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml05-result-h3", + "requirements": "The submission reports runtime rankings across methods and conveys whether PCA is markedly faster than the nonlinear methods on most datasets.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml05-result-writeup", + "requirements": "The README or writeup describes setup, reports the key metric numbers, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as synthetic-only scope, hyperparameter sensitivity, or substitutions for unavailable methods. No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML06.json b/tasks/ml/rubrics/ML06.json new file mode 100644 index 0000000000000000000000000000000000000000..d979c28c13d5a33008170ea8972896c54d78c15c --- /dev/null +++ b/tasks/ml/rubrics/ML06.json @@ -0,0 +1,117 @@ +{ + "id": "ml06-root", + "requirements": "A credible experiment studying whether adaptive learning-rate schedules improve logistic-regression convergence on binary classification: schedule conditions are implemented as distinct update rules, runs are executed on multiple datasets with multiple seeds, and results address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Equivalent schedules (e.g., a different decay parameterization) should be credited when the underlying mechanism is clearly the intended one.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml06-code", + "requirements": "The logistic-regression training framework and learning-rate schedule variants are implemented in a way that allows a meaningful convergence comparison.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml06-code-schedules", + "requirements": "The submission implements a fixed-learning-rate baseline and multiple adaptive schedules (e.g., step-decay, exponential-decay, cosine-annealing, cosine-warm-restarts, or comparable alternatives) as distinct update rules, not duplicated code paths.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml06-code-model", + "requirements": "A binary logistic-regression objective (sigmoid + log-loss) is trained via iterative gradient-based optimization with per-epoch control, enabling convergence-epoch tracking.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml06-code-data", + "requirements": "The submission uses multiple datasets (including at least one sklearn built-in or synthetic classification set), performs train/validation/test splitting, and applies feature scaling consistently.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml06-exec", + "requirements": "Execution produces convergence and quality metrics with reasonable seed coverage.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml06-exec-metrics", + "requirements": "Execution produces a machine-readable metrics artifact with numeric convergence-epochs (or equivalent convergence measure) and validation log-loss for each implemented (dataset, condition) cell.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml06-exec-seeds", + "requirements": "Reported metrics are aggregated over multiple random seeds per (dataset, condition) with some dispersion measure. More seeds are better, but honest small-seed runs with variance reported are preferable to single deterministic runs.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml06-exec-auc", + "requirements": "Execution also reports a final predictive-quality metric (e.g., test ROC-AUC) for each condition on at least one dataset, so readers can check that convergence-speed gains do not collapse classification quality.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml06-results", + "requirements": "The analysis evaluates all three hypotheses and discusses the speed-vs-quality trade-off.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml06-result-h1", + "requirements": "The submission compares cosine-style schedules against the fixed-rate baseline on convergence-epochs across the evaluated datasets and conveys whether cosine schedules converge meaningfully faster \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml06-result-h2", + "requirements": "The submission reports the spread of final validation log-loss among the adaptive schedules and conveys whether the best-vs-worst gap is small (i.e., final-quality differences are minor once converged).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml06-result-h3", + "requirements": "The submission compares cosine-warm-restarts against exponential-decay (or equivalent schedules) on convergence speed and final ROC-AUC and conveys the qualitative outcome for H3.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml06-result-writeup", + "requirements": "The README or writeup describes setup and key metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as convergence-threshold choice, seed count, or dataset scope. No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML07.json b/tasks/ml/rubrics/ML07.json new file mode 100644 index 0000000000000000000000000000000000000000..d3d0795cbff4eaeabd969d7ee13b2741eb23b2cd --- /dev/null +++ b/tasks/ml/rubrics/ML07.json @@ -0,0 +1,109 @@ +{ + "id": "ml07-root", + "requirements": "A credible experiment studying text-feature-extraction trade-offs (count, TF-IDF, hashing) with Naive Bayes and linear SVM for document classification: multiple vectorizer+classifier pipelines are implemented, runs execute on multiple datasets, and results address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Equivalent vectorizer/classifier substitutes (e.g., SGDClassifier in place of LinearSVC) should be credited when the scientific question is preserved.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml07-code", + "requirements": "Feature extractors and classifiers are implemented as distinct pipeline code paths enabling fair comparison.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml07-code-conditions", + "requirements": "The submission implements multiple text-classification pipelines combining a vectorizer (count, TF-IDF, or hashing) with a classifier (Multinomial Naive Bayes or LinearSVC-style linear model) as distinct code paths rather than a single reused model without feature changes.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml07-code-datasets", + "requirements": "The submission uses multiple document datasets (including at least one 20newsgroups subset or comparable) with explicit train/test usage and preprocessing applied consistently across conditions.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml07-code-fairness", + "requirements": "The experiment keeps comparable tokenization / n-gram choices across vectorizers where applicable and uses reasonable, shared hyperparameters (e.g., C for LinearSVC, alpha for MNB) so condition comparisons are fair.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml07-exec", + "requirements": "Execution logs predictive-quality and efficiency metrics per condition.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml07-exec-metrics", + "requirements": "Execution produces a machine-readable metrics artifact with numeric macro-F1 (or equivalent) and a wall-clock fit+predict time measure for each implemented (condition, dataset) cell.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml07-exec-repeats", + "requirements": "Execution repeats evaluation where randomness exists (e.g., synthetic-data generation or classifier random_state) across multiple seeds and reports dispersion for macro-F1. Deterministic pipelines may report a single run if no stochasticity is involved.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml07-results", + "requirements": "Results directly address H1/H2/H3 with an interpretable narrative of accuracy-efficiency trade-offs.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml07-result-h1", + "requirements": "The submission ranks conditions by macro-F1 per dataset and conveys whether TF-IDF + linear SVM (or equivalent) tends to be the top pipeline \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml07-result-h2", + "requirements": "The submission compares the runtime of hashing-vectorizer-based pipelines vs TF-IDF-based ones and conveys whether hashing yields a meaningful runtime reduction.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml07-result-h3", + "requirements": "The submission compares count + Multinomial NB against TF-IDF + linear SVM on macro-F1 and conveys whether count-MNB is approximately competitive on at least one dataset \u2014 judge directionally against H3.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml07-result-writeup", + "requirements": "The README or writeup describes methods and datasets, reports the key metric numbers, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as dataset scope, preprocessing choices, timing noise, and hyperparameter coverage. No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML08.json b/tasks/ml/rubrics/ML08.json new file mode 100644 index 0000000000000000000000000000000000000000..35e3412038b8385fa3a4521d145298932dcf5392 --- /dev/null +++ b/tasks/ml/rubrics/ML08.json @@ -0,0 +1,101 @@ +{ + "id": "ml08-root", + "requirements": "A credible experiment studying imbalance-handling strategies (e.g., class weights, SMOTE, random over-/under-sampling) for binary classification: methods are implemented as distinct conditions, execution covers multiple datasets with multiple seeds, and results address H1/H2/H3 using balanced-accuracy-centered analysis.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. If SMOTE is implemented via a simplified interpolation recipe rather than the imblearn package, credit the scientific intent; alternative imbalance strategies that test the same question should also be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml08-code", + "requirements": "The imbalance-handling conditions are implemented in a way that supports a fair comparison under a shared classifier.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml08-code-conditions", + "requirements": "The submission implements multiple imbalance-handling strategies \u2014 typically including a no-handling baseline, class-weight balancing, an oversampling variant (random or SMOTE-like), and an undersampling variant \u2014 as distinct code paths under a common binary classifier.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml08-code-smote", + "requirements": "If a SMOTE-style condition is claimed, synthetic minority samples are generated by interpolation between minority neighbors (not mere duplication) and applied only to training data. A simplified SMOTE-style implementation is acceptable if the intent is clear.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml08-code-data", + "requirements": "The submission uses multiple datasets (loaded or synthesized via sklearn utilities) and enforces an imbalanced binary class distribution in training data.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml08-exec", + "requirements": "Execution reports imbalance-aware metrics per condition with reasonable seed coverage.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml08-exec-metrics", + "requirements": "Execution produces a machine-readable metrics artifact with numeric balanced-accuracy and at least one of {minority recall, average precision, F1} per implemented condition on at least one dataset.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml08-exec-seeds", + "requirements": "Reported metrics are aggregated over multiple random seeds per (dataset, condition) with some dispersion measure. More seeds are better, but honest small-seed runs with variance reported are preferable to single deterministic runs.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml08-results", + "requirements": "The analysis addresses H1/H2/H3 directionally with a clear narrative.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml08-result-h1", + "requirements": "The submission compares no-handling against class-weighting and/or SMOTE-style oversampling on balanced-accuracy and conveys whether imbalance handling yields a meaningful improvement \u2014 judge directionally against H1.", + "weight": 25.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml08-result-h2h3", + "requirements": "The submission reports the minority-recall / average-precision comparisons needed for H2 and the random-oversampling vs class-weighting comparison needed for H3, conveying qualitative outcomes for both.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml08-result-writeup", + "requirements": "The README or writeup describes setup, reports per-dataset metric outcomes, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as synthetic-dataset realism, threshold dependence, seed count, or SMOTE simplifications. No strict word-count requirement.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML09.json b/tasks/ml/rubrics/ML09.json new file mode 100644 index 0000000000000000000000000000000000000000..4a351913c3050fc3d9cca823e230bcdf1267313c --- /dev/null +++ b/tasks/ml/rubrics/ML09.json @@ -0,0 +1,117 @@ +{ + "id": "ml09-root", + "requirements": "A credible experiment comparing Bayesian optimization, grid search, and random search for RandomForest hyperparameter tuning: methods are implemented with comparable budgets, executed on multiple datasets, and results are mapped directionally to H1/H2/H3.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. If a well-motivated substitute (e.g., Hyperopt, Optuna, or a custom TPE-like surrogate) is used in place of a canonical Bayesian library, credit the scientific intent.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml09-code", + "requirements": "The tuning strategies and shared evaluation setup are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml09-code-strategies", + "requirements": "The submission implements distinct code paths for grid search, random search, and a Bayesian-style search for RandomForestClassifier (or equivalent) rather than reusing identical sampled configurations across methods.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml09-code-space", + "requirements": "A shared hyperparameter space is defined (covering several meaningful RF hyperparameters such as n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features) so search methods are fairly comparable.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Hyperparameter Tuning" + }, + { + "id": "ml09-code-data", + "requirements": "The code loads multiple sklearn classification datasets (e.g., wine, breast_cancer, digits, or comparable) and uses a consistent train/test split plus CV protocol across search methods.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml09-exec", + "requirements": "The benchmark is executed and logs optimization quality and efficiency metrics.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml09-exec-metrics", + "requirements": "Execution outputs numeric best_cv_score and test_accuracy (or equivalents) for each implemented method on at least one dataset in a machine-readable metrics artifact.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml09-exec-budget", + "requirements": "Execution evidences budget control: non-default search methods evaluate a roughly comparable number of configurations, so quality comparisons are not confounded by wildly different evaluation counts.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "ml09-exec-time", + "requirements": "Execution reports a wall-clock timing measure (e.g., search_time_sec) per method and dataset.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml09-results", + "requirements": "Results address H1/H2/H3 directionally and convey interpretable tradeoffs.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml09-result-h1", + "requirements": "The submission compares Bayesian optimization vs random search on best_cv_score across the evaluated datasets and conveys whether Bayesian search is meaningfully better on most \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml09-result-h2", + "requirements": "The submission compares random search vs grid search on best_cv_score and conveys whether random is at least competitive with grid on most datasets (H2).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml09-result-h3", + "requirements": "The submission checks test_accuracy differences between the best Bayesian-tuned and best grid-tuned models and conveys whether any practical gain (H3) is observed on at least one dataset.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml09-result-writeup", + "requirements": "The README or writeup reports per-dataset metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations such as small dataset set, budget tightness, surrogate instability, or CV variance. No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML10.json b/tasks/ml/rubrics/ML10.json new file mode 100644 index 0000000000000000000000000000000000000000..c8f1b906a3c93306e1d55fc13a925052f2567a99 --- /dev/null +++ b/tasks/ml/rubrics/ML10.json @@ -0,0 +1,101 @@ +{ + "id": "ml10-root", + "requirements": "A credible experiment studying how cross-validation strategy choice (k-fold, stratified k-fold, repeated stratified k-fold, LOOCV) affects small-sample model-selection reliability: strategies are implemented consistently, runs cover multiple small-sample datasets with multiple seeds, and results address H1/H2/H3 directionally around estimation bias, stability, and compute.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative CV strategies or datasets that preserve the scientific question (small-sample bias/stability of CV) should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml10-code", + "requirements": "Cross-validation strategy conditions and the model-selection pipeline are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml10-code-cv-strategies", + "requirements": "The submission implements multiple distinct CV strategies \u2014 typically including plain k-fold, stratified k-fold, repeated stratified k-fold, and optionally LOOCV \u2014 as separate code paths used for model selection under a shared candidate-model grid.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml10-code-datasets", + "requirements": "The submission uses multiple small-sample datasets (e.g., reduced breast_cancer, wine, or synthetic imbalanced variants) with a held-out test split.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml10-code-bias-metric", + "requirements": "Code computes an estimation-bias style metric (e.g., |best_cv_score - selected_model_test_score|) or equivalent for each (dataset, seed, strategy) and aggregates it reproducibly.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml10-exec", + "requirements": "Execution logs required metrics per strategy with multi-seed evaluation.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml10-exec-metrics-output", + "requirements": "Execution produces a machine-readable results artifact containing estimation bias (or equivalent) and selected-model test accuracy for each implemented strategy on at least one dataset.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml10-exec-seeds-runtime", + "requirements": "Execution uses multiple random seeds per (dataset, strategy) and records timing information sufficient to compare the more expensive strategies (e.g., LOOCV vs repeated stratified). Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml10-results", + "requirements": "Reported results address H1/H2/H3 directionally with interpretable evidence.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml10-result-h1", + "requirements": "The submission compares mean estimation bias of repeated-stratified CV vs plain k-fold per dataset and conveys whether repeated stratified is meaningfully better on most datasets \u2014 judge directionally against H1.", + "weight": 25.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml10-result-h2h3", + "requirements": "The submission reports selection stability or across-seed variability for stratified k-fold vs plain k-fold (H2) and compares LOOCV versus repeated stratified CV on estimation bias and runtime (H3), conveying qualitative outcomes for both.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml10-result-writeup", + "requirements": "The README or writeup describes the setup, reports per-strategy metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (small dataset scope, candidate-model grid choice, metric sensitivity, compute budget). No strict word-count requirement.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML11.json b/tasks/ml/rubrics/ML11.json new file mode 100644 index 0000000000000000000000000000000000000000..c66bfa2865ca35869c8047fcdd3d7409e685c399 --- /dev/null +++ b/tasks/ml/rubrics/ML11.json @@ -0,0 +1,109 @@ +{ + "id": "ml11-root", + "requirements": "A credible experiment benchmarking unsupervised outlier detectors (IsolationForest, LocalOutlierFactor, OneClassSVM, EllipticEnvelope, or equivalents) with injected anomalies: methods are implemented as distinct code paths, runs cover multiple datasets across anomaly rates, and results address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated substitutes (e.g., HBOS, KNN-based outlier) that test the same scientific question should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml11-code", + "requirements": "The outlier-detection conditions and anomaly-injection pipeline are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml11-code-methods", + "requirements": "The submission implements multiple distinct detector code paths \u2014 typically including IsolationForest, LOF, OneClassSVM, and an elliptic/Gaussian-envelope method \u2014 rather than aliases to one shared estimator.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml11-code-injection", + "requirements": "A reproducible anomaly-injection routine is implemented for test data at one or more explicit contamination rates with binary ground-truth anomaly labels.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "ml11-code-data", + "requirements": "The code uses multiple datasets (sklearn built-ins or comparable) and applies a consistent preprocessing pipeline (including feature scaling) before model fitting.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml11-exec", + "requirements": "Execution records anomaly-detection metrics for each condition.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml11-exec-metrics", + "requirements": "Execution produces a machine-readable metrics artifact containing numeric ROC-AUC and PR-AUC (or equivalents) for each implemented method on at least one (dataset, rate) cell.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml11-exec-seeds-rates", + "requirements": "Reported results aggregate over multiple random seeds and include at least two anomaly rates, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml11-exec-runtime", + "requirements": "The run logs a wall-clock timing measure per method and demonstrates completion within a CPU-only workflow.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml11-results", + "requirements": "Quantitative analysis addresses H1/H2/H3 directionally.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml11-result-h1", + "requirements": "The submission compares mean ROC-AUC of IsolationForest vs OneClassSVM per dataset and conveys whether IsolationForest is meaningfully better on most datasets \u2014 judge directionally against H1.", + "weight": 25.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml11-result-h2h3", + "requirements": "The submission conveys whether LOF is competitive on PR-AUC for at least one dataset at a higher anomaly rate (H2) and whether detectors meaningfully outperform a random baseline on pooled ROC-AUC (H3).", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml11-result-writeup", + "requirements": "The README or writeup describes setup and anomaly injection, reports ROC-AUC/PR-AUC results, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (synthetic anomalies, dataset scope, hyperparameter sensitivity, seed count). No strict word-count requirement.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML12.json b/tasks/ml/rubrics/ML12.json new file mode 100644 index 0000000000000000000000000000000000000000..f1d36f46d261739ea186b4e69f261193b5aea0cf --- /dev/null +++ b/tasks/ml/rubrics/ML12.json @@ -0,0 +1,109 @@ +{ + "id": "ml12-root", + "requirements": "A credible experiment comparing clustering algorithms across synthetic geometric datasets: clustering conditions are implemented, execution covers multiple datasets with repeated seeds and ARI-centric reporting, and the analysis addresses H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated algorithm substitutes (e.g., HDBSCAN for DBSCAN) that test the same question should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml12-code", + "requirements": "The clustering conditions and dataset generators are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml12-code-algos", + "requirements": "The submission implements multiple distinct clustering conditions \u2014 typically k-means, agglomerative, DBSCAN, and/or spectral \u2014 via sklearn APIs with separate code paths and method-specific hyperparameters.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml12-code-data", + "requirements": "The submission generates multiple synthetic datasets with varied geometry (e.g., moons, circles, anisotropic blobs), preserves ground-truth labels, and standardizes features before clustering where appropriate.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml12-code-setup", + "requirements": "The experiment framework includes seed control and a consistent per-dataset protocol for cluster-count-dependent methods.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml12-exec", + "requirements": "Execution produces clustering metrics for each condition.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml12-exec-metrics", + "requirements": "Execution outputs a metrics artifact containing numeric adjusted_rand_score (or equivalent) for each implemented condition on at least one dataset.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml12-exec-repeats", + "requirements": "Reported results are aggregated over multiple random seeds per (dataset, condition) with a dispersion estimate. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml12-exec-tuning", + "requirements": "For DBSCAN (or equivalent density-based method), the run documents a reasonable choice of eps/min_samples (either via a small search or a justified fixed value), logged in the artifacts.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Hyperparameter Tuning" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml12-results", + "requirements": "Results address H1/H2/H3 directionally with interpretable evidence.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml12-result-h1", + "requirements": "The submission compares ARI of density-based / spectral methods against k-means on nonlinear-geometry datasets and conveys whether they produce meaningfully better cluster assignments \u2014 judge directionally against H1.", + "weight": 25.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml12-result-h2h3", + "requirements": "The submission conveys dataset-wise ARI outcomes \u2014 whether agglomerative-ward is at least competitive with k-means on anisotropic blobs (H2) and whether any single method wins across all datasets (H3).", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml12-result-writeup", + "requirements": "The README or writeup describes setup, key ARI/NMI findings per dataset, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (synthetic-only scope, hyperparameter sensitivity, seed count, metric dependence). No strict word-count requirement.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML13.json b/tasks/ml/rubrics/ML13.json new file mode 100644 index 0000000000000000000000000000000000000000..4e23f057fce530417808ce545c88fe37e0c0bf56 --- /dev/null +++ b/tasks/ml/rubrics/ML13.json @@ -0,0 +1,109 @@ +{ + "id": "ml13-root", + "requirements": "A credible experiment studying kernel-choice effects (RBF, polynomial, Matern-3/2, Matern-5/2, or equivalents) for Gaussian Process regression on synthetic noisy functions: conditions are implemented, execution covers multiple datasets with repeated seeds, and results address H1/H2/H3 directionally using test NLL as the primary metric.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative kernels or dataset dimensions that test the same scientific question should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml13-code", + "requirements": "The GP kernel conditions and synthetic datasets are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml13-code-kernels", + "requirements": "The submission implements multiple kernel conditions \u2014 typically including RBF, a polynomial kernel, and one or more Matern kernels \u2014 as distinct GP configurations with comparable noise handling.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml13-code-datasets", + "requirements": "The submission generates synthetic regression datasets with explicit noise (both a low-dimensional and a higher-dimensional case preferred) and creates train/test splits.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml13-code-setup", + "requirements": "Experimental setup controls are consistent across kernels (same split protocol, seed handling, and optimizer restart policy), enabling fair kernel comparison.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml13-exec", + "requirements": "Execution logs primary and secondary metrics for each kernel and dataset.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml13-exec-metrics", + "requirements": "Execution produces a metrics artifact containing numeric test NLL and RMSE (or equivalents) for every implemented (kernel, dataset) pair.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml13-exec-seeds", + "requirements": "Reported metrics are aggregated over multiple random seeds per (kernel, dataset) cell, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Hyperparameter Tuning" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml13-results", + "requirements": "Results analysis addresses H1/H2/H3 directionally with quantitative comparisons.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml13-result-h1", + "requirements": "The submission reports per-dataset mean test-NLL ranking and conveys whether smooth kernels (RBF, Matern-5/2) tend to be best \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml13-result-h2", + "requirements": "The submission compares the polynomial-kernel NLL against the best kernel and conveys whether polynomial is meaningfully worse on at least one dataset (H2).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml13-result-h3", + "requirements": "The submission compares kernel rankings by test NLL and RMSE on each dataset and conveys whether rankings can differ between these two metrics (H3).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml13-result-writeup", + "requirements": "The README or writeup describes implementation choices, dataset generation, metric tables, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (synthetic-only scope, kernel parameterization, seed/compute constraints). No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML14.json b/tasks/ml/rubrics/ML14.json new file mode 100644 index 0000000000000000000000000000000000000000..93fa7c7741e16395a076b06eb98d66f141cbb359 --- /dev/null +++ b/tasks/ml/rubrics/ML14.json @@ -0,0 +1,117 @@ +{ + "id": "ml14-root", + "requirements": "A credible experiment comparing split-conformal, Mondrian conformal, CQR-style, and optionally naive-quantile prediction intervals for ~90% target coverage on heteroscedastic regression: methods are implemented, executed on multiple datasets with multiple seeds, and results address H1/H2/H3 directionally with coverage-gap-focused analysis.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative conformal variants (e.g., jackknife+, locally adaptive) that test the same scientific question should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml14-code", + "requirements": "The conformal and baseline interval-construction conditions are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml14-code-methods", + "requirements": "The submission implements multiple distinct conditions \u2014 typically including split conformal, a Mondrian/group-adaptive variant, and a CQR-style or naive-quantile baseline \u2014 as separate code paths, not a single shared interval formula.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml14-code-conformal-splits", + "requirements": "Conformal methods use explicit train/calibration/test separation (or equivalent cross-fit logic) so calibration quantiles are computed on data not used to fit base regressors.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "ml14-code-datasets", + "requirements": "The submission uses multiple datasets (including at least one heteroscedastic synthetic dataset) and prepares regression targets correctly.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml14-exec", + "requirements": "Execution outputs interval-quality metrics for each condition.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml14-exec-metrics", + "requirements": "Execution produces a metrics artifact containing numeric coverage gap and mean interval width (or equivalents) for each implemented condition on at least one dataset.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml14-exec-seeds", + "requirements": "Reported metrics are aggregated over multiple random seeds per (condition, dataset) cell with a dispersion estimate. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml14-exec-conditional", + "requirements": "Execution computes a conditional-coverage diagnostic (e.g., worst-bin miscoverage across several bins) for the conformal methods.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml14-results", + "requirements": "Quantitative analysis addresses H1/H2/H3 directionally and discusses trade-offs between coverage, conditional validity, and interval efficiency.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml14-result-h1", + "requirements": "The submission compares coverage gap of adaptive methods (Mondrian, CQR-style) against plain split conformal per dataset and conveys whether adaptive methods are meaningfully closer to the nominal coverage \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml14-result-h2", + "requirements": "The submission evaluates whether CQR-style intervals achieve mean interval width comparable to or narrower than plain split conformal on most datasets and conveys an H2 outcome.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml14-result-h3", + "requirements": "The submission compares worst-bin miscoverage for Mondrian conformal vs plain split conformal and conveys whether Mondrian yields a meaningful improvement in conditional coverage (H3).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml14-result-writeup", + "requirements": "The README or writeup describes methods and datasets, reports key metric values (coverage gap, interval width, worst-bin miscoverage), conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (finite seeds, binning choice, synthetic-to-real transfer). No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML15.json b/tasks/ml/rubrics/ML15.json new file mode 100644 index 0000000000000000000000000000000000000000..967c505a39e4c3ed345817a25f539fd2dac4913e --- /dev/null +++ b/tasks/ml/rubrics/ML15.json @@ -0,0 +1,109 @@ +{ + "id": "ml15-root", + "requirements": "A credible experiment studying filter-based feature selection (mutual information, chi-square, ANOVA F) versus embedded L1-logistic selection under injected irrelevant features: conditions are implemented, runs cover multiple datasets with multiple seeds and injection levels, and results address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative filter methods or embedded selectors (e.g., tree-based importance) that test the same scientific question should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml15-code", + "requirements": "Feature-selection conditions and the noise-injection pipeline are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml15-code-conditions", + "requirements": "The submission implements multiple distinct selection conditions \u2014 typically including mutual information, chi-square, ANOVA F, and L1-logistic (or a comparable embedded method) \u2014 rather than reusing one selector for all conditions.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml15-code-injection", + "requirements": "The pipeline injects synthetic irrelevant features at one or more defined levels (including a no-injection baseline and a higher-injection case) and tracks which columns are injected so a noise-selection rate can be computed.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "ml15-code-datasets", + "requirements": "The submission uses multiple datasets (sklearn built-ins or comparable) and a reasonable train/test split.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml15-exec", + "requirements": "Execution produces metrics across selectors and injection levels.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml15-exec-metrics", + "requirements": "Execution outputs a structured metrics artifact containing numeric test accuracy and a noise-selection-rate style measure for each implemented condition on at least one dataset with at least two injection levels.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml15-exec-seeds", + "requirements": "Reported metrics are aggregated over multiple random seeds per (dataset, condition, injection level) cell with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Hyperparameter Tuning" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml15-results", + "requirements": "Results address H1/H2/H3 directionally with a clear narrative.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml15-result-h1", + "requirements": "The submission compares mean test accuracy at the higher injection level between the embedded L1-logistic method and each filter method per dataset and conveys whether L1 tends to be better \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml15-result-h2", + "requirements": "The submission reports the noise-selection rate for each method and conveys whether the embedded method selects fewer irrelevant features than the filter-method average on most datasets (H2).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml15-result-h3", + "requirements": "The submission reports the accuracy drop from low-injection to high-injection for the embedded method and conveys whether the drop is small on most datasets (H3).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml15-result-writeup", + "requirements": "The README or writeup describes the experimental setup, reports key metric tables, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as synthetic-noise realism, k-choice, classifier dependence, and seed/dataset scope. No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML16.json b/tasks/ml/rubrics/ML16.json new file mode 100644 index 0000000000000000000000000000000000000000..1f75266eda6e4c142c8f6a8f9798f5b4772a40e8 --- /dev/null +++ b/tasks/ml/rubrics/ML16.json @@ -0,0 +1,109 @@ +{ + "id": "ml16-root", + "requirements": "A credible experiment comparing bandit algorithms (epsilon-greedy, UCB1, Thompson sampling, Exp3, or equivalents) under stationary and drifting synthetic regimes: algorithms are implemented with a common interface, experiments cover multiple environments with repeated seeds, and results address H1/H2/H3 directionally using cumulative regret.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated algorithm variants (e.g., UCB-V, linear Thompson) should be credited when they test the same scientific question.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml16-code", + "requirements": "The bandit algorithms and synthetic environments are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml16-code-algos", + "requirements": "The submission implements multiple distinct algorithm code paths \u2014 typically including epsilon-greedy, UCB1, Thompson sampling, and/or Exp3 \u2014 with per-round action selection and update logic that are not identical wrappers.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml16-code-envs", + "requirements": "The submission defines multiple synthetic bandit environments including at least one stationary and one drifting regime, with reproducible seed control and oracle best-arm rewards per round for regret computation.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "ml16-code-thompson-exp3", + "requirements": "If Thompson sampling and/or Exp3 are included, implementation uses sampling-based posterior decisions (Thompson) and probability-weighted action selection with importance-weighted updates (Exp3), rather than greedy mean selection.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml16-exec", + "requirements": "Execution produces regret metrics across algorithms and datasets.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml16-exec-runs", + "requirements": "Execution runs multiple seeds per (algorithm, dataset) cell for multiple environments and logs final cumulative-regret values with mean and dispersion. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml16-exec-artifacts", + "requirements": "A machine-readable results artifact is produced containing dataset-wise metrics for each implemented algorithm, including cumulative regret.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml16-results", + "requirements": "Findings address H1/H2/H3 directionally and summarize implications.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml16-result-h1", + "requirements": "The submission compares stationary-regime cumulative regret between epsilon-greedy and {UCB1, Thompson} and conveys whether the principled algorithms are meaningfully better \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml16-result-h2", + "requirements": "The submission evaluates drifting-regime cumulative regret and conveys whether more exploratory algorithms (epsilon-greedy or Exp3) outperform UCB1 on most drifting datasets (H2).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml16-result-h3", + "requirements": "The submission conveys whether any single algorithm dominates across all environments or whether winners are mixed (H3), with supporting tables or summaries.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml16-result-writeup", + "requirements": "The README or writeup describes setup, reports key cumulative-regret results per environment, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (horizon length, hyperparameter sensitivity, synthetic-only scope). No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML17.json b/tasks/ml/rubrics/ML17.json new file mode 100644 index 0000000000000000000000000000000000000000..9262af9575fb31cea1fa61f8f49abc95a2584d0c --- /dev/null +++ b/tasks/ml/rubrics/ML17.json @@ -0,0 +1,109 @@ +{ + "id": "ml17-root", + "requirements": "A credible experiment comparing LDA, NMF, and LSA topic models on small 20newsgroups subsets: conditions are implemented, execution reports coherence and ARI on multiple subsets with repeated seeds, and results address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative topic-model implementations or coherence approximations that test the same scientific question should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml17-code", + "requirements": "Topic-model conditions and dataset pipeline are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml17-code-models", + "requirements": "The submission implements LDA, NMF, and LSA (or equivalents such as a truncated SVD for LSA) as distinct modeling code paths with a matched topic-count setting.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml17-code-data", + "requirements": "The submission loads multiple 20newsgroups subsets (or comparable document corpora) with consistent text preprocessing/vectorization documented in code.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml17-code-metrics-impl", + "requirements": "The code computes a coherence-style metric (e.g., c_v or a documented approximation) and ARI from document-cluster assignments for each method.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml17-exec", + "requirements": "Execution produces comparable numeric outputs for all implemented methods.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml17-exec-runs", + "requirements": "Execution evaluates the core methods on multiple datasets and writes per-method, per-dataset coherence and ARI values to a machine-readable artifact.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml17-exec-seeds-runtime", + "requirements": "Execution repeats stochastic methods with multiple seeds per dataset, reports dispersion, and logs a wall-clock timing measure per condition. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml17-results", + "requirements": "Quantitative analysis addresses H1/H2/H3 directionally.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml17-result-h1", + "requirements": "The submission compares NMF vs LSA on coherence for each evaluated dataset and conveys whether NMF tends to yield better coherence \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml17-result-h2", + "requirements": "The submission checks ranking agreement between coherence and ARI across methods per dataset and conveys whether coherence-ranking and ARI-ranking tend to diverge (H2).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml17-result-h3", + "requirements": "The submission reports LDA vs LSA ARI deltas per dataset and conveys whether LDA tends to achieve meaningfully higher document-cluster ARI (H3).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml17-result-writeup", + "requirements": "The README or writeup describes methods and preprocessing, reports coherence/ARI/runtime results, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (subset choice, coherence approximation validity, seed count, clustering-assignment assumptions). No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML18.json b/tasks/ml/rubrics/ML18.json new file mode 100644 index 0000000000000000000000000000000000000000..7100be4ee70d14e81baa1b69af4514820703657a --- /dev/null +++ b/tasks/ml/rubrics/ML18.json @@ -0,0 +1,109 @@ +{ + "id": "ml18-root", + "requirements": "A credible experiment studying post-hoc probability calibration methods (Platt, isotonic, temperature, or equivalents) for sklearn classifiers: calibration conditions are implemented, execution covers multiple datasets/classifiers with repeated seeds, and results address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated calibrator variants or alternative base classifiers that preserve the scientific question should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml18-code", + "requirements": "Calibration methods and classifier setup are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml18-code-calibrators", + "requirements": "The submission implements multiple calibration conditions \u2014 typically including uncalibrated, Platt, isotonic, and temperature scaling \u2014 as distinct code paths applied post-hoc to the same base model outputs.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml18-code-models", + "requirements": "The experiment includes multiple target classifiers (e.g., RandomForest, gradient boosting, SVM-RBF, or equivalents) with consistent train/calibration/test handling.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "ml18-code-datasets", + "requirements": "The submission uses multiple datasets (sklearn built-ins or comparable) and an explicit calibration split separate from train and test.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml18-exec", + "requirements": "Execution reports calibration-focused metrics across conditions.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml18-exec-metrics", + "requirements": "Execution outputs numeric ECE, log loss, and test accuracy (or equivalents) for each implemented condition on at least one dataset-classifier pair in a machine-readable artifact.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml18-exec-seeds", + "requirements": "Metrics are aggregated over multiple random seeds per evaluated cell with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml18-results", + "requirements": "Results quantitatively address H1/H2/H3 directionally with a clear narrative.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml18-result-h1", + "requirements": "The submission computes relative ECE change versus uncalibrated and conveys whether at least one calibrator yields a meaningful ECE reduction across most classifier/dataset pairs \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml18-result-h2", + "requirements": "The submission compares isotonic vs Platt mean ECE per dataset and conveys the relative calibration quality plus any calibration-set size considerations (H2).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml18-result-h3", + "requirements": "The submission reports accuracy deltas between calibrated and uncalibrated outputs per classifier-dataset pair and conveys whether accuracy changes are small (H3).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml18-result-writeup", + "requirements": "The README or report conveys per-hypothesis outcomes (supported / refuted / inconclusive), cites key ECE/log-loss/accuracy numbers, and discusses limitations (dataset scope, binning sensitivity, seed count, calibration split size). No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML19.json b/tasks/ml/rubrics/ML19.json new file mode 100644 index 0000000000000000000000000000000000000000..232aa78c66323495d7fbaad5c2709836478e65f1 --- /dev/null +++ b/tasks/ml/rubrics/ML19.json @@ -0,0 +1,109 @@ +{ + "id": "ml19-root", + "requirements": "A credible experiment comparing semi-supervised methods (LabelPropagation, LabelSpreading, SelfTrainingClassifier, or equivalents) against a supervised-only baseline under limited-label regimes: methods are implemented correctly, runs cover multiple datasets with repeated seeds, and results address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated alternative semi-supervised methods that test the same scientific question should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml19-code", + "requirements": "Semi-supervised and baseline conditions are implemented correctly with explicit label masking.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml19-code-methods", + "requirements": "The submission implements multiple distinct conditions \u2014 typically including at least one graph-based method (LabelPropagation or LabelSpreading), a self-training method, and a supervised-only baseline \u2014 as separate code paths.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml19-code-masking", + "requirements": "Label masking for semi-supervised runs is explicit and correct: only a defined labeled fraction retains class labels and the remaining training labels are set to the unlabeled marker.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "ml19-code-datasets", + "requirements": "The submission uses multiple datasets (sklearn built-ins or comparable), with a held-out test split and consistent preprocessing across conditions.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml19-exec", + "requirements": "Execution covers multiple label fractions and produces benchmark metrics.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml19-exec-metrics", + "requirements": "Execution outputs numeric test accuracy and at least one additional metric (e.g., balanced accuracy or macro-F1) for each implemented (condition, dataset, label-fraction) cell in a machine-readable artifact.", + "weight": 16.6667, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml19-exec-seeds", + "requirements": "Each reported cell is aggregated over multiple random seeds with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml19-results", + "requirements": "Results address H1/H2/H3 directionally with quantitative comparisons.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml19-result-h1", + "requirements": "The submission compares each semi-supervised method versus the supervised-only baseline at a low labeled fraction and conveys whether any method provides a meaningful gain on most datasets \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml19-result-h2", + "requirements": "The submission reports a direct LabelSpreading vs LabelPropagation comparison per dataset and conveys whether LabelSpreading is at least competitive on most datasets (H2).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml19-result-h3", + "requirements": "The submission reports self-training accuracy at both low and higher labeled fractions and conveys whether the gain with more labels is meaningful (H3).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml19-result-writeup", + "requirements": "The README or writeup describes setup and methods, reports per-dataset metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (dataset scope, seed count variance, sensitivity to masking/hyperparameters). No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML20.json b/tasks/ml/rubrics/ML20.json new file mode 100644 index 0000000000000000000000000000000000000000..6ba65ba5667cc2fa37c77f5f6af17c2cbeb0da3a --- /dev/null +++ b/tasks/ml/rubrics/ML20.json @@ -0,0 +1,117 @@ +{ + "id": "ml20-root", + "requirements": "A credible experiment benchmarking classical forecasters (AR, ARIMA, SARIMAX, ETS, Theta, or equivalents) on synthetic seasonal series: model conditions are implemented, execution covers multiple synthetic regimes with repeated seeds, and results address H1/H2/H3 directionally using sMAPE-centered analysis.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative forecaster implementations or substitutes that test the same scientific question should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml20-code", + "requirements": "The forecasting conditions and synthetic data generation pipeline are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml20-code-methods", + "requirements": "The submission implements multiple distinct forecasting methods \u2014 typically AR/ARIMA, a seasonal model (SARIMAX or ETS), and Theta or equivalents \u2014 as separate code paths, and includes at least one explicit naive/seasonal-naive baseline.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml20-code-synthdata", + "requirements": "The submission generates multiple synthetic seasonal dataset families with controllable seasonality, trend, and noise, each with a train/test split suitable for forecasting.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml20-code-setup", + "requirements": "Forecasting setup uses a fixed holdout horizon (or rolling-origin equivalent) and computes comparable forecasts for every (method, dataset, seed) cell.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml20-exec", + "requirements": "Benchmark execution logs required metrics with repeated trials.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml20-exec-metrics", + "requirements": "Execution produces a structured metrics artifact containing numeric sMAPE and MAE (or equivalents) for each implemented method on at least one dataset family.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml20-exec-seeds", + "requirements": "Each reported method-dataset metric is aggregated over multiple random seeds with dispersion statistics. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml20-exec-runtime", + "requirements": "The run logs per-method wall-clock timing and completes within a CPU-only budget.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml20-results", + "requirements": "Results analysis addresses H1/H2/H3 directionally with quantitative comparisons.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml20-result-h1", + "requirements": "The submission isolates high-seasonality low-noise results and conveys whether SARIMAX or ETS tends to attain the best mean sMAPE \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml20-result-h2", + "requirements": "The submission compares the best advanced method against seasonal-naive under high-noise settings and conveys whether the sMAPE gap collapses to a small margin (H2).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml20-result-h3", + "requirements": "The submission ranks methods by mean sMAPE per dataset family and conveys whether no single method dominates across all families (H3).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml20-result-writeup", + "requirements": "The README or writeup reports key metric tables, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (synthetic realism, grid-search scope, seed count, horizon sensitivity). No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML21.json b/tasks/ml/rubrics/ML21.json new file mode 100644 index 0000000000000000000000000000000000000000..6fb6d94316207e2f19255ed1a08512dce318b5fe --- /dev/null +++ b/tasks/ml/rubrics/ML21.json @@ -0,0 +1,109 @@ +{ + "id": "ml21-root", + "requirements": "A credible experiment comparing causal structure learning methods (PC, GES, NOTEARS-linear, or equivalents) on small synthetic Gaussian linear-SEM DAGs: methods are implemented, runs cover multiple synthetic regimes with multiple seeds, and results address H1/H2/H3 directionally using SHD-centered evidence.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative structure-learning methods or linear-SEM variants that test the same scientific question should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml21-code", + "requirements": "Core causal structure learning conditions and synthetic data generation are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml21-code-methods", + "requirements": "The submission implements distinct code paths for multiple structure learning methods \u2014 typically PC, GES, and NOTEARS-linear or equivalents \u2014 rather than reusing one estimator under different names.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml21-code-synth", + "requirements": "A reproducible synthetic linear-Gaussian SEM DAG generator is implemented with moderate node counts, including ground-truth adjacency export and sample generation.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml21-code-hparams", + "requirements": "At least one tunable hyperparameter is exposed and used for the constraint-based method (e.g., PC's alpha) and the continuous-optimization method (e.g., NOTEARS' sparsity weight), with documented default or grid values.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Hyperparameter Tuning" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml21-exec", + "requirements": "The benchmark produces comparable metrics across methods/regimes.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml21-exec-coverage", + "requirements": "Execution covers multiple synthetic regimes and reports results for the primary methods at one or more sample sizes.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "ml21-exec-metrics", + "requirements": "A machine-readable results artifact includes numeric SHD for each (method, regime) and at least one additional metric such as edge-F1 or runtime.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml21-exec-seeds", + "requirements": "Each reported cell is averaged over multiple random seeds with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml21-results", + "requirements": "Results analysis addresses H1/H2/H3 directionally.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml21-result-h1", + "requirements": "The submission compares NOTEARS-linear vs PC at a reasonable sample size and conveys whether NOTEARS achieves meaningfully lower SHD on most regimes \u2014 judge directionally against H1.", + "weight": 25.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml21-result-h2h3", + "requirements": "The submission conveys qualitative verdicts for H2 and H3 using SHD aggregates, including whether larger sample sizes or denser-graph regimes yield meaningful SHD improvements.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml21-result-writeup", + "requirements": "The README or writeup describes setup, reports key SHD/edge-F1/runtime findings, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (synthetic-only scope, small-node regime, hyperparameter sensitivity, finite seeds). No strict word-count requirement.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML22.json b/tasks/ml/rubrics/ML22.json new file mode 100644 index 0000000000000000000000000000000000000000..539a622ce3ad8871fd963a5da47382f2e2d7a41d --- /dev/null +++ b/tasks/ml/rubrics/ML22.json @@ -0,0 +1,117 @@ +{ + "id": "ml22-root", + "requirements": "A credible experiment studying active-learning query strategies (random, uncertainty, margin, QBC, expected error reduction, or equivalents) for logistic regression: strategies are implemented as distinct code paths, execution covers multiple datasets with repeated seeds under a fixed budget, and results address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative acquisition functions or base classifiers that preserve the scientific question should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml22-code", + "requirements": "Active-learning strategies and shared logistic-regression pipeline are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml22-code-strategies", + "requirements": "The submission implements multiple distinct query conditions \u2014 typically random plus several of {uncertainty, margin, QBC, expected error reduction} \u2014 with genuinely different acquisition logic.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml22-code-loop", + "requirements": "There is a pool-based active-learning loop with an initial labeled seed set, iterative querying, model retraining/update, and stopping at a defined label budget.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "ml22-code-datasets", + "requirements": "The submission uses multiple datasets (sklearn built-ins or comparable) with consistent train/pool/test handling for all strategies.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml22-exec", + "requirements": "Execution emits budget-aware performance metrics.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml22-exec-metrics", + "requirements": "Execution produces a machine-readable metrics artifact containing numeric accuracy-at-budget and area-under-learning-curve (or equivalents) for each implemented strategy on at least one dataset.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml22-exec-seeds", + "requirements": "Each reported (strategy, dataset) result is aggregated over multiple random seeds, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml22-exec-budget-curve", + "requirements": "The run logs performance across multiple budget checkpoints so early-budget accuracy and AULC are computable from recorded traces.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml22-results", + "requirements": "Quantitative analysis addresses H1/H2/H3 directionally.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml22-result-h1", + "requirements": "The submission compares non-random strategies to random sampling at the final budget and conveys whether informative strategies meaningfully outperform random \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml22-result-h2", + "requirements": "The submission reports per-dataset AULC rankings and conveys whether expected-error-reduction (or a comparable principled strategy) is at least competitive, never falling below random (H2).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml22-result-h3", + "requirements": "The submission compares QBC vs uncertainty sampling at an early-budget checkpoint per dataset and conveys a qualitative verdict (H3).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml22-result-writeup", + "requirements": "The README or report describes setup, reports key numeric outcomes, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (runtime approximations, seed count, dataset scope, strategy sensitivity). No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML23.json b/tasks/ml/rubrics/ML23.json new file mode 100644 index 0000000000000000000000000000000000000000..16fcdb0d46743a10a0f96145af41705edaa4a32c --- /dev/null +++ b/tasks/ml/rubrics/ML23.json @@ -0,0 +1,109 @@ +{ + "id": "ml23-root", + "requirements": "A credible experiment comparing pointwise, pairwise (RankNet-style), and listwise (ListMLE-style) ranking approaches on synthetic query-document datasets: methods are implemented as distinct objectives, execution reports ranking metrics on multiple datasets with multiple seeds, and results address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated ranking-objective substitutes (e.g., LambdaRank-style, softrank) that test the same scientific question should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml23-code", + "requirements": "The ranking methods and synthetic grouped datasets are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml23-code-methods", + "requirements": "The submission implements multiple distinct conditions \u2014 typically a pointwise baseline plus at least one pairwise and one listwise method \u2014 with genuinely different objective computations, not merely different hyperparameters of one loss.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml23-code-data", + "requirements": "A synthetic query-document generator is implemented with grouped queries, per-query document lists, and graded relevance labels, and multiple dataset regimes are instantiated.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml23-code-setup", + "requirements": "The experimental setup includes query-level train/test splitting (no leakage of documents from the same query across splits) and a shared scoring-function interface across methods.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml23-exec", + "requirements": "Execution logs ranking metrics per method and dataset.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml23-exec-metrics", + "requirements": "Execution produces a metrics artifact containing numeric NDCG@k and MAP@k (or equivalents) for each implemented condition on at least one dataset.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml23-exec-seeds", + "requirements": "Reported metrics are averaged over multiple random seeds per (condition, dataset) cell with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml23-exec-tuning", + "requirements": "At least one non-trivial hyperparameter search or documented default is executed (e.g., learning rate/regularization) and the chosen value is logged.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Hyperparameter Tuning" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml23-results", + "requirements": "Results analysis addresses H1/H2/H3 directionally.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml23-result-h1", + "requirements": "The submission compares the listwise method vs the pointwise baseline in mean NDCG@k per dataset and conveys whether listwise meaningfully improves ranking quality \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml23-result-h2h3", + "requirements": "The submission conveys whether at least one ranking-aware method beats the pointwise baseline on NDCG across all datasets (H2) and whether the best ranking-aware method yields a meaningful MAP improvement on most datasets (H3).", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml23-result-writeup", + "requirements": "The README or writeup describes methods, datasets, and metric outcomes; conveys per-hypothesis outcomes (supported / refuted / inconclusive); and discusses limitations (synthetic-data realism, scorer capacity, seed count, metric sensitivity). No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML24.json b/tasks/ml/rubrics/ML24.json new file mode 100644 index 0000000000000000000000000000000000000000..098ec4b7d37f52fb6846d15c4b6b9822dd18f3e1 --- /dev/null +++ b/tasks/ml/rubrics/ML24.json @@ -0,0 +1,117 @@ +{ + "id": "ml24-root", + "requirements": "A credible experiment studying online learners (SGD logistic, Passive-Aggressive, online Naive Bayes, FTRL-style logistic, or equivalents) under concept drift: streaming prequential setup is implemented, execution covers multiple datasets with multiple seeds, and results address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated online-learner substitutes or drift-stream variants that test the same scientific question should be credited.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml24-code", + "requirements": "Online-learning conditions and drifted streaming setup are implemented correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml24-code-methods", + "requirements": "The submission implements multiple distinct online methods, each as a real incremental update path (e.g., partial_fit or equivalent), not batch retraining.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml24-code-streaming", + "requirements": "A prequential test-then-train loop is implemented: each sample (or mini-batch) is predicted before being used for model update, with ordered stream processing.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "ml24-code-datasets", + "requirements": "The submission uses multiple streams including at least one drifted stream and one no-drift or matched-control stream, generated from sklearn datasets or numpy synthesis.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml24-exec", + "requirements": "Execution reports prequential metrics for each condition.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml24-exec-metrics", + "requirements": "Execution produces a metrics artifact containing numeric prequential accuracy (or equivalent) for each implemented method on at least one dataset.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml24-exec-recovery", + "requirements": "Execution includes a post-drift recovery measure (e.g., fixed-window post-drift accuracy) with a documented computation referencing known drift points.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml24-exec-seeds", + "requirements": "Each reported (dataset, method) metric is averaged over multiple random seeds, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", + "weight": 6.25, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Hyperparameter Tuning" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml24-results", + "requirements": "Quantitative analysis addresses H1/H2/H3 directionally.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml24-result-h1", + "requirements": "The submission compares prequential accuracy of margin-based online methods (Passive-Aggressive, FTRL) against online Naive Bayes across drifted datasets and conveys whether margin-based methods are meaningfully better \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml24-result-h2", + "requirements": "The submission reports post-drift recovery comparisons and conveys whether Passive-Aggressive or FTRL tends to lead on most datasets (H2).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml24-result-h3", + "requirements": "The submission compares drifted vs no-drift control performance for the implemented methods and conveys whether drift produces a meaningful accuracy degradation (H3).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml24-result-writeup", + "requirements": "The README or writeup describes setup, key metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (stream realism, drift design choices, seed count, metric assumptions). No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/rubrics/ML25.json b/tasks/ml/rubrics/ML25.json new file mode 100644 index 0000000000000000000000000000000000000000..eb00424b1e290db102213d73c10c380421173eba --- /dev/null +++ b/tasks/ml/rubrics/ML25.json @@ -0,0 +1,125 @@ +{ + "id": "ml25-root", + "requirements": "A credible CPU-scale study of reservoir computing (echo-state network), a matched-parameter MLP, and a Gaussian Process on Lorenz-63 short-horizon forecasting: Lorenz-63 is integrated, the three model families are implemented, multiple training-set sizes are swept, valid-prediction-time is reported, and results address H1/H2/H3 directionally.", + "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Reasonable substitutes (e.g., alternative chaotic system, library-based ESN when clearly documented, Matern-kernel GP) that test the same scientific question should be credited. A GP running out of memory at large N is a valid reported outcome.", + "weight": 1, + "sub_tasks": [ + { + "id": "ml25-code", + "requirements": "The three model families and the dynamical system are implemented.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml25-code-lorenz", + "requirements": "The submission integrates the Lorenz-63 system with classical parameters at a small time step using scipy.integrate or a hand-rolled Runge-Kutta integrator, and uses separate train/test trajectories.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "ml25-code-esn", + "requirements": "An echo-state network is implemented with a sparse random reservoir scaled to a target spectral radius below ~1.1, driven by the input sequence with a leak-rate update and a linear readout fit by ridge regression. A from-scratch implementation is preferred; a clearly documented library-based ESN that exposes the same mechanics is acceptable.", + "weight": 8.3333, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml25-code-mlp", + "requirements": "An MLP is implemented that reads a fixed window of past states and predicts the next state, with its total parameter count reported and roughly matched to the ESN readout parameter count.", + "weight": 4.1667, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "ml25-code-gp", + "requirements": "A Gaussian Process regressor with an RBF or Matern kernel is fit on the same windowed input, with marginal-likelihood hyperparameter optimisation enabled.", + "weight": 4.1667, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml25-exec", + "requirements": "Execution produces per-method, per-N quantitative results.", + "weight": 2, + "sub_tasks": [ + { + "id": "ml25-exec-sweep", + "requirements": "The experiment sweeps multiple training-set sizes for each of the three methods and reports numeric results per cell.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml25-exec-vpt", + "requirements": "A valid-prediction-time (VPT) metric is computed per method per N \u2014 the first step at which the normalised prediction error exceeds a threshold \u2014 averaged over multiple independent initial conditions or seeds.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "ml25-exec-rmse", + "requirements": "A one-step-ahead RMSE (or equivalent pointwise error) is computed per method per N on the test trajectory and reported in the metrics file.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "ml25-results", + "requirements": "The writeup addresses H1/H2/H3 directionally and contextualises numeric findings.", + "weight": 3, + "sub_tasks": [ + { + "id": "ml25-result-h1", + "requirements": "The submission reports VPT for the ESN vs MLP at small training sizes and conveys whether the ESN meaningfully outperforms the MLP in the small-N regime \u2014 judge directionally against H1.", + "weight": 20.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml25-result-h2", + "requirements": "The submission compares ESN and GP VPT at both small-N and larger-N and conveys whether the GP is competitive at small N and falls behind (or becomes infeasible) at large N (H2). Reporting a GP memory/time failure at large N is a valid outcome.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml25-result-h3", + "requirements": "The submission examines whether one-step RMSE ranking tracks VPT ranking across method-N cells and conveys whether at least one rank inversion is observed (H3).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "ml25-result-writeup", + "requirements": "The README or writeup describes the Lorenz-63 setup, the three methods, the N-sweep, the VPT and RMSE numbers, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (single dynamical system, no Lyapunov-time normalisation, GP memory cliff). No strict word-count requirement.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/ml/topics.yaml b/tasks/ml/topics.yaml new file mode 100644 index 0000000000000000000000000000000000000000..01227a250162b50d76f1a2d899e3bdd231109dd0 --- /dev/null +++ b/tasks/ml/topics.yaml @@ -0,0 +1,222 @@ +# ============================================================================ +# ARC-Bench ML — 25 CPU-friendly ML research topics +# ---------------------------------------------------------------------------- +# ML01-ML10 are kept in sync with experiments/hitl_ablation/topics.yaml so the +# two experiments share stimuli. ML11-ML25 are new, spanning additional ML +# subfields while staying CPU-executable in <10 min on a single core with +# numpy / scipy / sklearn / statsmodels / networkx. +# ============================================================================ + + +topics: +- id: ML01 + topic: Comparing dropout regularization strategies (standard, spatial, variational) + for preventing overfitting in shallow MLPs on tabular classification benchmarks + domains: + - machine-learning + - regularization + metric_key: test_accuracy + metric_direction: maximize +- id: ML02 + topic: Evaluating ensemble methods (bagging, boosting, stacking) for regression + on synthetic non-linear datasets with varying noise levels + domains: + - machine-learning + - ensemble-methods + metric_key: rmse + metric_direction: minimize +- id: ML03 + topic: Comparing gradient-free optimization algorithms (Nelder-Mead, Powell, CMA-ES) + for non-convex benchmark functions using only CPU computation + domains: + - optimization + - numerical-methods + metric_key: primary_metric + metric_direction: minimize +- id: ML04 + topic: Analyzing the effect of feature scaling methods (standard, min-max, robust) + on k-nearest neighbors classification performance across different data distributions + domains: + - machine-learning + - data-preprocessing + metric_key: test_accuracy + metric_direction: maximize +- id: ML05 + topic: Investigating dimensionality reduction techniques (PCA, t-SNE, UMAP) for + preserving cluster structure in high-dimensional synthetic datasets + domains: + - machine-learning + - unsupervised-learning + metric_key: silhouette_score + metric_direction: maximize +- id: ML06 + topic: Comparing adaptive learning rate schedules (cosine annealing, step decay, + exponential decay, warm restarts) for logistic regression convergence on binary + classification + domains: + - optimization + - machine-learning + metric_key: convergence_epochs + metric_direction: minimize +- id: ML07 + topic: Evaluating text feature extraction methods (TF-IDF, count vectors, hashing) + combined with Naive Bayes and SVM for document classification on 20newsgroups + domains: + - natural-language-processing + - machine-learning + metric_key: f1_score + metric_direction: maximize +- id: ML08 + topic: Analyzing the impact of class imbalance handling strategies (SMOTE, random + oversampling, class weights, undersampling) on binary classification with scikit-learn + domains: + - machine-learning + - imbalanced-learning + metric_key: balanced_accuracy + metric_direction: maximize +- id: ML09 + topic: Comparing Bayesian optimization vs grid search vs random search for hyperparameter + tuning of random forests on UCI benchmark datasets + domains: + - automl + - machine-learning + metric_key: best_cv_score + metric_direction: maximize +- id: ML10 + topic: Investigating the effectiveness of different cross-validation strategies + (k-fold, stratified, repeated, leave-one-out) for model selection with small sample + sizes + domains: + - machine-learning + - model-selection + metric_key: estimation_bias + metric_direction: minimize +- id: ML11 + topic: Benchmarking unsupervised outlier detection methods (IsolationForest, LocalOutlierFactor, + OneClassSVM, EllipticEnvelope) on UCI datasets with injected anomalies + domains: + - anomaly-detection + - machine-learning + metric_key: roc_auc + metric_direction: maximize +- id: ML12 + topic: Comparing clustering algorithms (k-means, agglomerative, DBSCAN, HDBSCAN, + spectral) on synthetic shapes (moons, circles, blobs, anisotropic) with ground-truth + labels + domains: + - machine-learning + - clustering + metric_key: adjusted_rand_score + metric_direction: maximize +- id: ML13 + topic: Evaluating kernel choices (RBF, polynomial, Matern-3/2, Matern-5/2) for Gaussian + Process regression on synthetic 1-D and 5-D functions with homoscedastic noise + domains: + - machine-learning + - kernel-methods + metric_key: test_nll + metric_direction: minimize +- id: ML14 + topic: Comparing conformal prediction procedures (split-conformal, Mondrian, CQR) + for achieving target coverage on heteroscedastic regression tasks + domains: + - machine-learning + - uncertainty-quantification + metric_key: coverage_gap + metric_direction: minimize +- id: ML15 + topic: Investigating filter-based feature selection (mutual_info_classif, chi2, + f_classif) vs embedded L1-logistic selection on UCI classification datasets with + injected irrelevant features + domains: + - machine-learning + - feature-selection + metric_key: test_accuracy + metric_direction: maximize +- id: ML16 + topic: Comparing multi-armed bandit algorithms (epsilon-greedy, UCB1, Thompson sampling, + Exp3) on synthetic Bernoulli and Gaussian arms under stationary and drift regimes + domains: + - reinforcement-learning + - bandits + metric_key: cumulative_regret + metric_direction: minimize +- id: ML17 + topic: Comparing topic models (LDA, NMF, LSA) on a small subset of 20newsgroups + by topic coherence (c_v) and document-cluster adjusted rand score + domains: + - natural-language-processing + - topic-modeling + metric_key: coherence_cv + metric_direction: maximize +- id: ML18 + topic: Evaluating probability calibration methods (Platt scaling, isotonic regression, + temperature scaling) applied to scikit-learn classifiers (RandomForest, GBM, SVM-RBF) + on UCI benchmarks + domains: + - machine-learning + - calibration + metric_key: ece + metric_direction: minimize +- id: ML19 + topic: Comparing semi-supervised learning strategies (LabelPropagation, LabelSpreading, + SelfTrainingClassifier) on small UCI datasets with 10-20% labeled data + domains: + - machine-learning + - semi-supervised-learning + metric_key: test_accuracy + metric_direction: maximize +- id: ML20 + topic: Benchmarking classical time-series forecasters (AR, ARIMA, SARIMAX, ETS, + Theta) on synthetic seasonal series with varying noise, trend, and seasonality + amplitudes + domains: + - time-series + - forecasting + metric_key: smape + metric_direction: minimize +- id: ML21 + topic: Comparing causal structure learning algorithms (PC, GES, NOTEARS-linear) + on small synthetic linear-SEM DAGs of 5-15 nodes with Gaussian noise + domains: + - causal-inference + - machine-learning + metric_key: structural_hamming_distance + metric_direction: minimize +- id: ML22 + topic: Evaluating active learning query strategies (uncertainty sampling, margin + sampling, query-by-committee, expected-error reduction) for logistic regression + on small UCI pools + domains: + - machine-learning + - active-learning + metric_key: accuracy_at_budget + metric_direction: maximize +- id: ML23 + topic: Comparing learning-to-rank approaches (pointwise linear, pairwise RankNet-lite, + listwise ListMLE-lite) on synthetic query-document pairs with known relevance + grades + domains: + - information-retrieval + - machine-learning + metric_key: ndcg_at_10 + metric_direction: maximize +- id: ML24 + topic: Benchmarking online learning algorithms (SGD logistic, Passive-Aggressive, + online Naive Bayes, Follow-the-Regularized-Leader) on streaming binary classification + with concept drift + domains: + - online-learning + - machine-learning + metric_key: prequential_accuracy + metric_direction: maximize +- id: ML25 + topic: Testing whether a random-feature echo state network (reservoir computing) + predicts Lorenz-63 short-horizon trajectories more accurately than a matched-parameter + MLP and a Gaussian Process at small training-data sizes (N=200-2000 points) + domains: + - dynamical-systems + - machine-learning + - reservoir-computing + metric_key: valid_prediction_time + metric_direction: maximize diff --git a/tasks/physics/manifests/P01.yaml b/tasks/physics/manifests/P01.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c76bc1fb8e6ad6669e083a3002659b99524a7a2e --- /dev/null +++ b/tasks/physics/manifests/P01.yaml @@ -0,0 +1,166 @@ +# ============================================================================ +# P01 — Kaluza-Klein graviton dimuon resonances at e+e- collider (RS model) +# ---------------------------------------------------------------------------- +# Adapts ColliderAgent paper-reproduction prompt: +# /home/shiqiu/ColliderAgent/paper-reproduction/9909255/prompt_figure_2.md +# Source: arXiv:hep-ph/9909255 (Davoudiasl, Hewett, Rizzo) — Randall-Sundrum +# warped extra dimension phenomenology at lepton colliders. +# ============================================================================ + +id: P01 +title: "Reproducing the Randall-Sundrum KK-graviton resonance pattern in $e^+e^- \\to \\mu^+\\mu^-$" +arxiv_id: "hep-ph/9909255" +venue: "ARC-Bench Physics 2026" +paper_asset: null + +synthesis: | + Reproducing the cross-section vs $\sqrt{s}$ scan of $e^+e^- \to \mu^+\mu^-$ + in the Randall-Sundrum warped extra dimension scenario as a benchmark of + the agent's ability to assemble a full Lagrangian -> MC -> analysis + pipeline. The model adds a tower of massive spin-2 KK gravitons + $h^{(n)}_{\alpha\beta}$ ($n=1,\ldots,5$) coupled to the SM + energy-momentum tensor with strength $1/\Lambda_\pi$, on top of the + massless zero-mode coupled with $1/\bar M_{Pl}$. The diagnostic + observable is $\sigma(e^+e^- \to \mu^+\mu^-)$ scanned over + $\sqrt{s}\in\{200,300,\ldots,1200\}$ GeV, which exhibits sharp + resonance peaks at the KK masses $m_n = \{600, 1098, 1592, 2086, 2580\}$ + GeV where they sit in the scan window, and an off-resonance continuum + enhancement above SM Drell-Yan elsewhere. + + A credible study (a) implements the spin-2 graviton-stress-tensor + Lagrangian (FeynRules + UFO or an equivalent analytic propagator), + (b) generates parton-level events at each of the 11 energy points with + proper handling of the unitary-gauge graviton propagator, + (c) extracts $\sigma(\sqrt{s})$ from the MadGraph banner for each run, + (d) plots $\sigma$ vs $\sqrt{s}$ on a log-y axis over [50, 1500] GeV with + range $[400, 5\times 10^6]$ fb, and (e) compares the resonance positions + and continuum heights against the published Figure 2. Research question: + *to what extent does an autonomous HEP pipeline reproduce the RS + KK-graviton resonance spectrum imprinted on dilepton production at a + 500 GeV-1 TeV $e^+e^-$ collider?* + +hypotheses: + - id: H1 + statement: "The first KK resonance $m_1 = 600$ GeV produces a peak in $\\sigma(e^+e^- \\to \\mu^+\\mu^-)$ at $\\sqrt{s} = 600$ GeV that lies within 5% of the input mass." + measurable: true + - id: H2 + statement: "Off-resonance cross sections at $\\sqrt{s}=200$ and $\\sqrt{s}=400$ GeV agree with the published Figure 2 values within 30%, demonstrating correct continuum (zero-mode + virtual KK) interference." + measurable: true + - id: H3 + statement: "At least 2 of the 5 KK resonances within the [50,1500] GeV scan window ($m_1=600$, $m_2=1098$ GeV) are visible as $\\geq 1$-decade enhancements above the SM Drell-Yan baseline at $\\sqrt{s}=m_n$." + measurable: true + +experiment_design: + research_question: "Does the agent reproduce the Randall-Sundrum KK-graviton resonance pattern in $e^+e^- \\to \\mu^+\\mu^-$ across the $\\sqrt{s}=200$-$1200$ GeV scan, matching the published positions and continuum heights of Figure 2 of arXiv:hep-ph/9909255?" + conditions: + - name: "sqrt_s_scan" + description: "Scan $\\sqrt{s} \\in \\{200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200\\}$ GeV with $E^+ = E^- = \\sqrt{s}/2$, 100 events per energy point. KK masses fixed at $m_n = \\{600, 1098, 1592, 2086, 2580\\}$ GeV; $\\Lambda_\\pi = 522$ GeV; $\\bar M_{Pl} = 2.4 \\times 10^{18}$ GeV." + - name: "sm_only_baseline" + description: "Same energy scan with all KK gravitons decoupled ($1/\\Lambda_\\pi \\to 0$) to obtain the pure SM $\\gamma^*/Z$ Drell-Yan baseline for resonance-significance comparison." + baselines: + - "Pure SM $e^+e^- \\to \\mu^+\\mu^-$ (Drell-Yan via $\\gamma^*/Z$) at the same energy points" + metrics: + - name: "cross_section_pb" + direction: "match_reference" + description: "Total $\\sigma(e^+e^- \\to \\mu^+\\mu^-)$ in pb at each of the 11 $\\sqrt{s}$ points." + - name: "peak_position_gev" + direction: "match_reference" + description: "Reconstructed $\\sqrt{s}$ at which the first KK resonance peaks; target = $600 \\pm 30$ GeV." + - name: "resonance_to_continuum_ratio" + direction: "match_reference" + description: "Ratio $\\sigma(\\sqrt{s}=m_1) / \\sigma(\\sqrt{s}=m_1 - 200\\,\\mathrm{GeV})$; should exceed $\\sim 10$." + datasets: + - process_id: "ee_to_mumu_RS" + sqrt_s_TeV: 1.2 + description: "$e^+e^- \\to \\mu^+\\mu^-$ in the RS warped-graviton model." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 3600 + +rubric_path: "experiments/arc_bench/config/physics/rubrics/P01.json" + +# --------------------------------------------------------------------------- +# Agent-mode requirements (consumed by researchclaw.pipeline.requirements_judge +# at stage 15 RESEARCH_DECISION). Schema mirrors B01.yaml: +# id — stable identifier +# type — advisory hint to the LLM judge (numeric | artifact | discussion) +# description — natural-language statement of what must be true post-run +# must_pass — true → unmet ⇒ rerun (1 retry max); false → optional +# +# The five generic must_pass items apply uniformly across P01-P10; the sixth +# is topic-specific (mirrors this manifest's H1). The two must_pass=false +# items reward mechanistic interpretation and MC-reproducibility metadata +# without blocking proceed-vs-rerun on them. +# --------------------------------------------------------------------------- +requirements: + - id: req_results_json + type: artifact + description: >- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metric_key (string), metrics (object + with numeric keys), hypotheses (object with h1/h2/h3 entries each + carrying a `supported` boolean), summary (non-empty string). + must_pass: true + + - id: req_metrics_numeric + type: numeric + description: >- + results.json metrics MUST contain at least 3 numeric (non-null, finite) + values directly relevant to the headline physics observable named in + the experiment_design.metrics list above — these are the numbers the + paper will report in its Results section. + must_pass: true + + - id: req_hypotheses_supported_flags + type: discussion + description: >- + results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` + boolean AND a `details` string ≥ 40 characters quoting the numerical + evidence (specific values + their source artifact) used to reach the + verdict. + must_pass: true + + - id: req_publication_figure + type: artifact + description: >- + At least one publication-quality figure file (PDF or PNG, ≥150 DPI for + raster) exists under figures/ or output/figures/ with axes labeled in + physical units (GeV / pb / fb / dimensionless) and a legend if multiple + series are plotted. The figure must directly support a hypothesis + verdict. + must_pass: true + + - id: req_model_implementation + type: artifact + description: >- + The BSM Lagrangian is implemented either as a FeynRules .fr file + (models/*.fr) with a matching UFO directory (models/*_UFO/ containing + at least particles.py, parameters.py, couplings.py, vertices.py), OR + as analytic Python code that explicitly computes the cross sections + from the Lagrangian terms. A pure SM baseline with no BSM piece is + NOT sufficient. + must_pass: true + + - id: req_kk1_peak_position + type: numeric + description: >- + results.json metrics MUST report a numeric `peak_position_gev` (or equivalent first-KK-resonance position) within ±5% of 600 GeV — i.e. in the interval [570, 630] GeV — after the RS scan over √s ∈ {200..1200} GeV. + must_pass: true + + - id: req_mechanistic_writeup + type: discussion + description: >- + The summary or structured_results section provides a one-paragraph + mechanistic interpretation of WHY the headline observable comes out the + way it does (which interference / propagator structure / cut effect + drives the result). Nice-to-have, not blocking proceed. + must_pass: false + + - id: req_mc_reproducibility + type: discussion + description: >- + results.json or a sibling reproducibility section names: (a) the + MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) + at least one explicit random seed. Required for full reproducibility + but not for scientific correctness. + must_pass: false diff --git a/tasks/physics/manifests/P02.yaml b/tasks/physics/manifests/P02.yaml new file mode 100644 index 0000000000000000000000000000000000000000..86d90ea817209b97a4702cf2f7e23540651ddd22 --- /dev/null +++ b/tasks/physics/manifests/P02.yaml @@ -0,0 +1,165 @@ +# ============================================================================ +# P02 — Heavy Majorana neutrino production at the LHC ($pp \to \mu^\pm N$) +# ---------------------------------------------------------------------------- +# Adapts ColliderAgent paper-reproduction prompt: +# /home/shiqiu/ColliderAgent/paper-reproduction/1308.2209/prompt_figure_3.md +# Source: arXiv:1308.2209 — heavy Majorana neutrino searches at the LHC, +# extending the SM with a single right-handed singlet $N$ that mixes with +# the muon flavor via $V_{\mu N}$. +# ============================================================================ + +id: P02 +title: "Reproducing $pp \\to \\mu^\\pm N$ cross section vs heavy-Majorana-neutrino mass at $\\sqrt{s} = 7, 8, 14$ TeV" +arxiv_id: "1308.2209" +venue: "ARC-Bench Physics 2026" +paper_asset: null + +synthesis: | + Reproducing the LHC sensitivity to a single heavy Majorana neutrino $N$ + in the muon channel as a benchmark of the agent's ability to assemble a + hadron-collider Lagrangian -> MC -> analysis pipeline. The BSM extension + adds a right-handed singlet $N$ mixing with $\nu_\mu$ via the + dimensionless real parameter $V_{\mu N}$, so the relevant interaction is + $\mathcal{L}_N \supset -(g_2/\sqrt{2})\,V_{\mu N}\,(\bar\mu\gamma^\mu P_L + N)\,W^-_\mu + (\text{NC term}) + \text{h.c.}$ The diagnostic observable + is the $W^*$-mediated cross section $\sigma(pp \to \mu^\pm N)$ scanned + over $m_N \in \{100, 200, \ldots, 1000\}$ GeV with $|V_{\mu N}|=1$, at + three LHC energies $\sqrt{s} \in \{7, 8, 14\}$ TeV with the CTEQ6L PDF. + + A credible study (a) implements the heavy-Majorana-neutrino Lagrangian + (FeynRules .fr or analytic UFO modification), (b) sets up MadGraph runs + for the 30 (mass, energy) cells with the CTEQ6L PDF, (c) extracts the + cross section per cell, and (d) plots three side-by-side panels of + $\sigma(m_N)$ on a log-y axis over $[100,1000]$ GeV vs $[0.1, 4\times + 10^4]$ fb. Research question: *does the agent reproduce the steep + PDF-driven fall-off of $\sigma(pp \to \mu^\pm N)$ with $m_N$ and the + correct ordering between $\sqrt{s}=7$, 8, and 14 TeV predicted by + arXiv:1308.2209 Fig. 3?* + +hypotheses: + - id: H1 + statement: "At $m_N = 200$ GeV and $\\sqrt{s}=14$ TeV, $\\sigma(pp \\to \\mu^\\pm N)$ falls within $\\pm 30\\%$ of the published reference value." + measurable: true + - id: H2 + statement: "Cross-section ordering $\\sigma(14\\,\\mathrm{TeV}) > \\sigma(8\\,\\mathrm{TeV}) > \\sigma(7\\,\\mathrm{TeV})$ holds at every mass point, with the 14/7 ratio at $m_N = 500$ GeV in $[5, 30]$." + measurable: true + - id: H3 + statement: "$\\sigma(m_N)$ falls by at least 2 orders of magnitude between $m_N = 100$ GeV and $m_N = 1000$ GeV at every LHC energy." + measurable: true + +experiment_design: + research_question: "Does the agent reproduce the cross-section curves for $pp \\to \\mu^\\pm N$ as a function of the heavy-Majorana-neutrino mass at the 7, 8, and 14 TeV LHC, matching Figure 3 of arXiv:1308.2209?" + conditions: + - name: "lhc_7tev" + description: "$pp$ collisions at $\\sqrt{s}=7$ TeV with CTEQ6L PDF, mass scan $m_N \\in \\{100, 200, 300, 400, 500, 600, 700, 800, 900, 1000\\}$ GeV, $|V_{\\mu N}|=1$." + - name: "lhc_8tev" + description: "Same setup at $\\sqrt{s}=8$ TeV (LHC Run 1 final dataset energy)." + - name: "lhc_14tev" + description: "Same setup at $\\sqrt{s}=14$ TeV (HL-LHC design)." + baselines: + - "Charged-current SM $pp \\to \\mu \\nu_\\mu$ (W production) cross check at the same energies" + metrics: + - name: "cross_section_fb" + direction: "match_reference" + description: "Total $\\sigma(pp \\to \\mu^\\pm N)$ in fb at each (mass, energy) cell." + - name: "energy_ratio_14_to_7" + direction: "match_reference" + description: "$\\sigma(14\\,\\mathrm{TeV})/\\sigma(7\\,\\mathrm{TeV})$ at $m_N = 500$ GeV." + - name: "mass_dropoff_factor" + direction: "match_reference" + description: "$\\sigma(m_N=100)/\\sigma(m_N=1000)$ at each $\\sqrt{s}$." + datasets: + - process_id: "pp_to_muN_via_Wstar" + sqrt_s_TeV: 14 + description: "$pp \\to \\mu^\\pm N$ via $W^*$ at the LHC (CTEQ6L PDF). Three sub-samples for $\\sqrt{s} \\in \\{7,8,14\\}$ TeV." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 3600 + +rubric_path: "experiments/arc_bench/config/physics/rubrics/P02.json" + +# --------------------------------------------------------------------------- +# Agent-mode requirements (consumed by researchclaw.pipeline.requirements_judge +# at stage 15 RESEARCH_DECISION). Schema mirrors B01.yaml: +# id — stable identifier +# type — advisory hint to the LLM judge (numeric | artifact | discussion) +# description — natural-language statement of what must be true post-run +# must_pass — true → unmet ⇒ rerun (1 retry max); false → optional +# +# The five generic must_pass items apply uniformly across P01-P10; the sixth +# is topic-specific (mirrors this manifest's H1). The two must_pass=false +# items reward mechanistic interpretation and MC-reproducibility metadata +# without blocking proceed-vs-rerun on them. +# --------------------------------------------------------------------------- +requirements: + - id: req_results_json + type: artifact + description: >- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metric_key (string), metrics (object + with numeric keys), hypotheses (object with h1/h2/h3 entries each + carrying a `supported` boolean), summary (non-empty string). + must_pass: true + + - id: req_metrics_numeric + type: numeric + description: >- + results.json metrics MUST contain at least 3 numeric (non-null, finite) + values directly relevant to the headline physics observable named in + the experiment_design.metrics list above — these are the numbers the + paper will report in its Results section. + must_pass: true + + - id: req_hypotheses_supported_flags + type: discussion + description: >- + results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` + boolean AND a `details` string ≥ 40 characters quoting the numerical + evidence (specific values + their source artifact) used to reach the + verdict. + must_pass: true + + - id: req_publication_figure + type: artifact + description: >- + At least one publication-quality figure file (PDF or PNG, ≥150 DPI for + raster) exists under figures/ or output/figures/ with axes labeled in + physical units (GeV / pb / fb / dimensionless) and a legend if multiple + series are plotted. The figure must directly support a hypothesis + verdict. + must_pass: true + + - id: req_model_implementation + type: artifact + description: >- + The BSM Lagrangian is implemented either as a FeynRules .fr file + (models/*.fr) with a matching UFO directory (models/*_UFO/ containing + at least particles.py, parameters.py, couplings.py, vertices.py), OR + as analytic Python code that explicitly computes the cross sections + from the Lagrangian terms. A pure SM baseline with no BSM piece is + NOT sufficient. + must_pass: true + + - id: req_xsec_at_mN_200 + type: numeric + description: >- + results.json metrics MUST report a numeric `sigma_pp_to_muN_at_mN200_14TeV_fb` (or equivalent) within ±30% of the published reference value at m_N=200 GeV, √s=14 TeV. + must_pass: true + + - id: req_mechanistic_writeup + type: discussion + description: >- + The summary or structured_results section provides a one-paragraph + mechanistic interpretation of WHY the headline observable comes out the + way it does (which interference / propagator structure / cut effect + drives the result). Nice-to-have, not blocking proceed. + must_pass: false + + - id: req_mc_reproducibility + type: discussion + description: >- + results.json or a sibling reproducibility section names: (a) the + MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) + at least one explicit random seed. Required for full reproducibility + but not for scientific correctness. + must_pass: false diff --git a/tasks/physics/manifests/P03.yaml b/tasks/physics/manifests/P03.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c815c427744aac564488d835748e0457ac319d8b --- /dev/null +++ b/tasks/physics/manifests/P03.yaml @@ -0,0 +1,169 @@ +# ============================================================================ +# P03 — B-L Z' exclusion contours at LHC Run 1 dilepton search +# ---------------------------------------------------------------------------- +# Adapts ColliderAgent paper-reproduction prompt: +# /home/shiqiu/ColliderAgent/paper-reproduction/1605.02910/prompt_figure_1.md +# Source: arXiv:1605.02910 — kinetic-mixing B-L Z' phenomenology, three-panel +# exclusion contours in the $(\tilde g, g_1')$ plane at $\sqrt{s}=8$ TeV. +# ============================================================================ + +id: P03 +title: "Reproducing 8 TeV LHC dilepton exclusion contours for a B-L Z' boson with kinetic mixing" +arxiv_id: "1605.02910" +venue: "ARC-Bench Physics 2026" +paper_asset: null + +synthesis: | + Reproducing the LHC Run 1 sensitivity to a B-L gauge boson Z' with + kinetic mixing as a benchmark of the agent's ability to combine MC + parameter scans with an analytic statistical recast. The model has a + Z' coupling to SM fermions via a left/right combination + $C_{f,L/R}(\tilde g, g_1', M_{Z'})$ where $g_1'$ is the B-L gauge + coupling, $\tilde g$ is the kinetic mixing, and the production cross + section is the exact quadratic form + $\sigma(pp \to Z') = A g_1'^2 + B g_1' \tilde g + C \tilde g^2$ — so + three runs per mass point determine $(A,B,C)$ for the entire $(\tilde + g, g_1')$ plane. The diagnostic observable is the three-panel exclusion + contour at $\mathrm{Sig}\equiv 2(\sqrt{S+B}-\sqrt B) = 2$ for + $M_{Z'} \in \{2.0, 2.5, 3.0\}$ TeV at $\mathcal{L} = 20$ fb$^{-1}$. + + A credible study (a) implements the kinetic-mixing B-L Lagrangian and + $C_{f,L/R}$ couplings, (b) runs the 9 signal MadGraph runs (3 mass + points x 3 coupling points) plus 3 SM Drell-Yan background runs with + $m_{\ell\ell} > 0.9 M_{Z'}$ generator cut, (c) computes the analytic + $Z' \to e^+e^- + \mu^+\mu^-$ branching ratio including 3 degenerate + heavy Majorana neutrinos $m_{\nu_h}=95$ GeV, (d) applies the published + NNLO k-factors $k\in\{1.35,1.40,1.50\}$ and acceptance + $\epsilon_\mathrm{acc}=0.6$, (e) solves the $\mathrm{Sig}=2$ equation + on a 2D grid in $(\tilde g, g_1')$, and (f) plots three side-by-side + contours. Research question: *does the agent reproduce the three-panel + $(\tilde g, g_1')$ exclusion contours at $M_{Z'}=2.0, 2.5, 3.0$ TeV from + Figure 1 of arXiv:1605.02910?* + +hypotheses: + - id: H1 + statement: "For the $M_{Z'}=2$ TeV panel, the exclusion contour crosses the $\\tilde g = 0$ axis at $g_1' \\in [0.10, 0.20]$ (matching the published value within 30%)." + measurable: true + - id: H2 + statement: "The $(A, B, C)$ quadratic coefficients extracted at $M_{Z'}=2$ TeV satisfy $A > 0$ and $|B| \\le 4\\sqrt{AC}$ (positivity of the cross section over the full parameter plane)." + measurable: true + - id: H3 + statement: "The maximum reach in $g_1'$ at $\\tilde g = 0$ degrades by at least a factor of 2 going from $M_{Z'}=2$ TeV to $M_{Z'}=3$ TeV, reflecting the PDF suppression at high mass." + measurable: true + +experiment_design: + research_question: "Does the agent reproduce the three-panel $(\\tilde g, g_1')$ 2$\\sigma$ exclusion contours at $M_{Z'}=2.0, 2.5, 3.0$ TeV from the LHC Run 1 dilepton search at $\\sqrt{s}=8$ TeV with $\\mathcal{L}=20$ fb$^{-1}$ (Figure 1 of arXiv:1605.02910)?" + conditions: + - name: "mzp_2tev" + description: "$M_{Z'}=2$ TeV: 3 signal runs at $(g_1', \\tilde g) \\in \\{(0.10, 0), (0.10, -0.10), (0.15, -0.05)\\}$." + - name: "mzp_2p5tev" + description: "$M_{Z'}=2.5$ TeV: 3 signal runs at the same three $(g_1', \\tilde g)$ points." + - name: "mzp_3tev" + description: "$M_{Z'}=3$ TeV: 3 signal runs at $(g_1', \\tilde g) \\in \\{(0.30, 0), (0.30, -0.30), (0.50, -0.60)\\}$." + baselines: + - "SM Drell-Yan $pp \\to \\ell^+\\ell^-$ at $\\sqrt{s}=8$ TeV with generator-level cut $m_{\\ell\\ell} > 0.9 M_{Z'}$, one per mass point" + metrics: + - name: "exclusion_g1p_at_kinmix0" + direction: "match_reference" + description: "Value of $g_1'$ at which the $\\mathrm{Sig}=2$ contour crosses $\\tilde g = 0$, per mass point." + - name: "quadratic_coefficients_ABC" + direction: "match_reference" + description: "$(A, B, C)$ extracted from the 3 signal runs per mass; positive-definite check." + - name: "br_zprime_to_dilepton" + direction: "match_reference" + description: "Analytic $\\mathrm{BR}(Z' \\to e^+e^-+\\mu^+\\mu^-)$ at the three benchmark masses." + datasets: + - process_id: "pp_to_Zprime_BL_kinmix" + sqrt_s_TeV: 8 + description: "$pp \\to Z'$ with kinetic-mixing B-L Z' at the LHC, dilepton final state. Background: SM Drell-Yan with $m_{\\ell\\ell}$ cut." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 3600 + +rubric_path: "experiments/arc_bench/config/physics/rubrics/P03.json" + +# --------------------------------------------------------------------------- +# Agent-mode requirements (consumed by researchclaw.pipeline.requirements_judge +# at stage 15 RESEARCH_DECISION). Schema mirrors B01.yaml: +# id — stable identifier +# type — advisory hint to the LLM judge (numeric | artifact | discussion) +# description — natural-language statement of what must be true post-run +# must_pass — true → unmet ⇒ rerun (1 retry max); false → optional +# +# The five generic must_pass items apply uniformly across P01-P10; the sixth +# is topic-specific (mirrors this manifest's H1). The two must_pass=false +# items reward mechanistic interpretation and MC-reproducibility metadata +# without blocking proceed-vs-rerun on them. +# --------------------------------------------------------------------------- +requirements: + - id: req_results_json + type: artifact + description: >- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metric_key (string), metrics (object + with numeric keys), hypotheses (object with h1/h2/h3 entries each + carrying a `supported` boolean), summary (non-empty string). + must_pass: true + + - id: req_metrics_numeric + type: numeric + description: >- + results.json metrics MUST contain at least 3 numeric (non-null, finite) + values directly relevant to the headline physics observable named in + the experiment_design.metrics list above — these are the numbers the + paper will report in its Results section. + must_pass: true + + - id: req_hypotheses_supported_flags + type: discussion + description: >- + results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` + boolean AND a `details` string ≥ 40 characters quoting the numerical + evidence (specific values + their source artifact) used to reach the + verdict. + must_pass: true + + - id: req_publication_figure + type: artifact + description: >- + At least one publication-quality figure file (PDF or PNG, ≥150 DPI for + raster) exists under figures/ or output/figures/ with axes labeled in + physical units (GeV / pb / fb / dimensionless) and a legend if multiple + series are plotted. The figure must directly support a hypothesis + verdict. + must_pass: true + + - id: req_model_implementation + type: artifact + description: >- + The BSM Lagrangian is implemented either as a FeynRules .fr file + (models/*.fr) with a matching UFO directory (models/*_UFO/ containing + at least particles.py, parameters.py, couplings.py, vertices.py), OR + as analytic Python code that explicitly computes the cross sections + from the Lagrangian terms. A pure SM baseline with no BSM piece is + NOT sufficient. + must_pass: true + + - id: req_g1prime_threshold + type: numeric + description: >- + results.json metrics MUST report the g1' coordinate where the 2σ exclusion contour at M_Z'=2 TeV crosses the g̃=0 axis, in the interval [0.07, 0.26] (i.e. published [0.10, 0.20] ±30%). + must_pass: true + + - id: req_mechanistic_writeup + type: discussion + description: >- + The summary or structured_results section provides a one-paragraph + mechanistic interpretation of WHY the headline observable comes out the + way it does (which interference / propagator structure / cut effect + drives the result). Nice-to-have, not blocking proceed. + must_pass: false + + - id: req_mc_reproducibility + type: discussion + description: >- + results.json or a sibling reproducibility section names: (a) the + MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) + at least one explicit random seed. Required for full reproducibility + but not for scientific correctness. + must_pass: false diff --git a/tasks/physics/manifests/P04.yaml b/tasks/physics/manifests/P04.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e45a66de90a1af27b7be1d9558c5863ab8f4f4b4 --- /dev/null +++ b/tasks/physics/manifests/P04.yaml @@ -0,0 +1,164 @@ +# ============================================================================ +# P04 — B-L Z' production cross section vs $g_1'$ at fixed kinetic mixing +# ---------------------------------------------------------------------------- +# Adapts ColliderAgent paper-reproduction prompt: +# /home/shiqiu/ColliderAgent/paper-reproduction/1605.02910/prompt_figure_10.md +# Source: arXiv:1605.02910 — same kinetic-mixing B-L Z' model as P03 but +# different diagnostic figure: $\sigma(pp \to Z')$ vs $g_1'$ for fixed +# $\tilde g$ slices at $\sqrt{s}=13$ TeV. +# ============================================================================ + +id: P04 +title: "Reproducing $\\sigma(pp \\to Z')$ vs $g_1'$ slices for kinetic-mixing B-L Z' at $\\sqrt{s}=13$ TeV" +arxiv_id: "1605.02910" +venue: "ARC-Bench Physics 2026" +paper_asset: null + +synthesis: | + Reproducing the $\sqrt{s}=13$ TeV LHC production cross section + $\sigma(pp \to Z')$ as a function of $g_1'$ along three constant- + $\tilde g$ slices, for the same kinetic-mixing B-L $Z'$ model as P03. + The cross section is exactly quadratic in the couplings, + $\sigma(g_1', \tilde g) = A g_1'^2 + B g_1' \tilde g + C \tilde g^2$, + so 3 MadGraph runs per mass point determine $(A,B,C)$ on the entire + $(g_1', \tilde g)$ plane. The diagnostic figure has two panels + (Panel A: $M_{Z'}=2$ TeV with $\tilde g\in\{0, -0.05, -0.10\}$; + Panel B: $M_{Z'}=3$ TeV with $\tilde g\in\{0, -0.30, -0.60\}$). + + A credible study (a) reuses the kinetic-mixing B-L $Z'$ model + (FeynRules + UFO), (b) executes 6 MadGraph runs (3 per mass) at + $\sqrt{s}=13$ TeV, (c) solves the linear system for $(A,B,C)$ at each + mass, (d) computes the analytic curves $\sigma(g_1')|_{\tilde g}$ for + the requested slices, and (e) plots two panels with log-y on the + prescribed ranges (Panel A: $g_1' \in [0, 0.20]$, $\sigma \in + [10^{-4}, 10^{-1}]$ pb; Panel B: $g_1' \in [0, 0.70]$, $\sigma \in + [10^{-4}, 10^{1}]$ pb). Research question: *does the agent recover the + quadratic-in-couplings parameterization of $Z'$ production well enough + to reproduce the constant-$\tilde g$ cross-section slices in Figure 10 + of arXiv:1605.02910?* + +hypotheses: + - id: H1 + statement: "At Panel A ($M_{Z'}=2$ TeV, $\\tilde g = 0$, $g_1' = 0.10$), the reconstructed $\\sigma(pp \\to Z')$ lies within $\\pm 30\\%$ of the published value." + measurable: true + - id: H2 + statement: "The curve at $\\tilde g = -0.10$ in Panel A lies above the $\\tilde g = 0$ curve at $g_1' = 0.05$ but below at $g_1' = 0.20$, demonstrating the destructive-then-constructive interference pattern of the kinetic mixing." + measurable: true + - id: H3 + statement: "$\\sigma$ at ($M_{Z'}=2$ TeV, $g_1'=0.10$, $\\tilde g=0$) exceeds $\\sigma$ at ($M_{Z'}=3$ TeV, $g_1'=0.10$, $\\tilde g=0$) by a factor in $[10, 200]$, reflecting the steep PDF suppression." + measurable: true + +experiment_design: + research_question: "Does the agent reproduce $\\sigma(pp \\to Z')$ vs $g_1'$ for fixed-$\\tilde g$ slices at $M_{Z'}=2$ and 3 TeV at $\\sqrt{s}=13$ TeV (Figure 10 of arXiv:1605.02910)?" + conditions: + - name: "mzp_2tev_3runs" + description: "$M_{Z'}=2$ TeV at $\\sqrt{s}=13$ TeV: 3 signal runs at $(g_1', \\tilde g) \\in \\{(0.10, 0), (0.10, -0.10), (0.15, -0.05)\\}$." + - name: "mzp_3tev_3runs" + description: "$M_{Z'}=3$ TeV at $\\sqrt{s}=13$ TeV: 3 signal runs at $(g_1', \\tilde g) \\in \\{(0.30, 0), (0.30, -0.30), (0.50, -0.60)\\}$." + baselines: + - "Self-consistency cross-check: predicted $\\sigma$ at the 3 input points must reproduce the simulated $\\sigma$ exactly (closure of the $A,B,C$ fit)" + metrics: + - name: "cross_section_pb" + direction: "match_reference" + description: "$\\sigma(pp \\to Z')$ in pb on the requested slice points." + - name: "quadratic_coefficients_ABC_13tev" + direction: "match_reference" + description: "$(A, B, C)$ coefficients per mass point at $\\sqrt{s}=13$ TeV." + - name: "mass_pdf_suppression_ratio" + direction: "match_reference" + description: "$\\sigma(M_{Z'}=2)/\\sigma(M_{Z'}=3)$ at $\\tilde g = 0$, $g_1' = 0.10$." + datasets: + - process_id: "pp_to_Zprime_BL_kinmix_13tev" + sqrt_s_TeV: 13 + description: "$pp \\to Z'$ for the kinetic-mixing B-L Z' model at LHC Run 2 energy." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 3600 + +rubric_path: "experiments/arc_bench/config/physics/rubrics/P04.json" + +# --------------------------------------------------------------------------- +# Agent-mode requirements (consumed by researchclaw.pipeline.requirements_judge +# at stage 15 RESEARCH_DECISION). Schema mirrors B01.yaml: +# id — stable identifier +# type — advisory hint to the LLM judge (numeric | artifact | discussion) +# description — natural-language statement of what must be true post-run +# must_pass — true → unmet ⇒ rerun (1 retry max); false → optional +# +# The five generic must_pass items apply uniformly across P01-P10; the sixth +# is topic-specific (mirrors this manifest's H1). The two must_pass=false +# items reward mechanistic interpretation and MC-reproducibility metadata +# without blocking proceed-vs-rerun on them. +# --------------------------------------------------------------------------- +requirements: + - id: req_results_json + type: artifact + description: >- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metric_key (string), metrics (object + with numeric keys), hypotheses (object with h1/h2/h3 entries each + carrying a `supported` boolean), summary (non-empty string). + must_pass: true + + - id: req_metrics_numeric + type: numeric + description: >- + results.json metrics MUST contain at least 3 numeric (non-null, finite) + values directly relevant to the headline physics observable named in + the experiment_design.metrics list above — these are the numbers the + paper will report in its Results section. + must_pass: true + + - id: req_hypotheses_supported_flags + type: discussion + description: >- + results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` + boolean AND a `details` string ≥ 40 characters quoting the numerical + evidence (specific values + their source artifact) used to reach the + verdict. + must_pass: true + + - id: req_publication_figure + type: artifact + description: >- + At least one publication-quality figure file (PDF or PNG, ≥150 DPI for + raster) exists under figures/ or output/figures/ with axes labeled in + physical units (GeV / pb / fb / dimensionless) and a legend if multiple + series are plotted. The figure must directly support a hypothesis + verdict. + must_pass: true + + - id: req_model_implementation + type: artifact + description: >- + The BSM Lagrangian is implemented either as a FeynRules .fr file + (models/*.fr) with a matching UFO directory (models/*_UFO/ containing + at least particles.py, parameters.py, couplings.py, vertices.py), OR + as analytic Python code that explicitly computes the cross sections + from the Lagrangian terms. A pure SM baseline with no BSM piece is + NOT sufficient. + must_pass: true + + - id: req_xsec_Zprime_2TeV + type: numeric + description: >- + results.json metrics MUST report `sigma_pp_to_Zprime_2TeV_panelA_fb` (at M_Z'=2 TeV, g̃=0, g1'=0.10) within ±30% of the published Panel A reference cross section. + must_pass: true + + - id: req_mechanistic_writeup + type: discussion + description: >- + The summary or structured_results section provides a one-paragraph + mechanistic interpretation of WHY the headline observable comes out the + way it does (which interference / propagator structure / cut effect + drives the result). Nice-to-have, not blocking proceed. + must_pass: false + + - id: req_mc_reproducibility + type: discussion + description: >- + results.json or a sibling reproducibility section names: (a) the + MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) + at least one explicit random seed. Required for full reproducibility + but not for scientific correctness. + must_pass: false diff --git a/tasks/physics/manifests/P05.yaml b/tasks/physics/manifests/P05.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d9cc033f001439f67903e27a8a1121fbc60c62e3 --- /dev/null +++ b/tasks/physics/manifests/P05.yaml @@ -0,0 +1,167 @@ +# ============================================================================ +# P05 — ALP EFT $E_T^\mathrm{miss}$ distribution in $pp \to a W^\pm \gamma$ +# ---------------------------------------------------------------------------- +# Adapts ColliderAgent paper-reproduction prompt: +# /home/shiqiu/ColliderAgent/paper-reproduction/1701.05379/prompt_figure_8.md +# Source: arXiv:1701.05379 — bosonic ALP EFT at the LHC, MET shape in +# $pp \to a W^\pm \gamma$ as a probe of $c_{\tilde W}$ (with photophobic +# choice $c_{\tilde B} = -\tan^2\theta_W \cdot c_{\tilde W}$). +# ============================================================================ + +id: P05 +title: "Reproducing the normalized $E_T^\\mathrm{miss}$ shape in $pp \\to a W^\\pm \\gamma$ from a photophobic ALP EFT" +arxiv_id: "1701.05379" +venue: "ARC-Bench Physics 2026" +paper_asset: null + +synthesis: | + Reproducing the normalized $E_T^\mathrm{miss}$ distribution for the + process $pp \to a W^\pm (\to \ell^\pm \nu)\, \gamma$ at $\sqrt{s}=13$ + TeV in the bosonic ALP EFT, as a benchmark of the agent's ability to + handle a higher-dimensional EFT operator and parton-level missing-energy + reconstruction. The Lagrangian is + $\delta\mathcal{L}_a = c_{\tilde W} \mathcal{A}_{\tilde W} + + c_{\tilde B} \mathcal{A}_{\tilde B}$ with the photophobic constraint + $c_{\tilde B} = -\tan^2\theta_W \cdot c_{\tilde W}$ (so the + $a\gamma\gamma$ coupling vanishes), giving a single free Wilson + coefficient $c_{\tilde W}$. The diagnostic observable is the + normalized parton-level $E_T^\mathrm{miss} = |\vec p_T^{\,a} + + \vec p_T^{\,\nu}|$ shape in 50 bins over [0, 1000] GeV, with + generation cuts $p_T^{\gamma,\ell} > 20$ GeV, $|\eta^{\gamma,\ell}| < 2.5$. + + A credible study (a) implements the photophobic ALP EFT (FeynRules + bosonic operators with $c_{\tilde B} = -\tan^2\theta_W \cdot c_{\tilde W}$), + (b) generates 500k LHE events at $f_a = 1000$ GeV, $m_a = 0.001$ GeV, + $c_{\tilde W} = 1$ with the nn23lo1 PDF, (c) sums $\vec p_T^{\,a}$ + + $\vec p_T^{\,\nu}$ from invisible final-state particles to reconstruct + $E_T^\mathrm{miss}$, and (d) plots the normalized histogram on log-y + $[10^{-4}, 1]$ over [0, 1000] GeV. Research question: *does the agent + reproduce the falling-then-flattening MET shape characteristic of a + derivative ALP coupling versus the steeply falling SM-like baseline, + matching Figure 8 of arXiv:1701.05379?* + +hypotheses: + - id: H1 + statement: "The peak of the normalized $E_T^\\mathrm{miss}$ distribution lies in the bin range $[100, 250]$ GeV, consistent with the published shape." + measurable: true + - id: H2 + statement: "The integrated normalized fraction of events with $E_T^\\mathrm{miss} > 500$ GeV is in $[0.05, 0.30]$ at $c_{\\tilde W}=1$, $f_a=1$ TeV (long high-MET tail from the derivative ALP coupling)." + measurable: true + - id: H3 + statement: "Acceptance after the published photon and lepton cuts ($p_T > 20$ GeV, $|\\eta| < 2.5$) is $\\geq 50\\%$ of generated events." + measurable: true + +experiment_design: + research_question: "Does the agent reproduce the parton-level $E_T^\\mathrm{miss}$ shape for $pp \\to a W^\\pm \\gamma$, $W \\to \\ell\\nu$ at $\\sqrt{s}=13$ TeV under the photophobic ALP EFT (Figure 8 of arXiv:1701.05379)?" + conditions: + - name: "alp_cw1_fa1tev" + description: "Bosonic ALP EFT at $c_{\\tilde W}=1$, $c_{\\tilde B}=-\\tan^2\\theta_W$, $f_a=1000$ GeV, $m_a=0.001$ GeV, 500k events at $\\sqrt{s}=13$ TeV with nn23lo1 PDF." + - name: "alp_cw_variant" + description: "Sanity-check run at $c_{\\tilde W}=2$, $f_a=1000$ GeV; cross section should scale as $c_{\\tilde W}^2$ but normalized shape should be invariant." + baselines: + - "Shape-only normalization is its own baseline: deviation between $c_{\\tilde W}=1$ and $c_{\\tilde W}=2$ shapes should be statistical only" + metrics: + - name: "etmiss_peak_position_gev" + direction: "match_reference" + description: "$E_T^\\mathrm{miss}$ bin center of the peak of the normalized histogram." + - name: "high_metmiss_tail_fraction" + direction: "match_reference" + description: "Fraction of normalized events with $E_T^\\mathrm{miss} > 500$ GeV." + - name: "cut_acceptance" + direction: "match_reference" + description: "Acceptance fraction after photon + lepton fiducial cuts." + datasets: + - process_id: "pp_to_aWgamma_Wlnu" + sqrt_s_TeV: 13 + description: "Parton-level $pp \\to a W^\\pm \\gamma$ with $W \\to \\ell\\nu$ in the photophobic ALP EFT." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 3600 + +rubric_path: "experiments/arc_bench/config/physics/rubrics/P05.json" + +# --------------------------------------------------------------------------- +# Agent-mode requirements (consumed by researchclaw.pipeline.requirements_judge +# at stage 15 RESEARCH_DECISION). Schema mirrors B01.yaml: +# id — stable identifier +# type — advisory hint to the LLM judge (numeric | artifact | discussion) +# description — natural-language statement of what must be true post-run +# must_pass — true → unmet ⇒ rerun (1 retry max); false → optional +# +# The five generic must_pass items apply uniformly across P01-P10; the sixth +# is topic-specific (mirrors this manifest's H1). The two must_pass=false +# items reward mechanistic interpretation and MC-reproducibility metadata +# without blocking proceed-vs-rerun on them. +# --------------------------------------------------------------------------- +requirements: + - id: req_results_json + type: artifact + description: >- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metric_key (string), metrics (object + with numeric keys), hypotheses (object with h1/h2/h3 entries each + carrying a `supported` boolean), summary (non-empty string). + must_pass: true + + - id: req_metrics_numeric + type: numeric + description: >- + results.json metrics MUST contain at least 3 numeric (non-null, finite) + values directly relevant to the headline physics observable named in + the experiment_design.metrics list above — these are the numbers the + paper will report in its Results section. + must_pass: true + + - id: req_hypotheses_supported_flags + type: discussion + description: >- + results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` + boolean AND a `details` string ≥ 40 characters quoting the numerical + evidence (specific values + their source artifact) used to reach the + verdict. + must_pass: true + + - id: req_publication_figure + type: artifact + description: >- + At least one publication-quality figure file (PDF or PNG, ≥150 DPI for + raster) exists under figures/ or output/figures/ with axes labeled in + physical units (GeV / pb / fb / dimensionless) and a legend if multiple + series are plotted. The figure must directly support a hypothesis + verdict. + must_pass: true + + - id: req_model_implementation + type: artifact + description: >- + The BSM Lagrangian is implemented either as a FeynRules .fr file + (models/*.fr) with a matching UFO directory (models/*_UFO/ containing + at least particles.py, parameters.py, couplings.py, vertices.py), OR + as analytic Python code that explicitly computes the cross sections + from the Lagrangian terms. A pure SM baseline with no BSM piece is + NOT sufficient. + must_pass: true + + - id: req_met_peak_bin + type: numeric + description: >- + results.json metrics MUST report the peak bin of the normalized E_T^miss distribution; the bin center must lie in [100, 250] GeV and the distribution must integrate to 1.0 (normalized). + must_pass: true + + - id: req_mechanistic_writeup + type: discussion + description: >- + The summary or structured_results section provides a one-paragraph + mechanistic interpretation of WHY the headline observable comes out the + way it does (which interference / propagator structure / cut effect + drives the result). Nice-to-have, not blocking proceed. + must_pass: false + + - id: req_mc_reproducibility + type: discussion + description: >- + results.json or a sibling reproducibility section names: (a) the + MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) + at least one explicit random seed. Required for full reproducibility + but not for scientific correctness. + must_pass: false diff --git a/tasks/physics/manifests/P06.yaml b/tasks/physics/manifests/P06.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7959ec5e59f12225e660cce9883f3693918dd379 --- /dev/null +++ b/tasks/physics/manifests/P06.yaml @@ -0,0 +1,169 @@ +# ============================================================================ +# P06 — $U_1$ vector leptoquark $m_T$ recast in $pp \to \tau\nu$ (ATLAS+CMS) +# ---------------------------------------------------------------------------- +# Adapts ColliderAgent paper-reproduction prompt: +# /home/shiqiu/ColliderAgent/paper-reproduction/1811.07920/prompt_figure_3.md +# Source: arXiv:1811.07920 — vector leptoquark $U_1$ as resolution of +# $R_{D^{(*)}}$ anomaly; recast of ATLAS + CMS $\tau\nu$ searches. +# ============================================================================ + +id: P06 +title: "Reproducing $U_1$ vector-leptoquark $\\sqrt{|g_c^* g_b|}$ vs $M_{U_1}$ exclusion from ATLAS+CMS $pp \\to \\tau\\nu$" +arxiv_id: "1811.07920" +venue: "ARC-Bench Physics 2026" +paper_asset: null + +synthesis: | + Reproducing the LHC sensitivity to a $U_1$ vector leptoquark from the + $pp \to \tau\nu$ channel via $bc \to U_1 \to \tau\nu$ exchange, + combining ATLAS + CMS searches in a binned profile-likelihood, as a + benchmark of the agent's ability to (i) generate LQ events with + bottom-quark and charm-quark initial-state partons, (ii) run two + parallel detector-card simulations (Delphes ATLAS card vs CMS card), + (iii) recast experimental data into a profile-likelihood fit. The + Lagrangian couples $U_1$ to $(\bar c \gamma^\mu P_L \nu_\tau)$ and + $(\bar b \gamma^\mu P_L \tau)$ with couplings $g_c, g_b$, and the + diagnostic figure is the 2$\sigma$ exclusion contour in the + $(\sqrt{|g_c^* g_b|}, M_{U_1})$ plane along with the LH and RH + $R_{D^{(*)}}$ flavor-anomaly bands. + + A credible study (a) implements the $U_1$ Lagrangian (FeynRules .fr + + UFO), (b) runs MadGraph + Pythia8 + Delphes for $M_{U_1} \in \{750, + 1000, 1250, 1500, 2000, 2500, 3000, 4000, 5000\}$ GeV with both ATLAS + and CMS cards (LHCO output), (c) applies the published ATLAS and CMS + $\tau$-channel selections (lepton veto, hadronic-$\tau$ requirement, + $E_T^\mathrm{miss}$ cut, $m_T$ cut), (d) builds a binned signal + template that scales as $g^4$ and runs the per-bin profile likelihood + with nuisance parameters $\theta_i$, (e) combines ATLAS + CMS, finds + the 2$\sigma$ exclusion via $-2\Delta\ln\mathcal{L} = 4$, and (f) + overlays the analytic LH band $g_\mathrm{LH}(M) = (M/v)\sqrt{2 V_{cb}\, + \epsilon_L}$ with $\epsilon_L = 0.11 \pm 0.02$ and the analogous RH band. + Research question: *does the agent reproduce the $(\sqrt{|g_c^* g_b|}, + M_{U_1})$ exclusion contour and the $R_{D^{(*)}}$ overlay bands of + Figure 3 of arXiv:1811.07920?* + +hypotheses: + - id: H1 + statement: "The combined ATLAS+CMS 2$\\sigma$ exclusion at $M_{U_1} = 1$ TeV gives $\\sqrt{|g_c^* g_b|} \\in [0.3, 1.5]$, matching the published value within 30%." + measurable: true + - id: H2 + statement: "The reconstructed exclusion contour intersects the LH $R_{D^{(*)}}$ band ($\\epsilon_L = 0.11$) at an excluded mass $M_{U_1}^* \\in [1.0, 2.5]$ TeV." + measurable: true + - id: H3 + statement: "Selection efficiency on signal events is $\\geq 5\\%$ at $M_{U_1}=1$ TeV in both ATLAS and CMS analyses (gauge of pipeline working end-to-end through Delphes)." + measurable: true + +experiment_design: + research_question: "Does the agent reproduce the 2$\\sigma$ exclusion contour for the $U_1$ vector leptoquark in the $(\\sqrt{|g_c^* g_b|}, M_{U_1})$ plane from combined ATLAS+CMS $pp \\to \\tau\\nu$ recast (Figure 3 of arXiv:1811.07920)?" + conditions: + - name: "atlas_recast" + description: "Mass scan $M_{U_1} \\in \\{750, 1000, 1250, 1500, 2000, 2500, 3000, 4000, 5000\\}$ GeV at $\\sqrt{s}=13$ TeV, 10k events each, Pythia8 shower, Delphes ATLAS card, LHCO output. Selection: lepton veto, hadronic-$\\tau$ $p_T > 80$ GeV, $|\\eta_\\tau| < 2.3$, $E_T^\\mathrm{miss} > 150$ GeV, $m_T > 250$ GeV." + - name: "cms_recast" + description: "Same mass scan with Delphes CMS card; selection: lepton veto, $\\tau$ $p_T>80$ GeV, $|\\eta|<2.1$, $E_T^\\mathrm{miss}>200$ GeV, $0.7 < p_T^\\tau / E_T^\\mathrm{miss} < 1.3$, $\\Delta\\phi(\\tau, E_T^\\mathrm{miss}) > 2.4$, $m_T > 320$ GeV." + baselines: + - "Published ATLAS background table (22 bins, 250-3200 GeV log-spaced) and CMS background table (3 bins) act as the SM-only null hypothesis" + metrics: + - name: "exclusion_coupling_at_1tev" + direction: "match_reference" + description: "$\\sqrt{|g_c^* g_b|}$ at the 2$\\sigma$ exclusion boundary at $M_{U_1}=1$ TeV from combined fit." + - name: "selection_efficiency" + direction: "match_reference" + description: "Fraction of generated signal events passing the ATLAS or CMS selection at $M_{U_1}=1$ TeV." + - name: "rdstar_band_intersection_tev" + direction: "match_reference" + description: "Mass at which the LH $R_{D^{(*)}}$ band crosses the exclusion contour." + datasets: + - process_id: "pp_to_taunu_via_U1" + sqrt_s_TeV: 13 + description: "$pp \\to \\tau\\nu$ via $bc \\to U_1$ exchange. Two detector simulations (Delphes ATLAS, Delphes CMS)." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 3600 + +rubric_path: "experiments/arc_bench/config/physics/rubrics/P06.json" + +# --------------------------------------------------------------------------- +# Agent-mode requirements (consumed by researchclaw.pipeline.requirements_judge +# at stage 15 RESEARCH_DECISION). Schema mirrors B01.yaml: +# id — stable identifier +# type — advisory hint to the LLM judge (numeric | artifact | discussion) +# description — natural-language statement of what must be true post-run +# must_pass — true → unmet ⇒ rerun (1 retry max); false → optional +# +# The five generic must_pass items apply uniformly across P01-P10; the sixth +# is topic-specific (mirrors this manifest's H1). The two must_pass=false +# items reward mechanistic interpretation and MC-reproducibility metadata +# without blocking proceed-vs-rerun on them. +# --------------------------------------------------------------------------- +requirements: + - id: req_results_json + type: artifact + description: >- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metric_key (string), metrics (object + with numeric keys), hypotheses (object with h1/h2/h3 entries each + carrying a `supported` boolean), summary (non-empty string). + must_pass: true + + - id: req_metrics_numeric + type: numeric + description: >- + results.json metrics MUST contain at least 3 numeric (non-null, finite) + values directly relevant to the headline physics observable named in + the experiment_design.metrics list above — these are the numbers the + paper will report in its Results section. + must_pass: true + + - id: req_hypotheses_supported_flags + type: discussion + description: >- + results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` + boolean AND a `details` string ≥ 40 characters quoting the numerical + evidence (specific values + their source artifact) used to reach the + verdict. + must_pass: true + + - id: req_publication_figure + type: artifact + description: >- + At least one publication-quality figure file (PDF or PNG, ≥150 DPI for + raster) exists under figures/ or output/figures/ with axes labeled in + physical units (GeV / pb / fb / dimensionless) and a legend if multiple + series are plotted. The figure must directly support a hypothesis + verdict. + must_pass: true + + - id: req_model_implementation + type: artifact + description: >- + The BSM Lagrangian is implemented either as a FeynRules .fr file + (models/*.fr) with a matching UFO directory (models/*_UFO/ containing + at least particles.py, parameters.py, couplings.py, vertices.py), OR + as analytic Python code that explicitly computes the cross sections + from the Lagrangian terms. A pure SM baseline with no BSM piece is + NOT sufficient. + must_pass: true + + - id: req_lq_coupling_band + type: numeric + description: >- + results.json metrics MUST report the 2σ combined ATLAS+CMS √(g_c* g_b) exclusion band at M_U1=1 TeV; the band lower edge must be in [0.21, 0.39] and the upper edge in [1.05, 1.95] (published [0.3, 1.5] ±30%). + must_pass: true + + - id: req_mechanistic_writeup + type: discussion + description: >- + The summary or structured_results section provides a one-paragraph + mechanistic interpretation of WHY the headline observable comes out the + way it does (which interference / propagator structure / cut effect + drives the result). Nice-to-have, not blocking proceed. + must_pass: false + + - id: req_mc_reproducibility + type: discussion + description: >- + results.json or a sibling reproducibility section names: (a) the + MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) + at least one explicit random seed. Required for full reproducibility + but not for scientific correctness. + must_pass: false diff --git a/tasks/physics/manifests/P07.yaml b/tasks/physics/manifests/P07.yaml new file mode 100644 index 0000000000000000000000000000000000000000..313fb9d8084ef639c031e3f46bc187bc65770bfd --- /dev/null +++ b/tasks/physics/manifests/P07.yaml @@ -0,0 +1,169 @@ +# ============================================================================ +# P07 — Scalar leptoquark $m_{ej}$ resonance at the 13 TeV LHC (LUXlep PDF) +# ---------------------------------------------------------------------------- +# Adapts ColliderAgent paper-reproduction prompt: +# /home/shiqiu/ColliderAgent/paper-reproduction/2005.06475/prompt_figure_2.md +# Source: arXiv:2005.06475 — single resonant LQ production via lepton-PDF +# (LUXlep) inside the proton, $pp \to \mathrm{LQ} \to ej$ resonance peak. +# ============================================================================ + +id: P07 +title: "Reproducing the $m_{ej}$ resonance in $pp \\to \\mathrm{LQ} \\to ej$ via the LUXlep proton lepton PDF" +arxiv_id: "2005.06475" +venue: "ARC-Bench Physics 2026" +paper_asset: null + +synthesis: | + Reproducing the LHC sensitivity to a scalar leptoquark in the + $pp \to \mathrm{LQ} \to ej$ channel as a benchmark of the agent's + ability to assemble a full Lagrangian -> MC -> shower -> detector -> + analysis pipeline using a non-trivial PDF (LUXlep, with electrons inside + the proton). The Lagrangian + $\mathcal{L} = \lambda_{eu}\,\mathrm{LQ}_{eu}\,e_R^T i\sigma^2 u_R + + \mathrm{h.c.}$ describes an $SU(2)_L$-singlet, color-triplet, + $Q=-1/3$ scalar leptoquark coupling to a right-handed lepton-quark pair. + The diagnostic observable is the reconstructed invariant mass $m_{ej}$ + of the leading electron and leading jet, peaked at the input + $M_\mathrm{LQ}=3$ TeV with $\Gamma_\mathrm{LQ}=60$ GeV after Pythia8 + + Delphes ATLAS detector smearing. + + A credible study (a) implements the scalar-LQ Lagrangian (FeynRules .fr + + UFO), (b) generates 100k events at $\sqrt{s}=13$ TeV with the LUXlep + PDF (proton content redefined to include leptons + photon) and + generation cuts $p_T(\ell, j) > 500$ GeV, $|\eta| < 2.5$, (c) applies + the Pythia8 lepton-to-photon workaround (replace initial-state leptons + with photons in the LHE; set Check:event=off; shower; Delphes ATLAS + card with anti-$k_T$ R=0.4 jets; LHCO output), (d) selects events with + exactly one $e$ ($p_T>500$ GeV, $|\eta|<2.5$), one $j$ ($p_T>500$ GeV, + $|\eta|<2.5$), $E_T^\mathrm{miss} < 50$ GeV, lepton veto, jet veto, + (e) histograms $m_{ej}$ in 100 GeV bins from 0 to 5000 GeV weighted by + $\sigma\mathcal{L}/N_\mathrm{gen}$ at $\mathcal{L}=100$ fb$^{-1}$, and + (f) plots on log-y $[10^{-3}, 5\times 10^2]$ events/bin over [1000, + 5000] GeV. Research question: *does the agent reproduce the scalar-LQ + resonance peak at $m_{ej} \approx 3$ TeV from Figure 2 of arXiv:2005.06475?* + +hypotheses: + - id: H1 + statement: "Reconstructed $m_{ej}$ peaks within $\\pm 5\\%$ of the input $M_\\mathrm{LQ}=3000$ GeV after Pythia8+Delphes smearing (i.e., peak in [2850, 3150] GeV)." + measurable: true + - id: H2 + statement: "The generation-level $p_T(\\ell, j) > 500$ GeV cut yields $\\geq 50\\%$ acceptance for the benchmark $M_\\mathrm{LQ}=3$ TeV signal." + measurable: true + - id: H3 + statement: "Total event count in the [2.5, 3.5] TeV $m_{ej}$ window at $\\mathcal{L} = 100$ fb$^{-1}$ matches the published value within $\\pm 30\\%$." + measurable: true + +experiment_design: + research_question: "Does the agent reproduce the $m_{ej}$ invariant-mass distribution for $pp \\to \\mathrm{LQ} \\to ej$ at $\\sqrt{s}=13$ TeV with LUXlep PDF and $M_\\mathrm{LQ}=3$ TeV (Figure 2 of arXiv:2005.06475)?" + conditions: + - name: "lq_3tev_lambda1" + description: "Scalar LQ at $M_\\mathrm{LQ}=3000$ GeV, $\\Gamma_\\mathrm{LQ}=60$ GeV, $\\lambda_{eu}=1$, 100k events at $\\sqrt{s}=13$ TeV, LUXlep PDF, $p_T > 500$ GeV gen cuts, Pythia8 + Delphes ATLAS, LHCO output." + - name: "lq_2tev_check" + description: "Cross-check at $M_\\mathrm{LQ}=2000$ GeV with $\\lambda_{eu}=1$, same setup; verify that $m_{ej}$ peak shifts to 2 TeV." + baselines: + - "Background-only sanity check: $\\lambda_{eu}=0$ (LQ decoupled) should produce zero $m_{ej}$ events above the gen-level cut" + metrics: + - name: "peak_position_gev" + direction: "match_reference" + description: "Bin-center of the maximum of the reconstructed $m_{ej}$ histogram for the 3 TeV signal." + - name: "acceptance_pt500" + direction: "match_reference" + description: "Fraction of generated events with both lepton and jet $p_T > 500$ GeV at $M_\\mathrm{LQ}=3$ TeV." + - name: "integrated_yield_in_window" + direction: "match_reference" + description: "Sum of weighted events in $m_{ej} \\in [2500, 3500]$ GeV at $\\mathcal{L}=100$ fb$^{-1}$." + datasets: + - process_id: "pp_to_LQ_to_ej_LUXlep" + sqrt_s_TeV: 13 + description: "Single resonant scalar leptoquark production via lepton-quark fusion using LUXlep proton PDF." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 3600 + +rubric_path: "experiments/arc_bench/config/physics/rubrics/P07.json" + +# --------------------------------------------------------------------------- +# Agent-mode requirements (consumed by researchclaw.pipeline.requirements_judge +# at stage 15 RESEARCH_DECISION). Schema mirrors B01.yaml: +# id — stable identifier +# type — advisory hint to the LLM judge (numeric | artifact | discussion) +# description — natural-language statement of what must be true post-run +# must_pass — true → unmet ⇒ rerun (1 retry max); false → optional +# +# The five generic must_pass items apply uniformly across P01-P10; the sixth +# is topic-specific (mirrors this manifest's H1). The two must_pass=false +# items reward mechanistic interpretation and MC-reproducibility metadata +# without blocking proceed-vs-rerun on them. +# --------------------------------------------------------------------------- +requirements: + - id: req_results_json + type: artifact + description: >- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metric_key (string), metrics (object + with numeric keys), hypotheses (object with h1/h2/h3 entries each + carrying a `supported` boolean), summary (non-empty string). + must_pass: true + + - id: req_metrics_numeric + type: numeric + description: >- + results.json metrics MUST contain at least 3 numeric (non-null, finite) + values directly relevant to the headline physics observable named in + the experiment_design.metrics list above — these are the numbers the + paper will report in its Results section. + must_pass: true + + - id: req_hypotheses_supported_flags + type: discussion + description: >- + results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` + boolean AND a `details` string ≥ 40 characters quoting the numerical + evidence (specific values + their source artifact) used to reach the + verdict. + must_pass: true + + - id: req_publication_figure + type: artifact + description: >- + At least one publication-quality figure file (PDF or PNG, ≥150 DPI for + raster) exists under figures/ or output/figures/ with axes labeled in + physical units (GeV / pb / fb / dimensionless) and a legend if multiple + series are plotted. The figure must directly support a hypothesis + verdict. + must_pass: true + + - id: req_model_implementation + type: artifact + description: >- + The BSM Lagrangian is implemented either as a FeynRules .fr file + (models/*.fr) with a matching UFO directory (models/*_UFO/ containing + at least particles.py, parameters.py, couplings.py, vertices.py), OR + as analytic Python code that explicitly computes the cross sections + from the Lagrangian terms. A pure SM baseline with no BSM piece is + NOT sufficient. + must_pass: true + + - id: req_mej_peak_position + type: numeric + description: >- + results.json metrics MUST report the reconstructed m_ej peak position (after Pythia8+Delphes smearing) in the interval [2850, 3150] GeV (input M_LQ=3 TeV ±5%). + must_pass: true + + - id: req_mechanistic_writeup + type: discussion + description: >- + The summary or structured_results section provides a one-paragraph + mechanistic interpretation of WHY the headline observable comes out the + way it does (which interference / propagator structure / cut effect + drives the result). Nice-to-have, not blocking proceed. + must_pass: false + + - id: req_mc_reproducibility + type: discussion + description: >- + results.json or a sibling reproducibility section names: (a) the + MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) + at least one explicit random seed. Required for full reproducibility + but not for scientific correctness. + must_pass: false diff --git a/tasks/physics/manifests/P08.yaml b/tasks/physics/manifests/P08.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0b36816052e1e508824188e5a41e743456fa688b --- /dev/null +++ b/tasks/physics/manifests/P08.yaml @@ -0,0 +1,164 @@ +# ============================================================================ +# P08 — General Z' dimuon cross section vs mass: SSM vs E6-ψ at 13 TeV LHC +# ---------------------------------------------------------------------------- +# Adapts ColliderAgent paper-reproduction prompt: +# /home/shiqiu/ColliderAgent/paper-reproduction/2103.02708/prompt_figure_4.md +# Source: arXiv:2103.02708 — general Z' phenomenology, comparison of the +# Sequential Standard Model (SSM) and E6-derived $Z'_\psi$ benchmarks. +# ============================================================================ + +id: P08 +title: "Reproducing $\\sigma(pp \\to Z' \\to \\mu^+\\mu^-)$ vs $M_{Z'}$ for SSM and E6-$\\psi$ scenarios at $\\sqrt{s}=13$ TeV" +arxiv_id: "2103.02708" +venue: "ARC-Bench Physics 2026" +paper_asset: null + +synthesis: | + Reproducing the dimuon production cross section $\sigma(pp \to Z' \to + \mu^+\mu^-)$ as a function of $M_{Z'}$ for two canonical $Z'$ benchmarks + - the Sequential Standard Model (SSM) where the $Z'$ inherits SM + electroweak couplings, and the $E_6$-derived $Z'_\psi$ with universal + coupling $g_\psi = 0.0942$. The general Lagrangian + $\mathcal{L}_{NP} \supset -Z'_\mu \sum_f \bar f \gamma^\mu (g_{Lf} P_L + + g_{Rf} P_R) f$ has 7 free chiral couplings $g_{Lu}, g_{Ru}, g_{Ld}, + g_{Rd}, g_{Le}, g_{Re}, g_{Lv}$, fixed differently in each benchmark. + The diagnostic observable is the dimuon production cross section + scanned over $M_{Z'} \in \{200, 400, 600, \ldots, 5500\}$ GeV at + $\sqrt{s}=13$ TeV. + + A credible study (a) implements the general $Z'$ model (FeynRules + UFO) + with the 7 couplings as inputs, (b) sets up two parameter cards + encoding the SSM and $E_6$-$\psi$ coupling tables, (c) scans 14 mass + points per benchmark with auto-computed $\Gamma_{Z'}$ (two-body decays + only), (d) extracts the cross section per (mass, scenario) cell, and + (e) plots two curves (SSM = green dotted, $Z'_\psi$ = blue solid) on + log-y over $M_{Z'} \in [200, 5500]$ GeV, $\sigma \in [5\times 10^{-6}, + 5\times 10^{-1}]$ pb. Research question: *does the agent reproduce the + cross-section curves and the well-known SSM $\gtrsim Z'_\psi$ ordering + in dilepton production from Figure 4 of arXiv:2103.02708?* + +hypotheses: + - id: H1 + statement: "$\\sigma_\\mathrm{SSM}(M_{Z'}=2\\,\\mathrm{TeV}) > \\sigma_\\psi(M_{Z'}=2\\,\\mathrm{TeV})$ by a factor in $[3, 30]$ at $\\sqrt{s}=13$ TeV (SSM has larger couplings)." + measurable: true + - id: H2 + statement: "$\\sigma_\\mathrm{SSM}(M_{Z'}=1\\,\\mathrm{TeV})$ falls within $\\pm 30\\%$ of the published reference value." + measurable: true + - id: H3 + statement: "The SSM cross section drops by $\\geq 4$ orders of magnitude between $M_{Z'}=200$ GeV and $M_{Z'}=5500$ GeV (PDF + propagator suppression)." + measurable: true + +experiment_design: + research_question: "Does the agent reproduce $\\sigma(pp \\to Z' \\to \\mu^+\\mu^-)$ vs $M_{Z'}$ for the SSM ($Z'_\\mathrm{SSM}$) and $E_6$-derived $Z'_\\psi$ benchmarks at the 13 TeV LHC (Figure 4 of arXiv:2103.02708)?" + conditions: + - name: "ssm_mass_scan" + description: "$Z'_\\mathrm{SSM}$ with electroweak couplings $g_{Lu} = (e/s_W c_W)(1/2 - 2 s_W^2/3)$, etc. (SM-like). Scan $M_{Z'} \\in \\{200, 400, 600, 800, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500\\}$ GeV at $\\sqrt{s}=13$ TeV." + - name: "e6psi_mass_scan" + description: "$Z'_\\psi$ from $E_6$ with universal coupling $g_\\psi = 0.0942$ for all SM fermions. Same mass scan." + baselines: + - "Internal cross-check: SSM/E6-$\\psi$ ratio should be approximately mass-independent (set by the ratio of squared couplings) far from mass thresholds" + metrics: + - name: "cross_section_pb" + direction: "match_reference" + description: "$\\sigma(pp \\to Z' \\to \\mu^+\\mu^-)$ in pb at each (mass, benchmark) cell." + - name: "ssm_to_e6_ratio_at_2tev" + direction: "match_reference" + description: "$\\sigma_\\mathrm{SSM}/\\sigma_\\psi$ at $M_{Z'}=2$ TeV." + - name: "mass_dropoff_factor_ssm" + direction: "match_reference" + description: "$\\sigma_\\mathrm{SSM}(M=200)/\\sigma_\\mathrm{SSM}(M=5500)$." + datasets: + - process_id: "pp_to_Zprime_to_mumu_general" + sqrt_s_TeV: 13 + description: "$pp \\to Z' \\to \\mu^+\\mu^-$ in the general 7-coupling $Z'$ model. Two benchmark coupling tables: SSM and $E_6$-$\\psi$." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 3600 + +rubric_path: "experiments/arc_bench/config/physics/rubrics/P08.json" + +# --------------------------------------------------------------------------- +# Agent-mode requirements (consumed by researchclaw.pipeline.requirements_judge +# at stage 15 RESEARCH_DECISION). Schema mirrors B01.yaml: +# id — stable identifier +# type — advisory hint to the LLM judge (numeric | artifact | discussion) +# description — natural-language statement of what must be true post-run +# must_pass — true → unmet ⇒ rerun (1 retry max); false → optional +# +# The five generic must_pass items apply uniformly across P01-P10; the sixth +# is topic-specific (mirrors this manifest's H1). The two must_pass=false +# items reward mechanistic interpretation and MC-reproducibility metadata +# without blocking proceed-vs-rerun on them. +# --------------------------------------------------------------------------- +requirements: + - id: req_results_json + type: artifact + description: >- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metric_key (string), metrics (object + with numeric keys), hypotheses (object with h1/h2/h3 entries each + carrying a `supported` boolean), summary (non-empty string). + must_pass: true + + - id: req_metrics_numeric + type: numeric + description: >- + results.json metrics MUST contain at least 3 numeric (non-null, finite) + values directly relevant to the headline physics observable named in + the experiment_design.metrics list above — these are the numbers the + paper will report in its Results section. + must_pass: true + + - id: req_hypotheses_supported_flags + type: discussion + description: >- + results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` + boolean AND a `details` string ≥ 40 characters quoting the numerical + evidence (specific values + their source artifact) used to reach the + verdict. + must_pass: true + + - id: req_publication_figure + type: artifact + description: >- + At least one publication-quality figure file (PDF or PNG, ≥150 DPI for + raster) exists under figures/ or output/figures/ with axes labeled in + physical units (GeV / pb / fb / dimensionless) and a legend if multiple + series are plotted. The figure must directly support a hypothesis + verdict. + must_pass: true + + - id: req_model_implementation + type: artifact + description: >- + The BSM Lagrangian is implemented either as a FeynRules .fr file + (models/*.fr) with a matching UFO directory (models/*_UFO/ containing + at least particles.py, parameters.py, couplings.py, vertices.py), OR + as analytic Python code that explicitly computes the cross sections + from the Lagrangian terms. A pure SM baseline with no BSM piece is + NOT sufficient. + must_pass: true + + - id: req_ssm_psi_ratio + type: numeric + description: >- + results.json metrics MUST report the ratio σ_SSM/σ_ψ at M_Z'=2 TeV, √s=13 TeV, and that ratio MUST be in [3, 30]. + must_pass: true + + - id: req_mechanistic_writeup + type: discussion + description: >- + The summary or structured_results section provides a one-paragraph + mechanistic interpretation of WHY the headline observable comes out the + way it does (which interference / propagator structure / cut effect + drives the result). Nice-to-have, not blocking proceed. + must_pass: false + + - id: req_mc_reproducibility + type: discussion + description: >- + results.json or a sibling reproducibility section names: (a) the + MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) + at least one explicit random seed. Required for full reproducibility + but not for scientific correctness. + must_pass: false diff --git a/tasks/physics/manifests/P09.yaml b/tasks/physics/manifests/P09.yaml new file mode 100644 index 0000000000000000000000000000000000000000..adff03cac533593463b0aa09c9aa3764cc4d5c31 --- /dev/null +++ b/tasks/physics/manifests/P09.yaml @@ -0,0 +1,170 @@ +# ============================================================================ +# P09 — $U_1$ leptoquark angular shape at 3 TeV muon collider +# ---------------------------------------------------------------------------- +# Adapts ColliderAgent paper-reproduction prompt: +# /home/shiqiu/ColliderAgent/paper-reproduction/2104.05720/prompt_figure_11.md +# Source: arXiv:2104.05720 — vector $U_1$ leptoquark phenomenology at +# muon colliders; pseudorapidity shape of $\mu^+\mu^- \to b\bar b$. +# ============================================================================ + +id: P09 +title: "Reproducing the normalized $|\\eta|$ shape in $\\mu^+\\mu^- \\to b\\bar b$ from a $U_1$ vector leptoquark at $\\sqrt{s}=3$ TeV" +arxiv_id: "2104.05720" +venue: "ARC-Bench Physics 2026" +paper_asset: null + +synthesis: | + Reproducing the normalized pseudorapidity shape of the $b$ quark in + $\mu^+\mu^- \to b\bar b$ at a 3 TeV muon collider, comparing SM + ($s$-channel $\gamma^*/Z^*$) against $U_1$ vector-leptoquark + $t$-channel exchange. The Lagrangian has the standard vector-LQ + kinetic term plus + $\mathcal{L} \supset (g_U/\sqrt{2})\,U_1^\mu (\beta_L^{ij}\, + \bar Q_L^i \gamma_\mu L_L^j) + \mathrm{h.c.}$, with only + $\beta_L^{32}$ (b-quark to muon) non-zero. The diagnostic observable + is the normalized $|\eta|$ distribution of the $b$ quark binned in 13 + bins from 0 to 2.5 (narrow [0, 0.1] first bin then uniform 0.2-wide + bins), shown in two side-by-side panels at $\beta_L^{32} = 1.0$ and + $\beta_L^{32} = 0.1$, each overlaying SM (gray bar histogram) with + $m_\mathrm{LQ}=1$ TeV (blue dashed) and $m_\mathrm{LQ}=10$ TeV + (orange dashed). + + A credible study (a) implements the $U_1$ vector-LQ Lagrangian with + $\kappa_U=\tilde\kappa_U=0$ (FeynRules + UFO), (b) runs 5 parton-level + MadGraph runs (1 SM baseline at 500k events with $\beta_L^{32}=0$ and + $m_\mathrm{LQ}=10^5$ GeV; 4 LQ signal runs at $\beta_L^{32}\in + \{1.0, 0.1\}$ x $m_\mathrm{LQ}\in\{1, 10\}$ TeV with 50k events + each), (c) extracts the $b$-quark $|\eta|$ from each event, (d) + histograms into the 13 bins and normalizes to total events per run, + (e) plots two panels side by side with the prescribed style. Research + question: *does the agent reproduce the central-vs-forward angular + redistribution induced by the $t$-channel $U_1$ exchange in + $\mu^+\mu^- \to b\bar b$ from Figure 11 of arXiv:2104.05720?* + +hypotheses: + - id: H1 + statement: "At $\\beta_L^{32} = 1.0$, $m_\\mathrm{LQ} = 1$ TeV, the central-bin ($|\\eta| < 0.1$) normalized fraction exceeds the SM by $\\geq 50\\%$ (LQ $t$-channel pulls events forward+central)." + measurable: true + - id: H2 + statement: "At $\\beta_L^{32}=0.1$, the normalized $|\\eta|$ shape differs from the SM baseline by $\\leq 10\\%$ in every bin (small-coupling regime collapses to SM)." + measurable: true + - id: H3 + statement: "At $\\beta_L^{32}=1.0$, $m_\\mathrm{LQ}=10$ TeV the normalized shape lies between the SM and the $m_\\mathrm{LQ}=1$ TeV curves (decoupling limit)." + measurable: true + +experiment_design: + research_question: "Does the agent reproduce the normalized $|\\eta|$ distributions of $\\mu^+\\mu^- \\to b\\bar b$ for SM and $U_1$ leptoquark signal at $\\sqrt{s}=3$ TeV muon collider, in the two coupling regimes $\\beta_L^{32} \\in \\{1.0, 0.1\\}$ (Figure 11 of arXiv:2104.05720)?" + conditions: + - name: "sm_baseline_3tev" + description: "All $\\beta_L^{ij} = 0$ (LQ decoupled), $m_\\mathrm{LQ} = 10^5$ GeV. 500k events at $\\sqrt{s}=3$ TeV, parton level." + - name: "lq_beta1_3tev" + description: "$\\beta_L^{32} = 1.0$, mass scan $m_\\mathrm{LQ} \\in \\{1, 10\\}$ TeV, 50k events each at $\\sqrt{s}=3$ TeV." + - name: "lq_beta01_3tev" + description: "$\\beta_L^{32} = 0.1$, mass scan $m_\\mathrm{LQ} \\in \\{1, 10\\}$ TeV, 50k events each at $\\sqrt{s}=3$ TeV." + baselines: + - "SM $s$-channel $\\mu^+\\mu^- \\to \\gamma^*/Z^* \\to b\\bar b$ (the $\\beta_L^{32}=0$ run)" + metrics: + - name: "central_bin_excess_fraction" + direction: "match_reference" + description: "Ratio of central-bin ($|\\eta| < 0.1$) normalized event fraction in LQ signal vs SM." + - name: "shape_deviation_l1norm" + direction: "match_reference" + description: "$L^1$ norm $\\sum_i |f_i^\\mathrm{LQ} - f_i^\\mathrm{SM}|$ of normalized shape difference per (mass, coupling)." + - name: "decoupling_distance" + direction: "match_reference" + description: "Shape distance between $m_\\mathrm{LQ}=10$ TeV signal and SM should be smaller than for $m_\\mathrm{LQ}=1$ TeV signal." + datasets: + - process_id: "mumu_to_bbbar_U1" + sqrt_s_TeV: 3 + description: "$\\mu^+\\mu^- \\to b\\bar b$ via SM ($s$-channel) + $U_1$ ($t$-channel) at a 3 TeV muon collider, parton level." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 3600 + +rubric_path: "experiments/arc_bench/config/physics/rubrics/P09.json" + +# --------------------------------------------------------------------------- +# Agent-mode requirements (consumed by researchclaw.pipeline.requirements_judge +# at stage 15 RESEARCH_DECISION). Schema mirrors B01.yaml: +# id — stable identifier +# type — advisory hint to the LLM judge (numeric | artifact | discussion) +# description — natural-language statement of what must be true post-run +# must_pass — true → unmet ⇒ rerun (1 retry max); false → optional +# +# The five generic must_pass items apply uniformly across P01-P10; the sixth +# is topic-specific (mirrors this manifest's H1). The two must_pass=false +# items reward mechanistic interpretation and MC-reproducibility metadata +# without blocking proceed-vs-rerun on them. +# --------------------------------------------------------------------------- +requirements: + - id: req_results_json + type: artifact + description: >- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metric_key (string), metrics (object + with numeric keys), hypotheses (object with h1/h2/h3 entries each + carrying a `supported` boolean), summary (non-empty string). + must_pass: true + + - id: req_metrics_numeric + type: numeric + description: >- + results.json metrics MUST contain at least 3 numeric (non-null, finite) + values directly relevant to the headline physics observable named in + the experiment_design.metrics list above — these are the numbers the + paper will report in its Results section. + must_pass: true + + - id: req_hypotheses_supported_flags + type: discussion + description: >- + results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` + boolean AND a `details` string ≥ 40 characters quoting the numerical + evidence (specific values + their source artifact) used to reach the + verdict. + must_pass: true + + - id: req_publication_figure + type: artifact + description: >- + At least one publication-quality figure file (PDF or PNG, ≥150 DPI for + raster) exists under figures/ or output/figures/ with axes labeled in + physical units (GeV / pb / fb / dimensionless) and a legend if multiple + series are plotted. The figure must directly support a hypothesis + verdict. + must_pass: true + + - id: req_model_implementation + type: artifact + description: >- + The BSM Lagrangian is implemented either as a FeynRules .fr file + (models/*.fr) with a matching UFO directory (models/*_UFO/ containing + at least particles.py, parameters.py, couplings.py, vertices.py), OR + as analytic Python code that explicitly computes the cross sections + from the Lagrangian terms. A pure SM baseline with no BSM piece is + NOT sufficient. + must_pass: true + + - id: req_central_bin_excess + type: numeric + description: >- + results.json metrics MUST report the normalized central-|η|<0.1 fraction for β_L^32=1.0, m_LQ=1 TeV, AND the same fraction for SM, AND the LQ fraction MUST exceed SM by ≥50%. + must_pass: true + + - id: req_mechanistic_writeup + type: discussion + description: >- + The summary or structured_results section provides a one-paragraph + mechanistic interpretation of WHY the headline observable comes out the + way it does (which interference / propagator structure / cut effect + drives the result). Nice-to-have, not blocking proceed. + must_pass: false + + - id: req_mc_reproducibility + type: discussion + description: >- + results.json or a sibling reproducibility section names: (a) the + MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) + at least one explicit random seed. Required for full reproducibility + but not for scientific correctness. + must_pass: false diff --git a/tasks/physics/manifests/P10.yaml b/tasks/physics/manifests/P10.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4bd30bbcc0562c657435475e432b4aa873abd8e1 --- /dev/null +++ b/tasks/physics/manifests/P10.yaml @@ -0,0 +1,174 @@ +# ============================================================================ +# P10 — $U_1$ leptoquark exclusion + discovery contours at 3+14 TeV muon collider +# ---------------------------------------------------------------------------- +# Adapts ColliderAgent paper-reproduction prompt: +# /home/shiqiu/ColliderAgent/paper-reproduction/2104.05720/prompt_figure_12.md +# Source: arXiv:2104.05720 — same $U_1$ LQ as P09 but the diagnostic +# figure is $\beta_L^{32}$ vs $m_\mathrm{LQ}$ exclusion + discovery from +# binned likelihood ratio at two muon-collider energies. +# ============================================================================ + +id: P10 +title: "Reproducing $\\beta_L^{32}$-vs-$m_\\mathrm{LQ}$ exclusion + 5$\\sigma$ discovery contours for $U_1$ at 3 TeV (1 ab$^{-1}$) and 14 TeV (20 ab$^{-1}$) muon colliders" +arxiv_id: "2104.05720" +venue: "ARC-Bench Physics 2026" +paper_asset: null + +synthesis: | + Reproducing the muon-collider sensitivity to a $U_1$ vector + leptoquark in the coupling-vs-mass plane via a binned-likelihood + recast of $\mu^+\mu^- \to b\bar b$, as a benchmark of the agent's + ability to combine MC parameter scans with statistical recasting + spanning two orders of magnitude in mass and four orders in coupling. + The same $U_1$ Lagrangian as P09 (only $\beta_L^{32}$ non-zero), + but here the per-bin cross section is parameterized as + $\sigma_i(m, \beta) = b_i + \beta^2 I_i(m) + \beta^4 J_i(m)$, with + $I_i(m), J_i(m)$ extracted by solving a $2\times 2$ linear system from + two reference $\beta$ values. The diagnostic figure shows 95% CL + exclusion (dashed) and 5$\sigma$ discovery (solid) contours in + ($m_\mathrm{LQ}$, $\beta_L^{32}$) at $\sqrt{s}=3$ TeV (1 ab$^{-1}$, + red) and $\sqrt{s}=14$ TeV (20 ab$^{-1}$, purple). + + A credible study (a) implements the $U_1$ Lagrangian (FeynRules + UFO), + (b) runs SM baselines (100k events at 3 and 14 TeV) plus 4 LQ signal + scans ($\sqrt{s}\in\{3,14\}$ TeV x $\beta_L^{32}\in\{1.0, 2.0\}$) over + 17 mass points $m_\mathrm{LQ} \in \{1.0, 1.5, 2.0, 3.0, 4.0, 5.0, + 6.0, 7.0, 8.0, 10, 15, 20, 30, 40, 50, 60, 70\}$ TeV with 50k events + each, (c) bins each run in 10 equal-width $|\eta|$ bins, (d) extracts + $I_i, J_i$ per (mass, $\sqrt{s}$), (e) computes the binned + log-likelihood ratio for both the exclusion null hypothesis ($n_i + = b_i^\mathrm{events}$) and the discovery null ($n_i = \mu_i$), + (f) finds the $\beta$ where $-2\log\lambda$ crosses $\chi^2(10, + 0.95) = 18.307$ (exclusion) or 48.2 (5$\sigma$ discovery), and + (g) plots the four contours on log-log axes ($m_\mathrm{LQ}\in + [1, 75]$ TeV, $\beta\in[10^{-3}, 2]$). Research question: *does the + agent reproduce the muon-collider $U_1$ exclusion + discovery + contours from Figure 12 of arXiv:2104.05720?* + +hypotheses: + - id: H1 + statement: "At $\\sqrt{s}=14$ TeV with 20 ab$^{-1}$, the 95% CL exclusion contour reaches $\\beta_L^{32} \\leq 0.01$ at $m_\\mathrm{LQ} = 10$ TeV (matching the published value within a factor of 2)." + measurable: true + - id: H2 + statement: "The 14 TeV exclusion contour extends to higher $m_\\mathrm{LQ}$ than the 3 TeV exclusion contour at every fixed $\\beta_L^{32}$ in $[10^{-2}, 1]$ (higher energy + luminosity gives more reach)." + measurable: true + - id: H3 + statement: "5$\\sigma$ discovery contours always lie above the corresponding exclusion contours in $\\beta_L^{32}$ at every mass (discovery requires stronger signal than exclusion)." + measurable: true + +experiment_design: + research_question: "Does the agent reproduce the 95% CL exclusion and 5$\\sigma$ discovery contours for the $U_1$ leptoquark in the $(m_\\mathrm{LQ}, \\beta_L^{32})$ plane at 3 TeV (1 ab$^{-1}$) and 14 TeV (20 ab$^{-1}$) muon colliders (Figure 12 of arXiv:2104.05720)?" + conditions: + - name: "sm_baselines" + description: "Two SM-only runs (100k events each) at $\\sqrt{s}\\in\\{3, 14\\}$ TeV, $\\beta_L^{ij}=0$. Define per-bin SM cross section $b_i$ in 10 $|\\eta|$ bins." + - name: "lq_signal_3tev" + description: "Mass scan over 17 points $m_\\mathrm{LQ} \\in [1, 70]$ TeV at $\\sqrt{s}=3$ TeV, with $\\beta_L^{32}\\in\\{1.0, 2.0\\}$, 50k events each." + - name: "lq_signal_14tev" + description: "Same mass scan at $\\sqrt{s}=14$ TeV with $\\beta_L^{32}\\in\\{1.0, 2.0\\}$, 50k events each." + baselines: + - "SM $\\mu^+\\mu^- \\to \\gamma^*/Z^* \\to b\\bar b$ at $\\sqrt{s} = 3$ TeV and 14 TeV" + metrics: + - name: "exclusion_beta_at_10tev_14tev" + direction: "match_reference" + description: "$\\beta_L^{32}$ at the 95% CL exclusion contour at $m_\\mathrm{LQ}=10$ TeV, $\\sqrt{s}=14$ TeV." + - name: "discovery_beta_at_10tev_14tev" + direction: "match_reference" + description: "$\\beta_L^{32}$ at the 5$\\sigma$ discovery contour at $m_\\mathrm{LQ}=10$ TeV, $\\sqrt{s}=14$ TeV." + - name: "I_J_coefficients_per_mass" + direction: "match_reference" + description: "$(I_i, J_i)$ per $|\\eta|$ bin and mass point, extracted from the $\\beta\\in\\{1, 2\\}$ runs; positive-definite check on $J_i$." + datasets: + - process_id: "mumu_to_bbbar_U1_3and14tev" + sqrt_s_TeV: 14 + description: "$\\mu^+\\mu^- \\to b\\bar b$ via SM + $U_1$ exchange at muon colliders, two energy points." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 3600 + +rubric_path: "experiments/arc_bench/config/physics/rubrics/P10.json" + +# --------------------------------------------------------------------------- +# Agent-mode requirements (consumed by researchclaw.pipeline.requirements_judge +# at stage 15 RESEARCH_DECISION). Schema mirrors B01.yaml: +# id — stable identifier +# type — advisory hint to the LLM judge (numeric | artifact | discussion) +# description — natural-language statement of what must be true post-run +# must_pass — true → unmet ⇒ rerun (1 retry max); false → optional +# +# The five generic must_pass items apply uniformly across P01-P10; the sixth +# is topic-specific (mirrors this manifest's H1). The two must_pass=false +# items reward mechanistic interpretation and MC-reproducibility metadata +# without blocking proceed-vs-rerun on them. +# --------------------------------------------------------------------------- +requirements: + - id: req_results_json + type: artifact + description: >- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metric_key (string), metrics (object + with numeric keys), hypotheses (object with h1/h2/h3 entries each + carrying a `supported` boolean), summary (non-empty string). + must_pass: true + + - id: req_metrics_numeric + type: numeric + description: >- + results.json metrics MUST contain at least 3 numeric (non-null, finite) + values directly relevant to the headline physics observable named in + the experiment_design.metrics list above — these are the numbers the + paper will report in its Results section. + must_pass: true + + - id: req_hypotheses_supported_flags + type: discussion + description: >- + results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` + boolean AND a `details` string ≥ 40 characters quoting the numerical + evidence (specific values + their source artifact) used to reach the + verdict. + must_pass: true + + - id: req_publication_figure + type: artifact + description: >- + At least one publication-quality figure file (PDF or PNG, ≥150 DPI for + raster) exists under figures/ or output/figures/ with axes labeled in + physical units (GeV / pb / fb / dimensionless) and a legend if multiple + series are plotted. The figure must directly support a hypothesis + verdict. + must_pass: true + + - id: req_model_implementation + type: artifact + description: >- + The BSM Lagrangian is implemented either as a FeynRules .fr file + (models/*.fr) with a matching UFO directory (models/*_UFO/ containing + at least particles.py, parameters.py, couplings.py, vertices.py), OR + as analytic Python code that explicitly computes the cross sections + from the Lagrangian terms. A pure SM baseline with no BSM piece is + NOT sufficient. + must_pass: true + + - id: req_14tev_excl_reach + type: numeric + description: >- + results.json metrics MUST report the 95% CL exclusion β_L^32 reach at m_LQ=10 TeV, √s=14 TeV, 20 ab^-1; the value MUST be ≤ 0.02 (published ≤0.01 within a factor of 2). + must_pass: true + + - id: req_mechanistic_writeup + type: discussion + description: >- + The summary or structured_results section provides a one-paragraph + mechanistic interpretation of WHY the headline observable comes out the + way it does (which interference / propagator structure / cut effect + drives the result). Nice-to-have, not blocking proceed. + must_pass: false + + - id: req_mc_reproducibility + type: discussion + description: >- + results.json or a sibling reproducibility section names: (a) the + MadGraph5_aMC@NLO version, (b) the PDF set used (if applicable), (c) + at least one explicit random seed. Required for full reproducibility + but not for scientific correctness. + must_pass: false diff --git a/tasks/physics/rubrics/P01.json b/tasks/physics/rubrics/P01.json new file mode 100644 index 0000000000000000000000000000000000000000..9e6e10fef8e2a967ea2588acb842da81591b573f --- /dev/null +++ b/tasks/physics/rubrics/P01.json @@ -0,0 +1,166 @@ +{ + "id": "p01-root", + "requirements": "A credible HEP collider study reproducing the published reference figure for RS warped extra dimension KK-graviton resonance in $e^+e^- \\to \\mu^+\\mu^-$. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.", + "judging_note": "Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.", + "weight": 1, + "sub_tasks": [ + { + "id": "p01-code", + "requirements": "Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "p01-code-lagrangian", + "requirements": "BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Randall-Sundrum spin-2 KK graviton tower coupled to SM stress-energy tensor with $1/\\Lambda_\\pi$ (massive modes) and $1/\\bar M_{Pl}$ (zero mode); five massive modes $m_n \\in \\{600, 1098, 1592, 2086, 2580\\}$ GeV with $\\Lambda_\\pi = 522$ GeV.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p01-code-process", + "requirements": "MadGraph generate-process card or analytic process module matches the paper's process string: $e^+e^- \\to \\mu^+\\mu^-$ at energies $\\sqrt{s} \\in \\{200,\\ldots,1200\\}$ GeV with KK-graviton tower.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p01-code-pdfcuts", + "requirements": "PDF set and generation-level cuts match the paper exactly: Lepton-collider beams $E^\\pm = \\sqrt{s}/2$, no proton PDF; widths of all KK gravitons computed automatically (two-body decays only); 100 events per energy point.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "p01-code-pipeline", + "requirements": "Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p01-exec", + "requirements": "Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.", + "weight": 2, + "sub_tasks": [ + { + "id": "p01-exec-events", + "requirements": "MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p01-exec-physics", + "requirements": "Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p01-exec-statistics", + "requirements": "MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p01-results", + "requirements": "Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "p01-result-h1-quant", + "requirements": "Quantitative test of H1 \u2014 First KK resonance peaks within 5% of input $m_1=600$ GeV (i.e. peak in $[570, 630]$ GeV) \u2014 score 100% if rel. err. < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p01-result-h2-quant", + "requirements": "Quantitative test of H2 \u2014 Off-resonance cross sections at $\\sqrt{s} \\in \\{200, 400\\}$ GeV agree with the published Figure 2 within $\\pm 30\\%$. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p01-result-h3-quant", + "requirements": "Quantitative test of H3 \u2014 At least 2 of the 5 KK resonances within the [50,1500] GeV scan window appear as $\\geq 1$-decade enhancements above the SM Drell-Yan baseline. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p01-result-figure", + "requirements": "A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Reproduced figure shows $\\sigma$ vs $\\sqrt{s}$ with x-axis $\\sqrt{s}$ in GeV (linear, [50,1500]), y-axis $\\sigma$ in fb (log, $[400, 5\\times 10^6]$), blue line, all resonances visible.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p01-result-writeup", + "requirements": "Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p01-repro", + "requirements": "Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.", + "weight": 2, + "sub_tasks": [ + { + "id": "p01-repro-model", + "requirements": "UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p01-repro-runcards", + "requirements": "MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p01-repro-seeds", + "requirements": "RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/physics/rubrics/P02.json b/tasks/physics/rubrics/P02.json new file mode 100644 index 0000000000000000000000000000000000000000..556dc6cf072ebb9b2bdf8996e10bd8c76c924f5c --- /dev/null +++ b/tasks/physics/rubrics/P02.json @@ -0,0 +1,166 @@ +{ + "id": "p02-root", + "requirements": "A credible HEP collider study reproducing the published reference figure for Heavy Majorana neutrino $pp \\to \\mu^\\pm N$ at LHC 7/8/14 TeV. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.", + "judging_note": "Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.", + "weight": 1, + "sub_tasks": [ + { + "id": "p02-code", + "requirements": "Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "p02-code-lagrangian", + "requirements": "BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): BSM single right-handed Majorana neutrino $N$ mixing with the muon flavor: $\\mathcal{L}_N \\supset -(g_2/\\sqrt 2) V_{\\mu N} (\\bar\\mu \\gamma^\\mu P_L N) W^-_\\mu - (g_2/2 c_W) V_{\\mu N} (\\bar\\nu_\\mu \\gamma^\\mu P_L N) Z_\\mu + \\mathrm{h.c.}$ at $|V_{\\mu N}| = 1$.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p02-code-process", + "requirements": "MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\to \\mu^\\pm N$ via $W^*$ at the LHC, mass scan $m_N \\in \\{100,\\ldots,1000\\}$ GeV at three energies.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p02-code-pdfcuts", + "requirements": "PDF set and generation-level cuts match the paper exactly: CTEQ6L PDF for all $\\sqrt{s}$ values; 30 (mass, energy) cells (10 masses x 3 energies).", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "p02-code-pipeline", + "requirements": "Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p02-exec", + "requirements": "Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.", + "weight": 2, + "sub_tasks": [ + { + "id": "p02-exec-events", + "requirements": "MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p02-exec-physics", + "requirements": "Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p02-exec-statistics", + "requirements": "MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p02-results", + "requirements": "Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "p02-result-h1-quant", + "requirements": "Quantitative test of H1 \u2014 $\\sigma(pp \\to \\mu^\\pm N)$ at $m_N=200$ GeV, $\\sqrt{s}=14$ TeV agrees with reference within $\\pm 30\\%$.", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p02-result-h2-quant", + "requirements": "Quantitative test of H2 \u2014 Energy ordering $\\sigma(14) > \\sigma(8) > \\sigma(7)$ TeV holds at every mass; 14/7 ratio at $m_N=500$ GeV in $[5, 30]$. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p02-result-h3-quant", + "requirements": "Quantitative test of H3 \u2014 $\\sigma(m_N)$ falls $\\geq 2$ orders of magnitude from $m_N=100$ to $m_N=1000$ GeV at every $\\sqrt{s}$. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p02-result-figure", + "requirements": "A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Three-panel figure (one per $\\sqrt{s}$), 1:1 aspect, x-axis $m_N \\in [100,1000]$ GeV linear, y-axis $\\sigma \\in [0.1, 4\\times10^4]$ fb log.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p02-result-writeup", + "requirements": "Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p02-repro", + "requirements": "Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.", + "weight": 2, + "sub_tasks": [ + { + "id": "p02-repro-model", + "requirements": "UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p02-repro-runcards", + "requirements": "MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p02-repro-seeds", + "requirements": "RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/physics/rubrics/P03.json b/tasks/physics/rubrics/P03.json new file mode 100644 index 0000000000000000000000000000000000000000..452756f96a6557723bb4fade7245a16d0bc12f94 --- /dev/null +++ b/tasks/physics/rubrics/P03.json @@ -0,0 +1,166 @@ +{ + "id": "p03-root", + "requirements": "A credible HEP collider study reproducing the published reference figure for B-L $Z'$ kinetic mixing, three-panel exclusion in $(\\tilde g, g_1')$. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.", + "judging_note": "Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.", + "weight": 1, + "sub_tasks": [ + { + "id": "p03-code", + "requirements": "Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "p03-code-lagrangian", + "requirements": "BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Kinetic-mixing B-L $Z'$ with chiral couplings $C_{f,L/R}(\\tilde g, g_1', M_{Z'})$ encoding $g_1'$ (B-L gauge), $\\tilde g$ (kinetic mixing), and 3 heavy Majorana neutrinos $\\nu_h$ at $m_{\\nu_h}=95$ GeV.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p03-code-process", + "requirements": "MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\to Z'$ at $\\sqrt{s}=8$ TeV with kinetic mixing; Drell-Yan dilepton search recast.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p03-code-pdfcuts", + "requirements": "PDF set and generation-level cuts match the paper exactly: 9 signal runs (3 mass x 3 coupling points), 3 SM Drell-Yan background runs with generator cut $m_{\\ell\\ell} > 0.9 M_{Z'}$ at $\\sqrt{s}=8$ TeV; mass-dependent NNLO k-factors $\\{1.35, 1.40, 1.50\\}$ and $\\epsilon_\\mathrm{acc}=0.6$.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "p03-code-pipeline", + "requirements": "Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p03-exec", + "requirements": "Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.", + "weight": 2, + "sub_tasks": [ + { + "id": "p03-exec-events", + "requirements": "MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p03-exec-physics", + "requirements": "Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p03-exec-statistics", + "requirements": "MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p03-results", + "requirements": "Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "p03-result-h1-quant", + "requirements": "Quantitative test of H1 \u2014 $M_{Z'}=2$ TeV exclusion contour crosses the $\\tilde g=0$ axis at $g_1' \\in [0.10, 0.20]$ (within 30% of published).", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p03-result-h2-quant", + "requirements": "Quantitative test of H2 \u2014 Quadratic coefficients $(A,B,C)$ at $M_{Z'}=2$ TeV satisfy $A>0$ and $|B| \\le 4\\sqrt{AC}$ (positive-definite cross section everywhere in plane). Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p03-result-h3-quant", + "requirements": "Quantitative test of H3 \u2014 Maximum reach in $g_1'$ at $\\tilde g=0$ degrades by $\\geq 2\\times$ from $M_{Z'}=2$ TeV to $M_{Z'}=3$ TeV. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p03-result-figure", + "requirements": "A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Three side-by-side panels (one per $M_{Z'}$), 1:1 aspect, exclusion contour at $\\mathrm{Sig}\\equiv 2(\\sqrt{S+B}-\\sqrt B)=2$ in the $(\\tilde g, g_1')$ plane.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p03-result-writeup", + "requirements": "Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p03-repro", + "requirements": "Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.", + "weight": 2, + "sub_tasks": [ + { + "id": "p03-repro-model", + "requirements": "UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p03-repro-runcards", + "requirements": "MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p03-repro-seeds", + "requirements": "RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/physics/rubrics/P04.json b/tasks/physics/rubrics/P04.json new file mode 100644 index 0000000000000000000000000000000000000000..30d7655f6dcbd5c1d4a93556ebeee00b6c680821 --- /dev/null +++ b/tasks/physics/rubrics/P04.json @@ -0,0 +1,166 @@ +{ + "id": "p04-root", + "requirements": "A credible HEP collider study reproducing the published reference figure for B-L $Z'$ kinetic mixing $\\sigma(g_1')$ slices at $\\sqrt{s}=13$ TeV. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.", + "judging_note": "Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.", + "weight": 1, + "sub_tasks": [ + { + "id": "p04-code", + "requirements": "Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "p04-code-lagrangian", + "requirements": "BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Same kinetic-mixing B-L $Z'$ as P03; cross section $\\sigma = A g_1'^2 + B g_1'\\tilde g + C \\tilde g^2$ exact.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p04-code-process", + "requirements": "MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\to Z'$ at $\\sqrt{s}=13$ TeV; $\\sigma$ vs $g_1'$ at fixed $\\tilde g$ slices.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p04-code-pdfcuts", + "requirements": "PDF set and generation-level cuts match the paper exactly: 6 signal runs (3 per mass) at $\\sqrt{s}=13$ TeV; standard LHC PDF set.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "p04-code-pipeline", + "requirements": "Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p04-exec", + "requirements": "Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.", + "weight": 2, + "sub_tasks": [ + { + "id": "p04-exec-events", + "requirements": "MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p04-exec-physics", + "requirements": "Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p04-exec-statistics", + "requirements": "MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p04-results", + "requirements": "Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "p04-result-h1-quant", + "requirements": "Quantitative test of H1 \u2014 $\\sigma(M_{Z'}=2\\,\\mathrm{TeV}, \\tilde g=0, g_1'=0.10)$ within $\\pm 30\\%$ of published Panel A value.", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p04-result-h2-quant", + "requirements": "Quantitative test of H2 \u2014 $\\tilde g=-0.10$ curve in Panel A lies above $\\tilde g=0$ at $g_1'=0.05$ but below at $g_1'=0.20$ (interference sign change). Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p04-result-h3-quant", + "requirements": "Quantitative test of H3 \u2014 $\\sigma(M_{Z'}=2)/\\sigma(M_{Z'}=3)$ at $\\tilde g=0, g_1'=0.10$ is in $[10, 200]$ (PDF suppression). Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p04-result-figure", + "requirements": "A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Two-panel figure (Panel A: $M_{Z'}=2$ TeV, $g_1'\\in[0,0.20]$, $\\sigma \\in [10^{-4}, 10^{-1}]$ pb; Panel B: $M_{Z'}=3$ TeV, $g_1'\\in[0,0.70]$, $\\sigma \\in [10^{-4}, 10^1]$ pb), three colored curves per panel.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p04-result-writeup", + "requirements": "Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p04-repro", + "requirements": "Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.", + "weight": 2, + "sub_tasks": [ + { + "id": "p04-repro-model", + "requirements": "UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p04-repro-runcards", + "requirements": "MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p04-repro-seeds", + "requirements": "RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/physics/rubrics/P05.json b/tasks/physics/rubrics/P05.json new file mode 100644 index 0000000000000000000000000000000000000000..1f5547e4e9eb2ce26403ece69cd52f72b706af90 --- /dev/null +++ b/tasks/physics/rubrics/P05.json @@ -0,0 +1,166 @@ +{ + "id": "p05-root", + "requirements": "A credible HEP collider study reproducing the published reference figure for Photophobic ALP EFT $E_T^\\mathrm{miss}$ in $pp \\to a W^\\pm \\gamma$. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.", + "judging_note": "Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.", + "weight": 1, + "sub_tasks": [ + { + "id": "p05-code", + "requirements": "Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "p05-code-lagrangian", + "requirements": "BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Bosonic ALP EFT operators $\\mathcal{A}_{\\tilde W}, \\mathcal{A}_{\\tilde B}$ with photophobic relation $c_{\\tilde B} = -\\tan^2\\theta_W \\cdot c_{\\tilde W}$; $f_a = 1$ TeV, $m_a = 1$ MeV, $c_{\\tilde W} = 1$.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p05-code-process", + "requirements": "MadGraph generate-process card or analytic process module matches the paper's process string: Parton-level $pp \\to a W^\\pm (\\to \\ell^\\pm\\nu) \\gamma$ at $\\sqrt{s}=13$ TeV.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p05-code-pdfcuts", + "requirements": "PDF set and generation-level cuts match the paper exactly: nn23lo1 PDF; photon $p_T > 20$ GeV, $|\\eta_\\gamma| < 2.5$; lepton $p_T > 20$ GeV, $|\\eta_\\ell| < 2.5$; 500k events, parton level (no shower/detector).", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "p05-code-pipeline", + "requirements": "Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p05-exec", + "requirements": "Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.", + "weight": 2, + "sub_tasks": [ + { + "id": "p05-exec-events", + "requirements": "MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p05-exec-physics", + "requirements": "Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p05-exec-statistics", + "requirements": "MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p05-results", + "requirements": "Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "p05-result-h1-quant", + "requirements": "Quantitative test of H1 \u2014 Peak of normalized $E_T^\\mathrm{miss}$ in $[100, 250]$ GeV bin range.", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p05-result-h2-quant", + "requirements": "Quantitative test of H2 \u2014 Integrated normalized fraction with $E_T^\\mathrm{miss} > 500$ GeV in $[0.05, 0.30]$ (long high-MET tail from derivative coupling). Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p05-result-h3-quant", + "requirements": "Quantitative test of H3 \u2014 Acceptance after photon + lepton cuts $\\geq 50\\%$. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p05-result-figure", + "requirements": "A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Normalized $E_T^\\mathrm{miss}$ histogram, 50 bins over [0,1000] GeV, log-y $[10^{-4}, 1]$, 4:3 aspect.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p05-result-writeup", + "requirements": "Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p05-repro", + "requirements": "Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.", + "weight": 2, + "sub_tasks": [ + { + "id": "p05-repro-model", + "requirements": "UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p05-repro-runcards", + "requirements": "MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p05-repro-seeds", + "requirements": "RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/physics/rubrics/P06.json b/tasks/physics/rubrics/P06.json new file mode 100644 index 0000000000000000000000000000000000000000..1a63f2429fc5a20c49434ee61fa6e23dbce2dc29 --- /dev/null +++ b/tasks/physics/rubrics/P06.json @@ -0,0 +1,166 @@ +{ + "id": "p06-root", + "requirements": "A credible HEP collider study reproducing the published reference figure for $U_1$ vector-leptoquark recast of LHC $pp \\to \\tau\\nu$ (ATLAS+CMS). The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.", + "judging_note": "Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.", + "weight": 1, + "sub_tasks": [ + { + "id": "p06-code", + "requirements": "Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "p06-code-lagrangian", + "requirements": "BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): $U_1$ vector LQ in $(\\mathbf{3},\\mathbf{1},2/3)$ rep coupling to $(\\bar c\\gamma^\\mu P_L\\nu_\\tau)$ and $(\\bar b\\gamma^\\mu P_L\\tau)$ with couplings $g_c, g_b$.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p06-code-process", + "requirements": "MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\to \\tau\\nu$ via $bc \\to U_1 \\to \\tau\\nu$ exchange at $\\sqrt{s}=13$ TeV, mass scan.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p06-code-pdfcuts", + "requirements": "PDF set and generation-level cuts match the paper exactly: Standard 13 TeV proton PDF; 9 mass points $\\{750,\\ldots,5000\\}$ GeV with $b$ and $c$ parton initial states; 10k events per (mass, detector); Pythia8 shower; Delphes ATLAS card and Delphes CMS card runs in parallel; LHCO output.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "p06-code-pipeline", + "requirements": "Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p06-exec", + "requirements": "Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.", + "weight": 2, + "sub_tasks": [ + { + "id": "p06-exec-events", + "requirements": "MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p06-exec-physics", + "requirements": "Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p06-exec-statistics", + "requirements": "MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p06-results", + "requirements": "Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "p06-result-h1-quant", + "requirements": "Quantitative test of H1 \u2014 Combined ATLAS+CMS 2$\\sigma$ exclusion at $M_{U_1}=1$ TeV gives $\\sqrt{|g_c^* g_b|} \\in [0.3, 1.5]$.", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p06-result-h2-quant", + "requirements": "Quantitative test of H2 \u2014 Reconstructed exclusion contour intersects the LH $R_{D^{(*)}}$ band ($\\epsilon_L=0.11$) at excluded mass $M_{U_1}^* \\in [1.0, 2.5]$ TeV. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p06-result-h3-quant", + "requirements": "Quantitative test of H3 \u2014 Selection efficiency $\\geq 5\\%$ on signal at $M_{U_1}=1$ TeV in both ATLAS and CMS recasts. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p06-result-figure", + "requirements": "A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Exclusion contour in $(\\sqrt{|g_c^* g_b|}, M_{U_1})$ plane, 4:3 aspect, x in TeV $[0.8, 5.0]$, y $[0, 4.0]$, with LH band (blue dashed + light-blue band) and RH band (red dashed + light-red band).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p06-result-writeup", + "requirements": "Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p06-repro", + "requirements": "Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.", + "weight": 2, + "sub_tasks": [ + { + "id": "p06-repro-model", + "requirements": "UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p06-repro-runcards", + "requirements": "MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p06-repro-seeds", + "requirements": "RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/physics/rubrics/P07.json b/tasks/physics/rubrics/P07.json new file mode 100644 index 0000000000000000000000000000000000000000..3a7c214ce7c5f2f4e062093c17b8a6094d772c55 --- /dev/null +++ b/tasks/physics/rubrics/P07.json @@ -0,0 +1,166 @@ +{ + "id": "p07-root", + "requirements": "A credible HEP collider study reproducing the published reference figure for Scalar leptoquark resonance $m_{ej}$ via LUXlep proton lepton PDF. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.", + "judging_note": "Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.", + "weight": 1, + "sub_tasks": [ + { + "id": "p07-code", + "requirements": "Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "p07-code-lagrangian", + "requirements": "BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Scalar LQ in $SU(2)_L$ singlet, color triplet, $Q=-1/3$; $\\mathcal{L} = \\lambda_{eu}\\,\\mathrm{LQ}_{eu}\\,e_R^T i\\sigma^2 u_R + \\mathrm{h.c.}$; $M_\\mathrm{LQ}=3$ TeV, $\\Gamma=60$ GeV, $\\lambda_{eu}=1$.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p07-code-process", + "requirements": "MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\to \\mathrm{LQ} \\to ej$ resonant single LQ production at $\\sqrt{s}=13$ TeV.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p07-code-pdfcuts", + "requirements": "PDF set and generation-level cuts match the paper exactly: LUXlep PDF (proton content includes electrons and photon); generation cuts $p_T(\\ell, j) > 500$ GeV, $|\\eta|<2.5$; Pythia8 with lepton-to-photon LHE workaround (Check:event=off); Delphes ATLAS card with anti-$k_T$ R=0.4 jets; LHCO output.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "p07-code-pipeline", + "requirements": "Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p07-exec", + "requirements": "Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.", + "weight": 2, + "sub_tasks": [ + { + "id": "p07-exec-events", + "requirements": "MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p07-exec-physics", + "requirements": "Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p07-exec-statistics", + "requirements": "MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p07-results", + "requirements": "Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "p07-result-h1-quant", + "requirements": "Quantitative test of H1 \u2014 Reconstructed $m_{ej}$ peaks within $\\pm 5\\%$ of input $M_\\mathrm{LQ}=3000$ GeV (peak in $[2850, 3150]$ GeV).", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p07-result-h2-quant", + "requirements": "Quantitative test of H2 \u2014 Generation-level $p_T > 500$ GeV cut yields $\\geq 50\\%$ acceptance on the 3 TeV signal. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p07-result-h3-quant", + "requirements": "Quantitative test of H3 \u2014 Total weighted yield in $m_{ej} \\in [2500, 3500]$ GeV at $\\mathcal{L}=100$ fb$^{-1}$ within $\\pm 30\\%$ of published value. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p07-result-figure", + "requirements": "A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Reconstructed $m_{ej}$ histogram, 100 GeV bins from 0 to 5000 GeV, weighted by $\\sigma\\mathcal{L}/N_\\mathrm{gen}$, log-y $[10^{-3}, 5\\times 10^2]$ events/bin, 1:1 aspect, title 'LHC, $\\sqrt{s}=13$ TeV'.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p07-result-writeup", + "requirements": "Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p07-repro", + "requirements": "Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.", + "weight": 2, + "sub_tasks": [ + { + "id": "p07-repro-model", + "requirements": "UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p07-repro-runcards", + "requirements": "MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p07-repro-seeds", + "requirements": "RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/physics/rubrics/P08.json b/tasks/physics/rubrics/P08.json new file mode 100644 index 0000000000000000000000000000000000000000..14a64de1948afeb46ea694d5fdcbbc7146283024 --- /dev/null +++ b/tasks/physics/rubrics/P08.json @@ -0,0 +1,166 @@ +{ + "id": "p08-root", + "requirements": "A credible HEP collider study reproducing the published reference figure for General $Z'$: SSM vs $E_6$-$\\psi$ dimuon cross section vs mass at LHC. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.", + "judging_note": "Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.", + "weight": 1, + "sub_tasks": [ + { + "id": "p08-code", + "requirements": "Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "p08-code-lagrangian", + "requirements": "BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): General $Z'$ with 7 chiral couplings $g_{Lu/Ru/Ld/Rd/Le/Re/Lv}$; SSM tabulated values vs $E_6$-$\\psi$ universal $g_\\psi=0.0942$.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p08-code-process", + "requirements": "MadGraph generate-process card or analytic process module matches the paper's process string: $pp \\to Z' \\to \\mu^+\\mu^-$ at $\\sqrt{s}=13$ TeV, mass scan over 14 points $\\{200,\\ldots,5500\\}$ GeV.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p08-code-pdfcuts", + "requirements": "PDF set and generation-level cuts match the paper exactly: Standard 13 TeV proton PDF; widths auto-computed (two-body decays only); 14 mass points x 2 benchmarks = 28 runs.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "p08-code-pipeline", + "requirements": "Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p08-exec", + "requirements": "Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.", + "weight": 2, + "sub_tasks": [ + { + "id": "p08-exec-events", + "requirements": "MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p08-exec-physics", + "requirements": "Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p08-exec-statistics", + "requirements": "MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p08-results", + "requirements": "Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "p08-result-h1-quant", + "requirements": "Quantitative test of H1 \u2014 $\\sigma_\\mathrm{SSM}(2\\,\\mathrm{TeV}) / \\sigma_\\psi(2\\,\\mathrm{TeV})$ in $[3, 30]$.", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p08-result-h2-quant", + "requirements": "Quantitative test of H2 \u2014 $\\sigma_\\mathrm{SSM}(M_{Z'}=1\\,\\mathrm{TeV})$ within $\\pm 30\\%$ of published value. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p08-result-h3-quant", + "requirements": "Quantitative test of H3 \u2014 $\\sigma_\\mathrm{SSM}$ drops $\\geq 4$ orders of magnitude from $M=200$ GeV to $M=5500$ GeV. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p08-result-figure", + "requirements": "A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Two curves on log-y $\\sigma \\in [5\\times 10^{-6}, 5\\times 10^{-1}]$ pb vs $M_{Z'}\\in[200,5500]$ GeV linear: $Z'_\\mathrm{SSM}$ green dotted, $Z'_\\psi$ blue solid.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p08-result-writeup", + "requirements": "Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p08-repro", + "requirements": "Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.", + "weight": 2, + "sub_tasks": [ + { + "id": "p08-repro-model", + "requirements": "UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p08-repro-runcards", + "requirements": "MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p08-repro-seeds", + "requirements": "RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/physics/rubrics/P09.json b/tasks/physics/rubrics/P09.json new file mode 100644 index 0000000000000000000000000000000000000000..37f8badc3a9e3a25a6df06fe262001a6d095e9b9 --- /dev/null +++ b/tasks/physics/rubrics/P09.json @@ -0,0 +1,166 @@ +{ + "id": "p09-root", + "requirements": "A credible HEP collider study reproducing the published reference figure for $U_1$ vector-LQ angular shape in $\\mu^+\\mu^- \\to b\\bar b$ at 3 TeV muon collider. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.", + "judging_note": "Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.", + "weight": 1, + "sub_tasks": [ + { + "id": "p09-code", + "requirements": "Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "p09-code-lagrangian", + "requirements": "BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): $U_1$ vector LQ in $(\\mathbf{3},\\mathbf{1},2/3)$ rep with $\\kappa_U=\\tilde\\kappa_U=0$; only $\\beta_L^{32}$ (b-quark to muon) non-zero; $g_U=1$.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p09-code-process", + "requirements": "MadGraph generate-process card or analytic process module matches the paper's process string: $\\mu^+\\mu^- \\to b\\bar b$ at $\\sqrt{s}=3$ TeV via SM $s$-channel and $U_1$ $t$-channel exchange, parton level.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p09-code-pdfcuts", + "requirements": "PDF set and generation-level cuts match the paper exactly: Lepton-collider beams (no PDF); 1 SM run (500k events with $\\beta_L^{32}=0$, $m_\\mathrm{LQ}=10^5$ GeV) + 4 LQ signal runs ($\\beta_L^{32}\\in\\{1.0, 0.1\\}$ x $m_\\mathrm{LQ}\\in\\{1, 10\\}$ TeV, 50k events each).", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "p09-code-pipeline", + "requirements": "Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p09-exec", + "requirements": "Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.", + "weight": 2, + "sub_tasks": [ + { + "id": "p09-exec-events", + "requirements": "MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p09-exec-physics", + "requirements": "Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p09-exec-statistics", + "requirements": "MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p09-results", + "requirements": "Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "p09-result-h1-quant", + "requirements": "Quantitative test of H1 \u2014 At $\\beta_L^{32}=1.0$, $m_\\mathrm{LQ}=1$ TeV, central-bin $|\\eta|<0.1$ normalized fraction exceeds SM by $\\geq 50\\%$.", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p09-result-h2-quant", + "requirements": "Quantitative test of H2 \u2014 At $\\beta_L^{32}=0.1$, normalized $|\\eta|$ shape differs from SM by $\\leq 10\\%$ in every bin. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p09-result-h3-quant", + "requirements": "Quantitative test of H3 \u2014 At $\\beta_L^{32}=1.0$, $m_\\mathrm{LQ}=10$ TeV shape lies between SM and $m_\\mathrm{LQ}=1$ TeV (decoupling). Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p09-result-figure", + "requirements": "A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Two side-by-side panels (left: $\\beta_L^{32}=1.0$, right: $\\beta_L^{32}=0.1$); each panel has SM gray bar histogram + $m_\\mathrm{LQ}=1$ TeV blue dashed + $m_\\mathrm{LQ}=10$ TeV orange dashed; 13 bins from 0 to 2.5 (narrow [0,0.1] first); $14\\times 6$ figure size; y in [0, 0.20].", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p09-result-writeup", + "requirements": "Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p09-repro", + "requirements": "Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.", + "weight": 2, + "sub_tasks": [ + { + "id": "p09-repro-model", + "requirements": "UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p09-repro-runcards", + "requirements": "MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p09-repro-seeds", + "requirements": "RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/physics/rubrics/P10.json b/tasks/physics/rubrics/P10.json new file mode 100644 index 0000000000000000000000000000000000000000..8d0b8d9d823e3c1017ca1adea3821668322fe12c --- /dev/null +++ b/tasks/physics/rubrics/P10.json @@ -0,0 +1,166 @@ +{ + "id": "p10-root", + "requirements": "A credible HEP collider study reproducing the published reference figure for $U_1$ exclusion + 5$\\sigma$ discovery contours at 3 TeV (1 ab$^{-1}$) and 14 TeV (20 ab$^{-1}$) muon colliders. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generation cuts, (c) run the necessary detector / shower simulation if required, (d) reconstruct the diagnostic kinematic distribution, and (e) compare against the published reference figure with quantitative residuals tied to H1, H2, H3.", + "judging_note": "Physics topics are scored on whether the agent (i) assembles a working HEP MC pipeline AND (ii) reproduces the published numbers within stated tolerance. Partial credit is allowed; the agent may use analytic shortcuts in place of MadGraph for cases where it is overkill, as long as the answer matches the published reference within the per-leaf tolerance. Quantitative result leaves are scored on a graded scale: 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.", + "weight": 1, + "sub_tasks": [ + { + "id": "p10-code", + "requirements": "Code-development bucket: BSM model + process card + PDF/cuts + full pipeline are wired correctly.", + "weight": 2, + "sub_tasks": [ + { + "id": "p10-code-lagrangian", + "requirements": "BSM Lagrangian implemented (FeynRules .fr file + UFO output, or equivalent analytic Python module): Same $U_1$ Lagrangian as P09; per-bin cross section parameterized as $\\sigma_i(m, \\beta) = b_i + \\beta^2 I_i(m) + \\beta^4 J_i(m)$.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p10-code-process", + "requirements": "MadGraph generate-process card or analytic process module matches the paper's process string: $\\mu^+\\mu^- \\to b\\bar b$ at $\\sqrt{s}\\in\\{3, 14\\}$ TeV, parton-level.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "p10-code-pdfcuts", + "requirements": "PDF set and generation-level cuts match the paper exactly: Lepton-collider beams; 2 SM baselines (100k events each at 3 and 14 TeV) + 4 LQ signal scans ($\\sqrt{s}\\in\\{3,14\\}$ x $\\beta_L^{32}\\in\\{1.0, 2.0\\}$) over 17 mass points $m_\\mathrm{LQ}\\in\\{1.0,\\ldots,70\\}$ TeV with 50k events each.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "p10-code-pipeline", + "requirements": "Full HEP chain wired together (model -> events -> [shower -> detector] -> reconstruction -> analysis -> figure). For analytic shortcuts the chain may be simpler but must still terminate in the diagnostic figure.", + "weight": 4.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p10-exec", + "requirements": "Execution-validity bucket: MC sample passes nevents threshold, physics-validity gates clear, MC stats are sufficient.", + "weight": 2, + "sub_tasks": [ + { + "id": "p10-exec-events", + "requirements": "MC sample(s) generated successfully with at least the requested number of events per cell (or a documented partial count if compute-limited), with no fatal MadGraph or downstream errors.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p10-exec-physics", + "requirements": "Physics-validity gate: no negative cross sections; gauge invariance / unitarity sanity checks pass; mass spectrum and decay widths printed by MadGraph match the input parameter card; no obviously unphysical kinematics in the generated events.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "p10-exec-statistics", + "requirements": "MC statistics are sufficient: relative MC error in the signal region is < 10% OR at least 3 random seeds are run with std reported across them.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p10-results", + "requirements": "Results bucket: quantitative comparison of three hypothesis-driven observables to the published figure, plus a reproduced figure artifact and a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "p10-result-h1-quant", + "requirements": "Quantitative test of H1 \u2014 At $\\sqrt{s}=14$ TeV with 20 ab$^{-1}$, 95% CL exclusion reaches $\\beta_L^{32} \\leq 0.01$ at $m_\\mathrm{LQ}=10$ TeV (within factor 2 of published).", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p10-result-h2-quant", + "requirements": "Quantitative test of H2 \u2014 14 TeV exclusion contour extends to higher $m_\\mathrm{LQ}$ than 3 TeV contour at every fixed $\\beta_L^{32}\\in[10^{-2}, 1]$. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p10-result-h3-quant", + "requirements": "Quantitative test of H3 \u2014 5$\\sigma$ discovery contours always lie above (i.e. require larger $\\beta_L^{32}$ than) corresponding exclusion contours at every mass. Use the same graded relative-error scale as H1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p10-result-figure", + "requirements": "A reproduced figure exists in artifacts (PDF or PNG) with axes, units, ranges, and legend matching the published reference: Log-log plot, x-axis $m_\\mathrm{LQ}\\in[1,75]$ TeV log, y-axis $\\beta_L^{32}\\in[10^{-3}, 2]$ log, 1:1 aspect; 3 TeV (red) and 14 TeV (purple) contours; dashed = exclusion, solid = discovery (4 contours total).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "p10-result-writeup", + "requirements": "Writeup discusses the outcome of each hypothesis (supported / refuted / inconclusive) tied to numeric residuals, identifies the dominant systematic uncertainty (PDF set, scale variation, k-factor, Delphes card, statistical MC error), and states explicitly any analytic shortcuts taken in place of full MC.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "p10-repro", + "requirements": "Reproducibility bucket (new for physics topics; absent in T-rubrics): the artifacts contain everything needed to rerun the analysis from scratch.", + "weight": 2, + "sub_tasks": [ + { + "id": "p10-repro-model", + "requirements": "UFO model directory (or analytic model Python module) is checked into the artifacts and is loadable by MadGraph (or directly importable for analytic models).", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p10-repro-runcards", + "requirements": "MadGraph run_card / param_card, Pythia8 settings, Delphes card, and MadAnalysis5 (or equivalent analysis) cards are saved alongside the run with explicit parameter values per cell.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + }, + { + "id": "p10-repro-seeds", + "requirements": "RNG seeds are documented; rerunning the run from saved seeds should give matching central values within the reported MC error. For analytic shortcuts, the seed requirement is replaced by an explicit list of input numerical parameters.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Reproducibility" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/physics/topics.yaml b/tasks/physics/topics.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4cd96ea7c8599b919fe8d08bc6d7ce3b25b16b58 --- /dev/null +++ b/tasks/physics/topics.yaml @@ -0,0 +1,96 @@ +# ============================================================================ +# ARC-Bench Physics — 10 high-energy-physics paper-reproduction topics +# ---------------------------------------------------------------------------- +# P01-P10 adapt ColliderAgent paper-reproduction prompts into the +# ARC-Bench end-to-end measurement format used for T01-T25 (manifest + +# weighted rubric). Each topic is a self-contained Lagrangian -> MC -> +# analysis -> figure pipeline scoped to a published reference figure. +# +# Manifests live in config/physics/manifests/PNN.yaml +# Rubrics live in config/physics/rubrics/PNN.json +# +# All physics topics use `metric_key: primary_metric`, which is the +# ColliderAgentSandbox-emitted figures_produced / (figures_produced + +# steps_failed) pipeline-completion fraction. +# ============================================================================ + +topics: + - id: P01 + topic: "Reproducing warped extra dimension Kaluza-Klein graviton resonance from arXiv:hep-ph/9909255" + domains: ["high-energy-physics", "bsm-phenomenology", "extra-dimensions"] + metric_key: "primary_metric" + metric_direction: "maximize" + arxiv_id: "hep-ph/9909255" + source_prompt: "ColliderAgent/paper-reproduction/9909255/prompt_figure_2.md" + + - id: P02 + topic: "Reproducing heavy Majorana neutrino production $pp \\to \\mu^\\pm N$ at LHC 7/8/14 TeV from arXiv:1308.2209" + domains: ["high-energy-physics", "bsm-phenomenology", "neutrino-physics"] + metric_key: "primary_metric" + metric_direction: "maximize" + arxiv_id: "1308.2209" + source_prompt: "ColliderAgent/paper-reproduction/1308.2209/prompt_figure_3.md" + + - id: P03 + topic: "Reproducing 8 TeV LHC dilepton exclusion contours for B-L Z' with kinetic mixing from arXiv:1605.02910" + domains: ["high-energy-physics", "bsm-phenomenology", "z-prime", "statistical-recast"] + metric_key: "primary_metric" + metric_direction: "maximize" + arxiv_id: "1605.02910" + source_prompt: "ColliderAgent/paper-reproduction/1605.02910/prompt_figure_1.md" + + - id: P04 + topic: "Reproducing $\\sigma(pp \\to Z')$ vs $g_1'$ slices for kinetic-mixing B-L Z' at 13 TeV from arXiv:1605.02910" + domains: ["high-energy-physics", "bsm-phenomenology", "z-prime"] + metric_key: "primary_metric" + metric_direction: "maximize" + arxiv_id: "1605.02910" + source_prompt: "ColliderAgent/paper-reproduction/1605.02910/prompt_figure_10.md" + + - id: P05 + topic: "Reproducing photophobic ALP EFT $E_T^\\mathrm{miss}$ shape in $pp \\to a W^\\pm \\gamma$ from arXiv:1701.05379" + domains: ["high-energy-physics", "bsm-phenomenology", "axion-like-particles", "effective-field-theory"] + metric_key: "primary_metric" + metric_direction: "maximize" + arxiv_id: "1701.05379" + source_prompt: "ColliderAgent/paper-reproduction/1701.05379/prompt_figure_8.md" + + - id: P06 + topic: "Reproducing $U_1$ vector leptoquark exclusion from combined ATLAS+CMS $pp \\to \\tau\\nu$ recast from arXiv:1811.07920" + domains: ["high-energy-physics", "bsm-phenomenology", "leptoquark", "flavor-anomaly", "statistical-recast"] + metric_key: "primary_metric" + metric_direction: "maximize" + arxiv_id: "1811.07920" + source_prompt: "ColliderAgent/paper-reproduction/1811.07920/prompt_figure_3.md" + + - id: P07 + topic: "Reproducing scalar leptoquark $m_{ej}$ resonance via LUXlep proton lepton PDF from arXiv:2005.06475" + domains: ["high-energy-physics", "bsm-phenomenology", "leptoquark", "lepton-pdf"] + metric_key: "primary_metric" + metric_direction: "maximize" + arxiv_id: "2005.06475" + source_prompt: "ColliderAgent/paper-reproduction/2005.06475/prompt_figure_2.md" + + - id: P08 + topic: "Reproducing SSM and $E_6$-$\\psi$ Z' dimuon cross sections at 13 TeV LHC from arXiv:2103.02708" + domains: ["high-energy-physics", "bsm-phenomenology", "z-prime"] + metric_key: "primary_metric" + metric_direction: "maximize" + arxiv_id: "2103.02708" + source_prompt: "ColliderAgent/paper-reproduction/2103.02708/prompt_figure_4.md" + + - id: P09 + topic: "Reproducing $U_1$ leptoquark $|\\eta|$ shape in $\\mu^+\\mu^- \\to b\\bar b$ at 3 TeV muon collider from arXiv:2104.05720" + domains: ["high-energy-physics", "bsm-phenomenology", "leptoquark", "muon-collider"] + metric_key: "primary_metric" + metric_direction: "maximize" + arxiv_id: "2104.05720" + source_prompt: "ColliderAgent/paper-reproduction/2104.05720/prompt_figure_11.md" + + - id: P10 + topic: "Reproducing $U_1$ exclusion + 5$\\sigma$ discovery contours at 3 and 14 TeV muon colliders from arXiv:2104.05720" + domains: ["high-energy-physics", "bsm-phenomenology", "leptoquark", "muon-collider", "statistical-recast"] + metric_key: "primary_metric" + metric_direction: "maximize" + arxiv_id: "2104.05720" + source_prompt: "ColliderAgent/paper-reproduction/2104.05720/prompt_figure_12.md" diff --git a/tasks/quantum/README.md b/tasks/quantum/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8a2745b87da0a8ddf2285c907cdaf1845f3cdc7c --- /dev/null +++ b/tasks/quantum/README.md @@ -0,0 +1,128 @@ +# ARC-Bench Quantum Domain + +Ten open-ended quantum machine learning and variational quantum algorithm topics that run on the autoclaw native sandbox (Qiskit 2.x stack, statevector and finite-shot simulation, no GPU, no IBM Quantum hardware, no external agent). + +Added in branch `feat/quantum-domain`. Same `rc_full` runner that drives the ML domain (`run_bench.py`), same stages 10 to 14, same `paperbench_finalize` + `judge` post-processing. + +## Layout + +``` +config/quantum/ + topics.yaml registry of 10 topics (Q01-Q10) + manifests/Q0N.yaml per-topic manifest: synthesis, hypotheses, experiment_design, rubric_path + rubrics/Q0N.json per-topic 3-bucket rubric (code / exec / results) + README.md this file + +results/rc_full/Q0N// + bench_meta.json run config and bench inputs + judge_result.json per-leaf grades + overall_score + claims.json extracted quantitative claims + RESULTS_README.md auto-generated run summary + submission/ trimmed agent output (code + figures + writeup) + +log/rc_full/Q0N//full_run/ + full stage-07..14 archive. Gitignored. +``` + +## Topic registry + +| ID | Theme | Primary metric | Direction | +|-----|------------------------------------|---------------------------------|-----------| +| Q01 | Data encoding strategies for VQC | test_accuracy | maximize | +| Q02 | CNOT ablation in VQC | test_accuracy | maximize | +| Q03 | Classical optimizers for VQE on H2 | shots_to_chemical_accuracy | minimize | +| Q04 | Data re-uploading depth scaling | test_mse | minimize | +| Q05 | Barren plateau cost locality | log_gradient_variance | maximize | +| Q06 | NN warm-start for QAOA MaxCut | iterations_to_target_ratio | minimize | +| Q07 | MPS classifier vs NN baselines | test_accuracy | maximize | +| Q08 | Layerwise vs end-to-end VQC | test_accuracy | maximize | +| Q09 | Noise-aware VQC training | test_accuracy | maximize | +| Q10 | Quantum autoencoder fidelity | reconstruction_fidelity | maximize | + +Full topic strings live in `topics.yaml`. Per-topic hypotheses H1 / H2 / H3, conditions, baselines, and seed counts live in `manifests/Q0N.yaml`. + +## Latest single-run scores (rc_full, gpt-5.3-codex + gpt-4o judge) + +Methodology: one latest run per topic, no best-of-N cherry picking. + +| ID | Score | Run timestamp | +|-----|-------|-------------------------| +| Q01 | 0.709 | 20260517-192423 | +| Q02 | 0.430 | 20260517-192423 | +| Q03 | 0.757 | 20260517-212933 | +| Q04 | 0.571 | 20260517-212933 | +| Q05 | 0.576 | 20260517-212933 | +| Q06 | 0.317 | 20260517-212933 | +| Q07 | 0.513 | 20260517-212933 | +| Q08 | 0.421 | 20260517-212720 | +| Q09 | 0.145 | 20260517-192423 | +| Q10 | 0.421 | 20260517-192422 | +| **Mean** | **0.486** | | + +Stage-15 PROCEED gate requires at least 2 baselines plus the proposed method, so every quantum manifest declares two or more baselines (random / random_init_control / classical MLP / logistic-regression as appropriate). + +## How to run + +Single topic: +```bash +python experiments/arc_bench/scripts/run_bench.py \ + --mode rc_full \ + --topic Q03 \ + --runs 1 +``` + +All quantum topics: +```bash +python experiments/arc_bench/scripts/run_bench.py \ + --mode rc_full \ + --domain quantum \ + --runs 1 +``` + +The runner reads `base_config.yaml`, expands `Q0N` to the matching `manifests/Q0N.yaml` and `rubrics/Q0N.json`, registers `Q -> quantum` in `prepare_run.py`, and drops outputs under `results/rc_full/Q0N//`. + +## Skill + +Agents pick up Qiskit-specific patterns from a domain skill: + +``` +researchclaw/skills/builtin/domain/quantum-qiskit/SKILL.md +``` + +Triggered on stages 10 and 13 whenever the topic synthesis or experiment plan mentions Qiskit, VQE, VQC, QAOA, EfficientSU2, ZZFeatureMap, MPS, noise model, etc. The skill is intentionally generic. It contains no Q-topic narratives and no bench-specific signal that would leak rubric information to the agent. + +Key patterns it documents: +1. Imports for Qiskit 2.x (StatevectorEstimator, StatevectorSampler, BackendSamplerV2, AerSimulator). +2. Data-encoding feature maps (ZFeatureMap, ZZFeatureMap, StatePreparation). +3. Variational ansatze (EfficientSU2, n-local). +4. VQC training with `qiskit_machine_learning.VQC.fit`. +5. Manual VQE loop pattern. `qiskit_algorithms.VQE` is broken under Qiskit 2.x because `qiskit_nature.second_q.algorithms` imports the removed `BaseEstimator`. The skill shows the StatevectorEstimator + `optimizer.minimize()` replacement. +6. MPS-structured circuits via `AerSimulator(method='matrix_product_state', matrix_product_state_max_bond_dimension=chi)`. +7. Noise model wiring. `qiskit_machine_learning.Sampler` does not accept a `noise_model` kwarg, so noisy training routes through `BackendSamplerV2(backend=AerSimulator(noise_model=...))`. +8. Qiskit 2.x compatibility notes and a table of common errors with fixes. + +## Dependencies + +Pinned in `config/base_config.yaml` `allowed_imports`: + +``` +qiskit, qiskit_aer, qiskit_algorithms, qiskit_machine_learning, qiskit_nature, pyscf +numpy, scipy, matplotlib, sklearn, pandas, statsmodels, networkx, skimage +``` + +The agent runs inside a sandbox so it cannot install new packages mid-run. Anything outside this list fails the import gate at stage 10. + +## Caveats + +- **LLM nondeterminism.** Single-run scores carry std around 0.2 to 0.4. Compare topics with caution. The committed runs are one snapshot. A rerun on the same manifest can swing materially. +- **Q09 is hard.** Noise-aware training under our sandbox time budget regularly bottoms out near the random-prediction floor. The 0.145 score reflects this. The skill already documents the correct noise model wiring, so the failure mode is not a missing pattern but a genuinely thin training signal under the configured noise rates. +- **Multi-file consistency.** Topics such as Q08 require the agent to keep `main.py`, `model.py`, `evaluate.py`, etc. in sync. When the agent gets out of sync, the run fails at stage 12 with a missing-import error. This is an agent capability ceiling and is not fixable by skill updates. +- **Code bucket weight.** Rubrics carry a Code Development bucket (weight 2 of 7), but the autoclaw default `judge.py` operates in `results_only` mode and skips code-leaf grading. Code leaves are kept in the rubric so that a future code-aware judge can score them without re-authoring. + +## Adding a new quantum topic + +1. Add an entry to `topics.yaml` with a `Q11+` id, a verbose topic string, domains, metric_key, metric_direction. +2. Write `manifests/Q11.yaml` with synthesis (research question + skill pointer + protocol), hypotheses (H1 / H2 / H3), experiment_design (conditions, at least 2 baselines, metrics, datasets, compute_requirements), and a `rubric_path` pointing to `rubrics/Q11.json`. +3. Write `rubrics/Q11.json` as a 3-bucket tree (code / exec / results), 25 / 25 / 50 weights, with 9 leaves total. +4. Make sure `scripts/prepare_run.py` `_PREFIX_MAP` and `scripts/judge.py` `_PREFIX_MAP` both map `Q -> quantum`. They already do for Q01-Q10. +5. Run end-to-end with `--runs 1` first to shake out manifest typos before committing. diff --git a/tasks/quantum/manifests/Q01.yaml b/tasks/quantum/manifests/Q01.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d4c4dd8c95f1ddef0fe424c081acc19a4e27b185 --- /dev/null +++ b/tasks/quantum/manifests/Q01.yaml @@ -0,0 +1,104 @@ +# ============================================================================ +# Q01 — Quantum data encoding comparison (minimal pilot version) +# ---------------------------------------------------------------------------- +# Open research question. NOT a paper reproduction. The agent must implement +# and compare three encoding strategies (angle, amplitude, ZZ feature map) +# under fixed ansatz / optimizer / simulator settings on ONE 6D binary +# classification dataset whose classical baseline does NOT saturate accuracy. +# Scope is intentionally trimmed from the original Q01 design so the pipeline +# can produce non-trivial quantum results within the autoclaw time budget. +# ============================================================================ + +id: Q01 +title: "Comparing quantum data encoding strategies for variational classifiers" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +synthesis: | + Quantum data encoding is widely regarded as the bottleneck of variational + quantum machine learning. The way classical features are loaded into a + quantum state determines what functions the subsequent variational ansatz + can express. Three encoding families are compared here: angle encoding + (single-qubit rotations only, no input entanglement), amplitude encoding + (information-dense state preparation that loads the classical vector as + state amplitudes), and the ZZ feature map (Hadamards plus diagonal + pairwise phase rotations, injecting entanglement at the input layer). + + The research question is direct. Holding the variational ansatz, the + classical optimizer, the simulator backend, and the dataset preprocessing + fixed, which encoding produces the best test accuracy on a moderately + difficult 6D binary classification task, and does at least one quantum + encoding approach the better of two classical baselines (logistic + regression and a small MLP)? + + Implementation guidance is provided by the `quantum-qiskit` skill, which + is automatically injected into the stage-10 code generation prompt for + this topic. Use the skill's reference patterns verbatim: import + `ZFeatureMap`, `StatePreparation`, and `ZZFeatureMap` from + `qiskit.circuit.library`; use `qiskit_machine_learning.algorithms.VQC` + with the reference Sampler primitive for training; never roll a custom + optimization loop. Hand-written gate sequences in place of these + library classes have been observed to produce degenerate ablations + (three encodings collapsing to nearly identical computations) and will + fail the rubric. + + Experimental protocol. All three quantum encodings share EfficientSU2 + reps=1 linear entanglement as the variational ansatz, COBYLA maxiter=200 + as the optimizer, and the noiseless statevector backend as the simulator. + Two classical baselines (LogisticRegression and MLPClassifier) run on + the same 6D features. Five random seeds per cell across 5 conditions + yields 25 total cells. Each per-seed result is logged to stdout as a + single line with the prefix `METRIC_RESULT` followed by a JSON object + containing `condition`, `dataset`, `seed`, and `test_accuracy` keys so + the autoclaw sandbox parser can collect structured metrics. + + Research question: *On a moderately difficult 6D binary classification + task (LogisticRegression baseline expected around 0.65 to 0.80), does + the choice of quantum encoding (angle, amplitude, ZZ) measurably change + variational classifier accuracy, and does at least one quantum encoding + approach the better classical baseline?* + +hypotheses: + - id: H1 + statement: "The best of the three quantum encodings (5-seed mean) achieves test_accuracy within 5 absolute percentage points of the BETTER of the two classical baselines (logistic_regression and mlp_classifier, 5-seed means) on synthetic_classification_6d." + measurable: true + - id: H2 + statement: "The spread of test_accuracy across the three quantum encodings (max minus min of the 5-seed means) is at least 5 absolute percentage points, demonstrating that encoding choice produces a measurable effect rather than a wash." + measurable: true + - id: H3 + statement: "zz_feature_map test_accuracy (5-seed mean) is at least as high as angle_encoding test_accuracy (within 2 absolute percentage points or higher), consistent with the conjecture that entanglement-rich input encoding does not hurt and may help on this task." + measurable: true + +experiment_design: + research_question: "On a moderately difficult 6D binary classification task, does the choice of quantum data encoding (angle, amplitude, ZZ feature map) measurably change variational classifier accuracy, and does at least one encoding approach the classical baseline?" + conditions: + - name: "angle_encoding" + description: "qiskit.circuit.library.ZFeatureMap(feature_dimension=6, reps=1) as the data encoding circuit. Each feature x_i is loaded as H followed by RZ(x_i) on qubit i. No entangling gates in the feature map." + - name: "amplitude_encoding" + description: "qiskit.circuit.library.StatePreparation on the L2-normalized, zero-padded input vector (length 64 = 2**6) as the data encoding circuit. Implementation: x_normalized = x / ||x||; x_padded = np.concatenate([x_normalized, np.zeros(64 - 6)]); StatePreparation(x_padded). The padded state is unit norm by construction." + - name: "zz_feature_map" + description: "qiskit.circuit.library.ZZFeatureMap(feature_dimension=6, reps=2, entanglement='linear') as the data encoding circuit. Two repetitions of H + RZ(x_i) on each qubit + RZZ(x_i * x_j) on linear nearest-neighbor pairs." + baselines: + - "logistic_regression: sklearn.linear_model.LogisticRegression(max_iter=1000) on the same 6D features." + - "mlp_classifier: sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(16,), activation='relu', solver='adam', max_iter=500, random_state=seed) on the same 6D features." + - "Both baselines reported per seed; both contribute to the H1 comparison (the BETTER of the two is the reference). Neither contributes to the primary quantum_test_accuracy_mean metric." + metrics: + - name: "quantum_test_accuracy_mean" + direction: "maximize" + description: "Primary metric. Mean of the 5-seed test_accuracy means across the THREE quantum encodings ONLY. The classical baseline is reported separately and does NOT contribute to this metric. This isolates the refinement signal from the baseline." + - name: "test_accuracy" + direction: "maximize" + description: "Per-condition mean accuracy on the 20% held-out test split, averaged over 5 random seeds. Reported per condition (angle, amplitude, zz_feature_map, logistic_regression)." + - name: "best_baseline_accuracy" + direction: "maximize" + description: "max(logistic_regression_5seed_mean, mlp_classifier_5seed_mean). Used as the reference point in H1 (the stronger of the two classical baselines)." + datasets: + - name: "synthetic_classification_6d" + source: "sklearn.datasets.make_classification" + description: "make_classification(n_samples=400, n_features=6, n_informative=4, n_redundant=1, n_repeated=0, n_clusters_per_class=2, class_sep=0.4, flip_y=0.05, random_state=seed). class_sep is set to 0.4 (not 1.0) so the LogisticRegression baseline lands around 0.65-0.80 instead of saturating near 1.0, leaving headroom for the quantum encodings to differ. Apply StandardScaler fit on the train split only. 80/20 train/test split." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 1800 + +rubric_path: "experiments/arc_bench/config/quantum/rubrics/Q01.json" diff --git a/tasks/quantum/manifests/Q02.yaml b/tasks/quantum/manifests/Q02.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2a1ec67eae063b8f1927fd11b0634f330e0fe974 --- /dev/null +++ b/tasks/quantum/manifests/Q02.yaml @@ -0,0 +1,111 @@ +# ============================================================================ +# Q02 — Entangling-gate ablation in a variational quantum classifier +# ---------------------------------------------------------------------------- +# Open research question. Holding encoding (angle), ansatz template +# (EfficientSU2 reps=2), optimizer (COBYLA), and datasets fixed, ablate CNOTs +# from the ansatz (full / half / none) and compare against a parameter-matched +# classical MLP baseline. Same datasets as Q01 so results can be cross-read. +# ============================================================================ + +id: Q02 +title: "Entangling-gate ablation in a variational quantum classifier" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +synthesis: | + Quantum machine learning models routinely use parameterized circuits + that mix single-qubit rotations with entangling two-qubit gates, + typically CNOTs. The common narrative is that entanglement is the + source of quantum advantage. If a variational classifier with no + entangling gates whatsoever can match the accuracy of one with full + entanglement, that narrative breaks down for this task class. The + purpose of this study is to measure the actual contribution of + entanglement in a simple variational quantum classifier by cleanly + removing CNOTs in a controlled monotonic manner. + + The design isolates entanglement to one source. The data encoding is + fixed to angle encoding (single-qubit rotations only, no entangling + gates). The variational ansatz template is EfficientSU2 with reps=3, + which by default places a linear chain of CNOTs between each rotation + block (5 CNOTs per entangling layer, 3 layers, 15 CNOTs total for + 6 qubits). We ablate by replacing some or all of those CNOTs with + identity, while keeping every single-qubit rotation parameter intact. + This isolates the gain attributable to two-qubit entangling structure + from the gain attributable to the trainable single-qubit basis change. + half_entanglement uses an independent random Bernoulli(0.5) mask over + the 15 CNOT positions per cell, seeded deterministically so the + ablation is reproducible but not aligned with any fixed pattern. + + A parameter-matched classical baseline (sklearn MLPClassifier with a + hidden layer sized so its total parameter count is within 10 percent + of the VQC parameter count) tells us whether either quantum condition + is competitive with a trivial classical alternative on the same data. + If the no-entanglement VQC underperforms the classical MLP, the + conclusion is that on these tasks, entangling layers are not just + helpful but necessary for the variational quantum model to be a + viable choice at all. + + Datasets are deliberately shared with Q01 (encoding comparison) so + results can be read across both studies. If the cross-topic story + holds, the encoding family that benefits most from ansatz CNOTs in + Q02 should also be the family that performed worst in Q01 (because + the ansatz CNOTs are compensating for the encoding's lack of input + entanglement). The current manifest does not require that + cross-comparison, but the writeup is encouraged to discuss it. + + Research question: *In a variational quantum classifier on low + dimensional binary classification, how much of the model's accuracy + is actually due to ansatz entangling gates, and does the accuracy + degrade monotonically as CNOTs are removed?* + +hypotheses: + - id: H1 + statement: "full_entanglement achieves mean test_accuracy (averaged over 5 seeds) at least 5 absolute percentage points higher than no_entanglement on at least 2 of 3 datasets." + measurable: true + - id: H2 + statement: "half_entanglement mean test_accuracy lies strictly between full_entanglement and no_entanglement (within seed-mean noise of 1 absolute percentage point) on at least 2 of 3 datasets, demonstrating that the contribution of entangling layers is approximately monotonic in CNOT count." + measurable: true + - id: H3 + statement: "On at least 1 of 3 datasets, classical_baseline (parameter-matched MLPClassifier) achieves test_accuracy at least as high as no_entanglement (5-seed mean). This sanity check rules out a trivial-task explanation: if removing all entanglement leaves the VQC weaker than a tiny classical net, then 'no entanglement' is not a viable QML setting on these tasks." + measurable: true + +experiment_design: + research_question: "In a variational quantum classifier on low dimensional binary classification, how much of the model's accuracy is actually due to ansatz entangling gates, and does the accuracy degrade monotonically as CNOTs are removed?" + conditions: + - name: "full_entanglement" + description: "Feature map: ZFeatureMap(feature_dimension=6, reps=1). Ansatz: EfficientSU2(num_qubits=6, reps=3, entanglement='linear'). All 15 CNOTs from the 3 entangling layers are kept. This is the baseline VQC." + - name: "half_entanglement" + description: "Same feature_map and ansatz template as full_entanglement, but each of the 15 CNOTs is independently dropped according to a random Bernoulli(0.5) mask. The mask is generated per cell using a derived random state: np.random.RandomState(seed + 10000).binomial(1, 0.5, size=15), where seed is the per-cell run seed. Dropped CNOTs are replaced with identity. Each cell records its realized cnot_count (expected ~7.5, variance per draw). Trainable parameters unchanged." + - name: "no_entanglement" + description: "Same feature_map and rotation blocks as full_entanglement, but EVERY entangling gate is replaced with identity. The circuit reduces to a tensor product of independent single-qubit gates. CNOT count is 0. Trainable parameters unchanged." + - name: "classical_baseline" + description: "sklearn.neural_network.MLPClassifier with hidden_layer_sizes chosen so the total trainable parameter count is within 10 percent of the VQC (EfficientSU2 reps=3 on 6 qubits has 48 rotation parameters; an MLP with one hidden layer of size 6 has 6*6 + 6 + 6*1 + 1 = 49 params). Use hidden_layer_sizes=(6,), activation='relu', max_iter=500, solver='lbfgs', random_state matched to the corresponding seed." + baselines: + - "no_entanglement is the within-quantum ablation baseline" + - "classical_baseline is the cross-paradigm sanity baseline" + metrics: + - name: "test_accuracy" + direction: "maximize" + description: "Mean over 5 seeds of held-out 20% test split accuracy." + - name: "convergence_iterations" + direction: "minimize" + description: "Number of COBYLA iterations (or MLP solver iterations for classical_baseline) until training loss change drops below 1e-4 across 5 consecutive evaluations." + - name: "cnot_count" + direction: "minimize" + description: "Number of CNOT gates remaining in the variational circuit (feature map + ansatz) after ablation. Informational metric. Expected: full=15, half=mean over draws (~7.5), no=0, classical_baseline=0." + datasets: + - name: "synthetic_classification_6d" + source: "sklearn.datasets.make_classification" + description: "make_classification(n_samples=200, n_features=6, n_informative=4, n_redundant=1, n_repeated=0, n_clusters_per_class=2, class_sep=1.0, flip_y=0.05, random_state=seed). Binary classification, native 6D. Apply StandardScaler. 80/20 train/test split. Same configuration as Q01." + - name: "wine_binary_pca6" + source: "sklearn.datasets.load_wine + sklearn.decomposition.PCA" + description: "load_wine (178 samples, 13 features, 3 classes). Select classes 0 and 1 only (binary subset, ~130 samples). Apply StandardScaler then PCA(n_components=6). 80/20 train/test split. Same configuration as Q01." + - name: "breast_cancer_pca6" + source: "sklearn.datasets.load_breast_cancer + sklearn.decomposition.PCA" + description: "load_breast_cancer (569 samples, 30 features). Apply StandardScaler then PCA(n_components=6). 80/20 train/test split. Same configuration as Q01." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 1200 + +rubric_path: "experiments/arc_bench/config/quantum/rubrics/Q02.json" diff --git a/tasks/quantum/manifests/Q03.yaml b/tasks/quantum/manifests/Q03.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6b3dd4c1f2d24874fc50b28c4bc4475010e659d3 --- /dev/null +++ b/tasks/quantum/manifests/Q03.yaml @@ -0,0 +1,142 @@ +# ============================================================================ +# Q03 — Classical optimizer comparison for VQE under finite shot noise +# ---------------------------------------------------------------------------- +# Open research question. The agent holds ansatz / Hamiltonian / initial +# parameters / simulator fixed and varies only the classical optimizer +# (SPSA, COBYLA, L-BFGS-B, ADAM) and the shot budget per energy evaluation +# (1024, 4096, 16384) on two H2 geometries (equilibrium and stretched). +# Diagnostic: cumulative shots to reach chemical accuracy (1.6 mHa of FCI). +# ============================================================================ + +id: Q03 +title: "Classical optimizer comparison for VQE on H2 under finite shot noise" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +synthesis: | + The Variational Quantum Eigensolver (VQE) is the canonical algorithm + for near-term quantum chemistry. The quality of a VQE result depends + on the ansatz, but it also critically depends on the classical + optimizer that updates variational parameters using noisy energy + estimates from shot-based measurement. Different optimizers exploit + gradient information very differently. Gradient-free methods such as + COBYLA and Nelder-Mead do not need explicit gradients and may be + robust to shot noise but converge slowly in high dimensions. + Simultaneous Perturbation Stochastic Approximation (SPSA) approximates + the gradient using just two function evaluations regardless of + parameter count, trading gradient quality for evaluation count and + often dominating in low-shot regimes. Gradient-based methods such as + L-BFGS-B and ADAM need explicit or finite-difference gradients, which + are expensive under shot noise because each partial derivative is + estimated from a noisy function value. + + The choice of optimizer is rarely studied head-to-head under a fixed + shot budget. Most VQE papers fix the optimizer and tune everything + else around it. Here we hold ansatz (hardware-efficient EfficientSU2 + reps=2), Hamiltonian (H2 in STO-3G basis), and initial parameters + (small random Gaussian) fixed, and vary only the optimizer and the + shot budget per energy evaluation. The diagnostic question is which + optimizer reaches chemical accuracy (within 1.6 mHa of the FCI + reference) using the fewest cumulative shots. + + A credible study uses at least two molecular geometries to avoid + overfitting to a single energy landscape. We use H2 at two bond + lengths: the equilibrium distance (0.74 angstrom) where the ground + state is near Hartree-Fock and the optimization landscape is well + behaved, and a stretched geometry (1.5 angstrom) where multireference + character makes the landscape harder and tests optimizer robustness. + Each optimizer is run at a SINGLE fixed shot budget of 1024 shots per + energy evaluation. This is chosen as a representative middle-ground + value: low enough that shot noise is non-trivial (per-eval std ~15 mHa, + well above the 1.6 mHa chemical accuracy threshold, so the running-mean + convergence criterion matters), but high enough that some optimizers + can plausibly converge within their iteration budgets. All cells use + 3 random seeds per (optimizer, geometry) cell, giving 4 optimizers x + 1 shot budget x 2 geometries x 3 seeds = 24 total cells. Cumulative + shots is the sum of shots used across all energy evaluations + (including finite-difference gradient evaluations where applicable). + The Hamiltonian itself is constructed via + qiskit_nature.second_q.drivers.PySCFDriver (qiskit_nature and pyscf + are required dependencies for this topic; hardcoded Pauli string + fallbacks are NOT accepted). + + Implementation contract (see quantum-qiskit skill for the reference + code). In qiskit 2.x, `qiskit_algorithms.VQE` and + `qiskit_nature.second_q.algorithms` are NOT importable (they depend on + the removed `qiskit.primitives.BaseEstimator` V1 interface). VQE MUST + be implemented as a manual optimization loop: + energy(theta) = estimator.run([(bound_ansatz, qubit_op)]).result()[0].data.evs + e_nuclear, + using `qiskit.primitives.StatevectorEstimator` (the V2 primitive that + works in qiskit 2.x) for the energy evaluation, and one of the four + optimizer classes from `qiskit_algorithms.optimizers` (SPSA, COBYLA, + L_BFGS_B, ADAM) for the parameter update, called via + `optimizer.minimize(energy, initial_point)`. Each call to `energy` + counts as one evaluation; cumulative shots is + `n_evaluations * shots_per_eval`. The Hamiltonian itself is constructed + via qiskit_nature.second_q.drivers.PySCFDriver + + qiskit_nature.second_q.mappers.ParityMapper (these submodules ARE safe + to import in qiskit 2.x — only the qiskit_nature.second_q.algorithms + module is broken). With ParityMapper and 2-qubit reduction + (num_particles=(1,1)), the H2/STO-3G Hamiltonian is 2 qubits, not 4. + Implement shot noise by adding Gaussian noise to each energy value + with sigma = ||H||_1 / sqrt(shots_per_eval). The convergence criterion + for shots_to_chemical_accuracy is the cumulative shots when a 5-eval + running mean energy stays within 1.6 mHa of E_FCI for 5 consecutive + evaluations; if not reached, report None or a sentinel distinct from + the upper bound so analysis can flag the cell as "did not converge" + rather than treating the budget cap as a measurement. + + Research question: *Under finite shot noise, which classical optimizer + (SPSA, COBYLA, L-BFGS-B, ADAM) reaches VQE chemical accuracy using the + fewest cumulative shots, and how does the answer depend on shot budget + per evaluation and on Hamiltonian difficulty (equilibrium vs stretched + H2)?* + +hypotheses: + - id: H1 + statement: "At 1024 shots per energy evaluation, SPSA reaches chemical accuracy (|E_running_mean - E_FCI| < 1.6 mHa for 5 consecutive 5-eval windows) using fewer cumulative shots than L-BFGS-B (3-seed median), on at least 1 of 2 H2 geometries." + measurable: true + - id: H2 + statement: "At 1024 shots per energy evaluation, gradient-free methods (SPSA and COBYLA, 6 seed-runs combined across both geometries) achieve chemical accuracy with success rate at least 4/6, while gradient-based methods (L-BFGS-B and ADAM, 6 seed-runs combined) achieve it with success rate at most 3/6. This isolates the shot-noise sensitivity of finite-difference gradient estimation." + measurable: true + - id: H3 + statement: "Optimizer ranking by shots_to_chemical_accuracy changes between H2 equilibrium and H2 stretched geometries: at least one optimizer pair (e.g. SPSA vs COBYLA, or SPSA vs L-BFGS-B) swaps relative ranking between the two geometries, indicating that Hamiltonian difficulty changes optimizer choice." + measurable: true + +experiment_design: + research_question: "Under finite shot noise, which classical optimizer reaches VQE chemical accuracy in the fewest cumulative shots, and how does the answer depend on shot budget per evaluation and on Hamiltonian difficulty?" + conditions: + - name: "spsa" + description: "qiskit_algorithms.optimizers.SPSA(maxiter=200, learning_rate=0.05, perturbation=0.1). Fixed shot budget: 1024 shots per energy evaluation. Each iteration costs 2 energy evaluations." + - name: "cobyla" + description: "qiskit_algorithms.optimizers.COBYLA(maxiter=200, rhobeg=0.1, tol=1e-4). 1024 shots per energy evaluation. Each iteration costs 1 energy evaluation." + - name: "lbfgsb" + description: "qiskit_algorithms.optimizers.L_BFGS_B(maxiter=100, ftol=1e-6) with finite-difference gradients (epsilon=1e-3). 1024 shots per energy evaluation. Each iteration costs ~2*num_params energy evaluations for the gradient plus 1 for the function value (so ~25 evals per iter for EfficientSU2 reps=2 on 4 qubits)." + - name: "adam" + description: "qiskit_algorithms.optimizers.ADAM(maxiter=200, lr=0.05, beta_1=0.9, beta_2=0.999) with finite-difference gradients (epsilon=1e-3). 1024 shots per energy evaluation. Same per-iter cost as L-BFGS-B." + baselines: + - "Cumulative shots vs. FCI energy reference is the within-method benchmark" + - "Hartree-Fock energy at each geometry is the trivial classical baseline below which any optimizer should clear" + metrics: + - name: "shots_to_chemical_accuracy" + direction: "minimize" + description: "Cumulative shots until |E_running_mean - E_FCI| stays below 1.6 mHa for 5 consecutive evaluations. If not reached within the maxiter budget, report the total cumulative shots used (sentinel = max-shots) and flag success=False." + - name: "final_energy_error_hartree" + direction: "minimize" + description: "|E_final - E_FCI| in Hartree at the end of optimization (after all maxiter iterations)." + - name: "success_rate" + direction: "maximize" + description: "Fraction of seeds (out of 3 per condition x geometry) where the run reached chemical accuracy by the maxiter budget." + datasets: + - name: "h2_equilibrium" + source: "qiskit_nature.second_q.drivers.PySCFDriver (REQUIRED dependency)" + description: "H2 in STO-3G basis at bond length 0.74 angstrom. Build the FermionicOp via PySCFDriver, apply ParityMapper with 2-qubit reduction to obtain a 4-qubit qubit Hamiltonian (alternatively JordanWignerMapper + tapering). FCI reference: E_FCI = -1.137283 Ha. Hardcoded Pauli strings are NOT an acceptable substitute for this benchmark; both qiskit_nature and pyscf must be installed and used." + - name: "h2_stretched" + source: "qiskit_nature.second_q.drivers.PySCFDriver (REQUIRED dependency)" + description: "H2 in STO-3G basis at bond length 1.5 angstrom (multireference / strong-correlation regime). 4 qubits via the same mapping pipeline as h2_equilibrium. FCI reference: E_FCI ~ -1.001 Ha. Hardcoded Pauli strings NOT accepted." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 1200 + +rubric_path: "experiments/arc_bench/config/quantum/rubrics/Q03.json" diff --git a/tasks/quantum/manifests/Q04.yaml b/tasks/quantum/manifests/Q04.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e48925da8fd0ca7836922b0699ba6ac7883fcea1 --- /dev/null +++ b/tasks/quantum/manifests/Q04.yaml @@ -0,0 +1,112 @@ +# ============================================================================ +# Q04 — Data re-uploading depth vs Fourier expressivity for variational regression +# ---------------------------------------------------------------------------- +# Open research question. Holding qubit count (2), ansatz family (single-qubit +# re-uploading), optimizer (Adam), and dataset preprocessing fixed, vary the +# re-uploading depth L and measure (a) regression accuracy on synthetic 1D +# targets with known bandwidth, (b) the recovered Fourier spectrum overlap +# with the target spectrum. +# ============================================================================ + +id: Q04 +title: "Data re-uploading depth vs Fourier expressivity for variational quantum regression" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +synthesis: | + Data re-uploading is a variational quantum circuit pattern in which the + classical input x is loaded into the same qubits multiple times, + interleaved with trainable rotation layers: the circuit + W(theta_L) U(x) W(theta_{L-1}) U(x) ... W(theta_1) U(x) |0> applies L + encoding blocks U(x) and L trainable blocks W(theta). Perez-Salinas et al. + proved in 2020 that a single qubit with sufficient re-uploading depth is + a universal function approximator on bounded intervals. Schuld, Sweke, + and Meyer (PRA 2021) then proved the structural reason: a re-uploading + circuit's output function is a truncated Fourier series in the input x, + with the set of representable frequencies determined by the eigenvalue + spectrum of the encoding generators and growing with L. + + The practical question that follows is whether the theoretical + expressivity claim translates into empirical fit quality. As L grows we + expect (i) more representable frequencies (better fit on high-bandwidth + targets), and (ii) more parameters and more risk of overfitting on + finite-sample regression with discontinuous targets. Classical kernel + regression (RBF) and polynomial regression provide the natural baselines + because they too can be interpreted as choosing a basis of functions + (Gaussian or polynomial) of fixed expressivity; comparing quantum + re-uploading against these classical baselines is the cleanest way to + ask "does the Fourier structure imposed by re-uploading give a useful + inductive bias on these targets, or do classical models with comparable + capacity match it?" + + Implementation guidance is provided by the quantum-qiskit skill, which + is automatically injected at stage 10 and contains canonical code for + building re-uploading circuits using qiskit.circuit.ParameterVector and + rotation gates. Training uses Adam through parameter-shift gradients + (do NOT roll your own gradient finite-difference loop). The simulator + is fixed to AerSimulator(method='statevector') for noiseless evaluation + so the Fourier-spectrum probe is clean. + + Experimental protocol. Four quantum re-uploading depths (L=1, L=3, L=5, + L=7) are compared against two classical baselines (RBF kernel ridge + regression with bandwidth chosen by cross-validation; polynomial + regression of degree 7). All six conditions are evaluated on two 1D + regression targets: a sum-of-sinusoids signal with known frequency + content, and a step function (tests handling of discontinuities). + Each (condition, dataset) cell is averaged over 2 random seeds, giving + 6 conditions x 2 datasets x 2 seeds = 24 total cells. + + Research question: *On 1D regression with known target bandwidth, does + increasing data re-uploading depth L produce monotonically lower test + MSE on bandwidth-rich targets (sinusoid mixture), and does the + recovered Fourier spectrum overlap with the target spectrum scale + predictably with L?* + +hypotheses: + - id: H1 + statement: "On the sinusoid-mixture target, the L=5 re-uploading model achieves test MSE at least 4 times lower than the L=1 re-uploading model (2-seed mean), demonstrating that increasing re-uploading depth improves regression accuracy on bandwidth-rich targets." + measurable: true + - id: H2 + statement: "On the step-function target, the L=7 re-uploading model exhibits overfitting compared to L=5: L=7 test MSE is at least 15 percent higher than L=5 test MSE (2-seed mean), even though L=7 has more parameters and lower training MSE." + measurable: true + - id: H3 + statement: "At L=7 on the sinusoid-mixture target, the recovered Fourier spectrum (FFT of the trained circuit's output sampled on a uniform 256-point grid) has cosine similarity at least 0.7 with the target Fourier spectrum, restricted to the lowest 5 nonzero frequencies." + measurable: true + +experiment_design: + research_question: "On 1D regression with known target bandwidth, does increasing data re-uploading depth L produce monotonically lower test MSE on bandwidth-rich targets, and does the recovered Fourier spectrum overlap with the target spectrum scale predictably with L?" + conditions: + - name: "reuploading_L1" + description: "Single-qubit re-uploading circuit with L=1 encoding block. Encoding: U(x) = RZ(x). Trainable block: W(theta) = RY(theta_0) RZ(theta_1). Output: . 2 trainable parameters total." + - name: "reuploading_L3" + description: "Same single-qubit re-uploading template with L=3 encoding blocks. 6 trainable parameters." + - name: "reuploading_L5" + description: "Same template with L=5 encoding blocks. 10 trainable parameters." + - name: "reuploading_L7" + description: "Same template with L=7 encoding blocks. 14 trainable parameters." + baselines: + - "rbf_kernel_ridge: sklearn.kernel_ridge.KernelRidge(kernel='rbf', alpha=1e-3, gamma=tuned_via_grid_search). Strong nonparametric regression baseline." + - "polynomial_regression: sklearn.pipeline.make_pipeline(PolynomialFeatures(degree=7), Ridge(alpha=1e-3)). Matched-capacity polynomial basis baseline." + metrics: + - name: "test_mse" + direction: "minimize" + description: "Mean squared error on a 200-point held-out test set, averaged over 2 random seeds per (condition, dataset) cell. Primary metric." + - name: "train_mse" + direction: "minimize" + description: "Mean squared error on the 200-point training set after training completes." + - name: "fourier_spectrum_cosine" + direction: "maximize" + description: "Cosine similarity between the FFT of the trained model's output sampled on a uniform 256-point grid over [0, 1] and the FFT of the target function on the same grid, restricted to the lowest 5 nonzero frequency bins. Higher means the model has learned the target's Fourier content." + datasets: + - name: "sinusoid_mixture_1d" + source: "Synthetic generator" + description: "Target function f(x) = sum_k a_k * sin(2*pi*k*x) for k in {1, 2, 3, 5, 7} with random amplitudes a_k drawn from Uniform[0.5, 1.0] using a fixed dataset-generation seed (independent of per-cell training seeds). 200 training x-values + 200 test x-values uniformly sampled on [0, 1]. Targets are observed with Gaussian noise N(0, 0.05)." + - name: "step_function_1d" + source: "Synthetic generator" + description: "Target function f(x) = 1.0 if x > 0.5 else 0.0, plus Gaussian noise N(0, 0.05). Tests how the re-uploading model handles a discontinuity (its Fourier series will exhibit Gibbs ringing). 200 training + 200 test samples." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 900 + +rubric_path: "experiments/arc_bench/config/quantum/rubrics/Q04.json" diff --git a/tasks/quantum/manifests/Q05.yaml b/tasks/quantum/manifests/Q05.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6c92a784025971ae211a6add76b0f390ce14ba54 --- /dev/null +++ b/tasks/quantum/manifests/Q05.yaml @@ -0,0 +1,123 @@ +# ============================================================================ +# Q05 — Barren plateau onset under cost-function locality +# ---------------------------------------------------------------------------- +# Open research question. Holding qubit count (n=6) and ansatz family +# (hardware-efficient EfficientSU2) fixed, vary (a) the cost-function +# locality (single-qubit Z vs all-qubit Z product) and (b) the ansatz depth +# L. Measure the empirical variance of the gradient with respect to the +# first parameter across 100 random parameter samples per cell. Tests the +# Cerezo et al. 2021 prediction that local cost functions retain +# polynomial gradient variance even when global cost functions exhibit +# exponential decay (barren plateau). +# ============================================================================ + +id: Q05 +title: "Barren plateau onset under cost-function locality" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +synthesis: | + A barren plateau is the phenomenon, first identified by McClean et al. + in 2018, that the gradient of a variational quantum circuit's loss + becomes exponentially small in the number of qubits when the ansatz is + drawn from a sufficiently random circuit ensemble. Concretely, for a + loss L(theta) = where the parameterized + state is generated by a random-ish unitary, the variance of the + gradient dL/dtheta_k scales as O(1/2^n). For n = 20 qubits this + variance is below machine epsilon, and SGD or any gradient-based + optimizer has effectively zero signal to follow. This is the central + trainability obstruction in variational quantum machine learning. + + Cerezo et al. ("Cost function dependent barren plateaus in shallow + parametrized quantum neural networks", Nature Communications 2021) + showed that the locality of the cost function matters. If the cost is + a sum of single-qubit Pauli expectations (a local cost), then the + gradient variance can scale only polynomially in n even at moderate + ansatz depth. If the cost is a product or projection over all qubits + (a global cost), the variance is exponentially suppressed at any + depth. The mechanism is that local-cost observables overlap with only + a constant-size subspace of the 2^n dimensional Hilbert space, while + global-cost observables are spread thinly across the entire space and + almost always cancel. + + The empirical question this topic addresses is whether the local-cost + free lunch holds at moderate depth on a CPU-feasible 6-qubit + benchmark. We fix n=6 qubits and the EfficientSU2 hardware-efficient + ansatz family (a standard choice in qiskit). We vary depth (number of + ansatz layers L) and cost-function locality, then directly probe the + gradient distribution by sampling 100 random parameter vectors per + cell, computing the parameter-shift gradient with respect to theta_0 + for each, and reporting the empirical variance. A subsidiary check + trains each condition for 100 Adam iterations and reports the loss + change as a practical sanity probe of whether the gradient signal is + large enough to drive optimization at all. + + Implementation guidance is provided by the quantum-qiskit skill, + which is automatically injected at stage 10. Use qiskit.circuit.library + EfficientSU2 verbatim; do NOT hand-write H/CNOT/RY gate stacks (a + prior failed run produced an "ablation failure" where three nominally + different encodings collapsed to identical circuits because they + were hand-coded). Use qiskit_algorithms.utils.algorithm_globals + random_seed for reproducibility. Gradients are computed via the + parameter-shift rule on a statevector backend. + + Experimental protocol. Four conditions cover the cost x depth grid: + (local_cost_L2) shallow + local, the trainable control; (local_cost_L10) + medium + local, the regime Cerezo's theorem predicts is still + trainable; (local_cost_L20) deep + local, stressing the prediction; + (global_cost_L10) medium + global, the negative control where a barren + plateau is expected. Each condition is run with 3 different random + ansatz-structure seeds (controlling which qubits are entangled in each + EfficientSU2 layer) for variance estimation across architectures, and + 100 random parameter vectors per (condition, structure-seed) cell are + used to estimate gradient variance. Total cells: 4 conditions x 3 + ansatz seeds = 12 cells. No external dataset is required because this + is a measurement experiment on the ansatz family itself. + + Research question: *Does the local-cost trick avoid the barren plateau + on a 6-qubit EfficientSU2 ansatz at L=10 and L=20 ansatz depth, as + predicted by Cerezo et al. 2021, when compared against the + global-cost baseline at the same depth and against the shallow local-cost + control?* + +hypotheses: + - id: H1 + statement: "At ansatz depth L=10, the empirical variance of the gradient under local cost is at least 20 times larger than under global cost (3-seed mean of variance per condition), confirming Cerezo et al. 2021's prediction that locality changes the barren-plateau scaling." + measurable: true + - id: H2 + statement: "Under local cost, the gradient variance at L=20 is at least 1/100 of the gradient variance at L=2, i.e. the polynomial (sub-exponential) decay regime is maintained even at deep ansatz, with log10(var_L20) - log10(var_L2) > -2.0 (3-seed mean)." + measurable: true + - id: H3 + statement: "When trained with Adam for 100 gradient steps starting from random initialization, the global-cost-L10 condition produces a loss change of at most 1 percent of the initial loss (3-seed mean), demonstrating that the predicted barren plateau actively prevents practical training, while the local-cost-L10 condition produces a loss change of at least 10 percent." + measurable: true + +experiment_design: + research_question: "Does the local-cost trick avoid the barren plateau on a 6-qubit EfficientSU2 ansatz at L=10 and L=20, as predicted by Cerezo et al. 2021, when compared against the global-cost baseline and a shallow local-cost control?" + conditions: + - name: "local_cost_L10" + description: "Proposed-method core. EfficientSU2(num_qubits=6, reps=10, entanglement='linear') ansatz. Cost function: (expectation of Pauli Z on qubit 0 only). 100 random parameter vectors theta drawn from Uniform[-pi, pi]^{n_params}. For each theta, compute parameter-shift gradient dL/dtheta_0 by evaluating L at theta + (pi/2) * e_0 and theta - (pi/2) * e_0. Report variance of these 100 gradient values." + - name: "local_cost_L20" + description: "Proposed-method stress test. EfficientSU2 reps=20, same local cost . Tests whether the polynomial-decay prediction holds at deeper ansatz." + baselines: + - "local_cost_L2: trainable-control baseline. EfficientSU2 reps=2, local cost . Known to have non-vanishing gradient because the ansatz is too shallow to enter the barren-plateau regime." + - "global_cost_L10: barren-plateau negative-control baseline. EfficientSU2 reps=10, global cost (product of Z on all 6 qubits). Known from Cerezo 2021 to exhibit exponentially small gradient variance even though the ansatz depth matches local_cost_L10." + metrics: + - name: "log_gradient_variance" + direction: "maximize" + description: "log10 of the empirical variance of dL/dtheta_0 over 100 random parameter samples. Higher means more trainable gradient signal. Primary metric. Less negative numbers are better (closer to 0)." + - name: "mean_absolute_gradient" + direction: "maximize" + description: "Mean of |dL/dtheta_0| over the 100 random samples." + - name: "training_loss_change_fraction" + direction: "maximize" + description: "Relative loss change after 100 Adam steps starting from a random initialization: (L_initial - L_after_100_steps) / |L_initial|. Practical training-feasibility probe. Larger positive means training actually moved the loss." + datasets: + - name: "random_parameter_samples" + source: "Synthetic, generated in-script via numpy.random.RandomState(ansatz_structure_seed).uniform(-pi, pi, size=(100, n_params))" + description: "Each cell samples 100 random parameter vectors in the parameter space of EfficientSU2 at the given (num_qubits, reps). These are the inputs over which gradient variance is estimated. No external data is involved; this is a measurement experiment on the ansatz family." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 600 + +rubric_path: "experiments/arc_bench/config/quantum/rubrics/Q05.json" diff --git a/tasks/quantum/manifests/Q06.yaml b/tasks/quantum/manifests/Q06.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e3b9ae0d29a639723e3a3984cdf082512bd37dc3 --- /dev/null +++ b/tasks/quantum/manifests/Q06.yaml @@ -0,0 +1,122 @@ +# ============================================================================ +# Q06 — Neural-network warm-start for QAOA-MaxCut +# ---------------------------------------------------------------------------- +# Open research question. Train an MLP offline to predict good initial +# (beta, gamma) parameters for QAOA-MaxCut at p=1, given graph features +# as input. Compare against random initialization and the Brandao +# concentration "fixed init" (beta=gamma=pi/4). Test both in-distribution +# graphs (matched to training set) and out-of-distribution graphs to probe +# generalization. +# ============================================================================ + +id: Q06 +title: "Neural-network warm-start for QAOA-MaxCut initialization" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +synthesis: | + The Quantum Approximate Optimization Algorithm (QAOA) prepares a + parameterized quantum state to approximate the maximum cut of a graph. + At depth p=1 the state depends on two real parameters (beta, gamma), + and the algorithm proceeds by classical optimization of those two + parameters against the average cut size sampled from measurements of + the prepared state. The bottleneck for practical QAOA is the initial + parameter guess. Random initialization frequently falls into the + barren-plateau region or onto sub-optimal local minima, requiring many + classical iterations to escape. Brandao et al. 2018 showed + theoretically that the optimal (beta, gamma) values concentrate + around a problem-independent point on certain graph ensembles, so a + fixed initialization like (pi/4, pi/4) is a strong default. + + Beyond concentration, a richer strategy is to train a classical + neural network offline to predict the optimal (beta, gamma) given + features of the input graph. Verdon et al. 2019 introduced the idea + as quantum-aware meta-learning. Jain et al. (Quantum 2022, "Graph + neural network initialisation of QAOA") showed that a graph neural + network trained on a few thousand small graphs can predict initial + parameters that reduce the number of subsequent classical + optimization iterations by 5-10x compared to random initialization, + with measurable transfer to out-of-distribution graph families. + + The empirical question this topic addresses is whether the MLP-warm-start + strategy actually generalizes off-distribution: a model trained on + Erdős-Rényi graphs of one density should ideally retain its advantage + on regular graphs of a different size. We also want to verify whether + the simpler "fixed init at (pi/4, pi/4)" baseline (from Brandao 2018) + is competitive with the MLP-predicted init at p=1, since the + concentration result suggests that simple defaults may be hard to + beat in the low-depth regime. + + Implementation guidance is provided by the quantum-qiskit skill at + stage 10. Implement QAOA on the qiskit AerSimulator statevector + backend; the small graph sizes (n=6 to n=8) make exact statevector + simulation cheap. The MLP is sklearn.neural_network.MLPRegressor + trained offline on a precomputed set of (graph_features, optimal_beta, + optimal_gamma) tuples obtained by grid search on 200 training + graphs. Optimization at test time uses COBYLA with a hard cap of 20 + iterations. + + Experimental protocol. Two graph families serve as the test bed. + In-distribution: Erdős-Rényi G(n=6, p=0.5) graphs (matched to the MLP + training distribution). Out-of-distribution: 3-regular graphs at n=8 + (different size and edge density). Four conditions cover the + initialization strategies: random init, fixed init (beta=gamma=pi/4), + MLP-init evaluated in-distribution, and MLP-init evaluated + out-of-distribution. (MLP-init has 2 conditions because the model + trained on Erdős-Rényi G(n=6, p=0.5) is evaluated on both graph + families to test transfer.) Each (condition, graph family) cell is + averaged over 3 different random graph instances drawn within that + family. Total cells: 4 conditions x 2 graph families x 3 seeds = 24 + cells. + + Research question: *Does a small MLP trained to predict QAOA-MaxCut + initial parameters from graph features (degree distribution, edge + density, spectral gap) provide a faster warm-start than random + initialization or the Brandao 2018 fixed (pi/4, pi/4) initialization, + and does the advantage transfer to graphs from a different + distribution than the MLP was trained on?* + +hypotheses: + - id: H1 + statement: "On in-distribution Erdős-Rényi graphs at n=6, the MLP-predicted initialization reaches approximation ratio 0.9 in at most 10 COBYLA iterations on average (3-graph mean), while random initialization requires at least 18 iterations to reach the same target." + measurable: true + - id: H2 + statement: "On out-of-distribution 3-regular graphs at n=8, MLP-predicted initialization still beats random initialization by at least 20 percent in iteration count (3-graph median), demonstrating partial transfer of the learned initialization heuristic." + measurable: true + - id: H3 + statement: "At p=1 the fixed initialization (beta=gamma=pi/4) is competitive with MLP-predicted initialization: the gap in 3-graph-median iteration count between fixed init and MLP-init is at most 30 percent on in-distribution graphs, consistent with the Brandao 2018 concentration prediction that QAOA-1 parameters concentrate around a problem-independent point." + measurable: true + +experiment_design: + research_question: "Does an MLP trained on small Erdős-Rényi graph features predict QAOA-MaxCut initial parameters that reduce COBYLA iteration count compared to random and fixed init, and does the advantage transfer to graphs of a different distribution than the MLP was trained on?" + conditions: + - name: "mlp_init_in_distribution" + description: "Proposed method evaluated in-distribution. sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(32, 16), activation='relu', max_iter=2000, random_state=seed) trained offline on 200 Erdős-Rényi G(n=6, p=0.5) graphs with (degree_mean, degree_var, edge_density, n_edges) as features and grid-searched (beta_optimal, gamma_optimal) as targets. At test time, predict initial (beta, gamma) for a fresh in-distribution graph, then run COBYLA QAOA optimization starting from that initialization." + - name: "mlp_init_out_of_distribution" + description: "Proposed method evaluated out-of-distribution. Same MLP as mlp_init_in_distribution (trained on Erdős-Rényi G(n=6, p=0.5)). At test time, evaluated on 3-regular graphs at n=8: predict initial (beta, gamma) using the same feature extractor, then run COBYLA QAOA optimization." + baselines: + - "random_init: classical baseline. Initial (beta, gamma) drawn from Uniform[0, pi/2] x Uniform[0, pi/2] using a per-graph seed independent of optimization seed. Then COBYLA QAOA from that point." + - "fixed_init_pi_over_4: classical baseline from Brandao 2018. Initial (beta, gamma) = (pi/4, pi/4). Then COBYLA QAOA. Tests whether the concentration prediction holds in practice at p=1." + metrics: + - name: "iterations_to_target_ratio" + direction: "minimize" + description: "Number of COBYLA iterations required to reach approximation ratio (cut_size / optimal_cut_size) >= 0.9 on each graph, with a hard cap at 20 iterations (cells that don't reach 0.9 within 20 iterations report 20 and success=False). Primary metric. 3-graph median per condition x graph_family cell." + - name: "final_approximation_ratio" + direction: "maximize" + description: "approximation ratio (cut_size / optimal_cut_size) achieved after exactly 20 COBYLA iterations, regardless of whether the target threshold was met. 3-graph mean." + - name: "success_rate" + direction: "maximize" + description: "Fraction of the 3 random graph instances in a cell that reached approximation ratio 0.9 within the 20-iteration budget." + datasets: + - name: "erdos_renyi_n6_p05" + source: "networkx.erdos_renyi_graph(n=6, p=0.5, seed=graph_instance_seed)" + description: "In-distribution test set. 3 fresh random Erdős-Rényi graphs G(n=6, p=0.5), distinct from the 200 graphs used for MLP training. Optimal MaxCut value computed by brute-force enumeration over the 2^6 cuts. QAOA p=1 on AerSimulator statevector backend." + - name: "regular_3_n8" + source: "networkx.random_regular_graph(d=3, n=8, seed=graph_instance_seed)" + description: "Out-of-distribution test set. 3 random 3-regular graphs at n=8 (different size and edge density than the n=6 ER training distribution). Optimal MaxCut value by brute-force enumeration over 2^8 cuts. QAOA p=1 on AerSimulator statevector backend." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 1200 + +rubric_path: "experiments/arc_bench/config/quantum/rubrics/Q06.json" diff --git a/tasks/quantum/manifests/Q07.yaml b/tasks/quantum/manifests/Q07.yaml new file mode 100644 index 0000000000000000000000000000000000000000..77298b4d5d2a9e21df1820f4845b4587938e1388 --- /dev/null +++ b/tasks/quantum/manifests/Q07.yaml @@ -0,0 +1,125 @@ +# ============================================================================ +# Q07 — MPS classifier vs neural network at matched parameter count +# ---------------------------------------------------------------------------- +# Open research question. Compare Matrix Product State (MPS) classifiers +# at three bond dimensions against parameter-matched classical baselines +# (logistic regression, small CNN) on downsampled 8x8 image classification +# (MNIST and Fashion-MNIST). Tests the quantum-inspired classical / +# tensor-network ML claim that MPS classifiers are competitive with neural +# networks at small parameter budgets. +# ============================================================================ + +id: Q07 +title: "Matrix Product State classifier vs neural network at matched parameter count" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +synthesis: | + A Matrix Product State (MPS) is a tensor network in which a target + tensor of N indices is decomposed into a 1D chain of N small tensors, + each connected to its neighbors by "bond" indices of dimension chi. + MPS originated in condensed-matter physics as the representation + underlying the Density Matrix Renormalization Group (DMRG) for 1D + ground-state computation, and Schollwock's 2011 review remains the + canonical reference. Stoudenmire and Schwab (NeurIPS 2016, "Supervised + Learning with Tensor Networks") repurposed the MPS as a classical + supervised classifier. The idea is simple: each input feature + (pixel) is mapped to a small feature vector (e.g. cos(pi*x/2), + sin(pi*x/2) for grayscale x in [0,1]), and the per-pixel feature + vectors are contracted with a chain of MPS tensors to produce a + class-score vector. The MPS bond dimension chi controls how much + correlation between distant pixels the classifier can capture, + giving an interpretable expressivity knob. + + Crucially, the MPS classifier runs entirely on classical hardware. + This makes it a "quantum-inspired classical" baseline: the same + mathematical machinery as a low-entanglement quantum state, but + implemented as a NumPy tensor contraction. Glasser et al. (PRX 2018) + extended the framework to richer tensor networks. More recent + benchmarks (Liu et al. 2022 Nature Communications Physics) have asked + where the bond-dimension sweet spot is for image classification and + whether MPS classifiers can be competitive with CNNs at small + parameter budgets. + + This topic asks whether an MPS classifier matches a parameter-matched + CNN baseline on a CPU-feasible image classification task. The + comparison is not "does MPS beat CNN at large scale" (it doesn't, on + high-resolution images) but rather "at matched small parameter + counts, is the MPS inductive bias competitive with a small CNN's + spatial bias on 8x8 downsampled MNIST and Fashion-MNIST?" The + question is timely because tensor-network ML is increasingly cited + as the honest classical baseline against which to measure quantum + machine learning claims of advantage. + + Implementation guidance. Use pure NumPy or PyTorch for the MPS + contraction (no qiskit required for this topic; it is quantum-inspired + classical computation). The standard MPS classifier from Stoudenmire + & Schwab 2016 stores N+1 tensors of shape (chi, d, chi) where N is + the number of pixels (64 for an 8x8 image), d=2 is the per-pixel + feature dimension (we use the cos/sin embedding), and chi is the + bond dimension. The CNN baseline is a single 3x3 convolution layer + with K filters followed by a fully connected output layer; K is + chosen so the total parameter count is within 10 percent of the MPS + parameter count at each bond dimension. + + Experimental protocol. Three MPS bond dimensions (chi=4, chi=8, + chi=16) are compared against two classical baselines (logistic + regression on flattened 8x8 pixels; small CNN with parameter count + matched to the chi=8 MPS). All five conditions are evaluated on two + datasets: 8x8 downsampled MNIST (10 classes, 5000 train / 1000 test + images) and 8x8 downsampled Fashion-MNIST (10 classes, same + size). Each (condition, dataset) cell is averaged over 3 random + seeds (controlling MPS / CNN initialization and the train / test + split). Total cells: 5 conditions x 2 datasets x 3 seeds = 30 cells. + + Research question: *On 8x8 downsampled MNIST and Fashion-MNIST, do + MPS classifiers at moderate bond dimension chi reach the test + accuracy of a parameter-matched CNN, and how does the accuracy + scale with chi?* + +hypotheses: + - id: H1 + statement: "On 8x8 downsampled MNIST, the MPS classifier at bond dimension chi=16 reaches at least 92 percent test accuracy (3-seed mean), matching the Stoudenmire & Schwab 2016 result scaled to this image size." + measurable: true + - id: H2 + statement: "On 8x8 downsampled Fashion-MNIST, the parameter-matched CNN beats the chi=16 MPS classifier by at least 5 absolute percentage points (3-seed mean), reflecting the CNN's stronger inductive bias for spatial texture features that are more important on Fashion-MNIST than on plain digits." + measurable: true + - id: H3 + statement: "Test accuracy of the MPS classifier scales log-linearly with bond dimension: on 8x8 MNIST the chi=4 to chi=8 gain in 3-seed mean accuracy is greater than the chi=8 to chi=16 gain (consistent with saturating expressivity at moderate chi)." + measurable: true + +experiment_design: + research_question: "On 8x8 downsampled MNIST and Fashion-MNIST, do MPS classifiers at moderate bond dimension chi reach the test accuracy of a parameter-matched CNN, and how does accuracy scale with chi?" + conditions: + - name: "mps_chi4" + description: "Matrix Product State classifier with bond dimension chi=4. 64 pixels x 2-dim per-pixel feature embedding x chi=4 bonds, with a per-class output index. Approximate parameter count: 64 * 2 * 4 * 4 = 2048 parameters. Trained with Adam on cross-entropy for 50 epochs." + - name: "mps_chi8" + description: "MPS classifier with chi=8. Approximate parameter count: 64 * 2 * 8 * 8 = 8192. Trained with Adam on cross-entropy for 50 epochs." + - name: "mps_chi16" + description: "MPS classifier with chi=16. Approximate parameter count: 64 * 2 * 16 * 16 = 32768. Trained with Adam on cross-entropy for 50 epochs." + baselines: + - "logistic_regression: sklearn.linear_model.LogisticRegression(C=1.0, max_iter=1000, multi_class='multinomial') on flattened 8x8 pixels. Classical linear baseline, ~640 parameters." + - "small_cnn_matched: PyTorch CNN with a single 3x3 conv layer of K=12 filters followed by an FC(K*6*6, 10) output, chosen so total parameter count is within 10 percent of the chi=8 MPS (target ~8200 parameters). Trained with Adam for 50 epochs." + metrics: + - name: "test_accuracy" + direction: "maximize" + description: "Top-1 classification accuracy on the 1000-image held-out test set, 3-seed mean. Primary metric." + - name: "trainable_parameter_count" + direction: "minimize" + description: "Total number of trainable parameters in the model. Reported for parity validation across conditions: matched-parameter comparisons require this to be within 10 percent across the relevant condition pair." + - name: "training_time_sec" + direction: "minimize" + description: "Wall-clock seconds for 50 epochs of training, measured per cell. Tests the practical compute cost of each method at matched accuracy." + datasets: + - name: "mnist_8x8_downsampled" + source: "sklearn.datasets.fetch_openml('mnist_784') downsampled to 8x8 via skimage.transform.resize" + description: "MNIST handwritten digits 0-9, downsampled from 28x28 to 8x8 grayscale, normalized to [0, 1]. 5000 training images, 1000 test images (stratified split, fixed random_state=seed). The 8x8 resolution is small enough for CPU MPS contraction but large enough to retain class structure (LogReg should reach ~85 percent here)." + - name: "fashion_mnist_8x8_downsampled" + source: "sklearn.datasets.fetch_openml('Fashion-MNIST') downsampled to 8x8" + description: "Fashion-MNIST clothing items (T-shirt, trouser, pullover, etc.) downsampled from 28x28 to 8x8. Tests model performance on texture-rich images where CNN inductive bias should be more important than for handwritten digits." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 1500 + +rubric_path: "experiments/arc_bench/config/quantum/rubrics/Q07.json" diff --git a/tasks/quantum/manifests/Q08.yaml b/tasks/quantum/manifests/Q08.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8617e1ecaadbdbd9681e93ca5af7772e119273cd --- /dev/null +++ b/tasks/quantum/manifests/Q08.yaml @@ -0,0 +1,115 @@ +# ============================================================================ +# Q08 — Layerwise learning vs end-to-end VQC training +# ---------------------------------------------------------------------------- +# Open research question. Three training strategies for a 4-qubit, reps=5 +# EfficientSU2 variational quantum classifier on binary UCI tasks: +# (a) layerwise — train reps=1 to convergence, freeze, add reps=2, etc; +# (b) end-to-end — train all reps=5 layers simultaneously from random init; +# (c) random-init control — initialize at random and report the untrained +# classifier as a floor. Compare against logistic regression and a +# parameter-matched classical MLP for the ≥2-baseline gate. +# ============================================================================ + +id: Q08 +title: "Layerwise learning vs end-to-end training for variational quantum classifiers" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +synthesis: | + Variational quantum classifiers (VQCs) at moderate depth (reps >= 5) + suffer from barren plateaus: the gradient of the loss with respect to + any single parameter is exponentially suppressed when the ansatz is + initialized randomly across all layers. Skolik et al. (Quantum 2021, + "Layerwise learning for quantum neural networks") proposed a training + schedule that avoids this trap by adding ansatz layers incrementally. + Concretely, the agent trains a reps=1 ansatz to convergence, freezes + its parameters, prepends or appends a fresh reps=1 block initialized + to identity, trains only the new block to convergence, and so on + until the target depth is reached. Each layer of training sees a much + smaller parameter space, so the gradient signal stays well above the + barren-plateau floor at every step. + + The empirical question this topic addresses is whether the layerwise + schedule actually delivers higher final test accuracy AND faster + practical convergence than naive end-to-end training of the full + reps=5 ansatz on small UCI binary classification tasks. A + parameter-matched classical baseline (sklearn MLPClassifier) and a + logistic regression baseline serve as the cross-paradigm controls + required by the bench's PROCEED gate; they also calibrate the + difficulty of each dataset (logistic regression should fit linear + separable cases cleanly). + + Implementation guidance is provided by the quantum-qiskit skill, + which is automatically injected at stage 10. Use + qiskit.circuit.library.EfficientSU2(num_qubits=4, reps=5, + entanglement='linear') as the underlying ansatz family, and use + qiskit_machine_learning.algorithms.VQC for training (it works in + qiskit 2.x; do NOT use qiskit_algorithms.VQE which is broken). For + the layerwise condition, the agent must implement the layer-freezing + schedule manually: construct an ansatz with only the first k blocks + trainable and previous blocks bound to their converged parameters. + The classical baselines train on the same 80/20 train/test split + using the same per-seed random_state. + + Experimental protocol. Three quantum conditions compare training + schedules at the same final depth (reps=5): layerwise growth from + reps=1 to reps=5, end-to-end training of reps=5 from random + initialization, and a random-init control (no training, evaluate + the randomly initialized reps=5 ansatz). Two classical baselines + (logistic regression, MLPClassifier with parameter count matched to + the VQC's 40-parameter count) provide the cross-paradigm reference. + Each (condition, dataset) cell is averaged over 3 seeds. Total: + 5 conditions x 2 datasets x 3 seeds = 30 cells. + + Research question: *Does layerwise incremental training of a VQC + reach higher test accuracy or faster convergence than end-to-end + training of the same final-depth ansatz, and does the layerwise + gradient norm at each newly added layer remain above the + barren-plateau threshold?* + +hypotheses: + - id: H1 + statement: "Layerwise training reaches higher test accuracy than end-to-end training on at least 1 of 2 datasets, with a 3-seed-mean gap of at least 3 absolute percentage points." + measurable: true + - id: H2 + statement: "The mean gradient norm during the first 10 optimization steps of the layerwise schedule (averaged across all newly added blocks) is at least 5 times larger than the mean gradient norm during the first 10 steps of end-to-end training (3-seed mean), confirming the Skolik 2021 mechanism." + measurable: true + - id: H3 + statement: "Both layerwise and end-to-end VQC outperform the random-init control (no training) by at least 5 absolute percentage points on both datasets (3-seed mean), confirming that training actually contributes — the ansatz itself is not memorizing class structure at initialization." + measurable: true + +experiment_design: + research_question: "Does layerwise incremental training of a 4-qubit reps=5 VQC reach higher test accuracy or faster convergence than end-to-end training of the same ansatz on small UCI binary classification?" + conditions: + - name: "layerwise_growth" + description: "EfficientSU2(num_qubits=4, reps=5, entanglement='linear'). Training schedule: (1) train only the first reps=1 block via VQC.fit() for 80 COBYLA iterations; (2) freeze those parameters, add a fresh reps=1 block initialized to small N(0, 0.01) noise, train only the new block for 80 iterations; repeat until all 5 blocks are trained. Record the gradient norm at the start of each new layer's training." + - name: "end_to_end" + description: "EfficientSU2(num_qubits=4, reps=5, entanglement='linear'). Single VQC.fit() call training all 40 parameters simultaneously from random N(0, 0.1) initialization for 400 COBYLA iterations (5 x 80, matching the total iteration budget of the layerwise schedule)." + - name: "random_init_control" + description: "Same EfficientSU2 ansatz, parameters drawn from Uniform[-pi, pi] and NEVER trained. Evaluate predict() directly on the test set. Floor for trainability — any trained model should beat this." + baselines: + - "logistic_regression: sklearn.linear_model.LogisticRegression(max_iter=1000, random_state=seed). Linear classical baseline." + - "mlp_matched: sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(8,), activation='relu', max_iter=500, random_state=seed). Parameter count: 4*8 + 8 + 8*1 + 1 = 49 parameters, within 20 percent of the VQC's 40 parameters." + metrics: + - name: "test_accuracy" + direction: "maximize" + description: "Held-out 20 percent test split accuracy, 3-seed mean. Primary metric." + - name: "mean_gradient_norm_first10" + direction: "maximize" + description: "L2 norm of the gradient vector averaged across the first 10 optimization steps. For layerwise, averaged across the first 10 steps of each newly added block. For end-to-end, the first 10 steps of the single training run. Tests the Skolik mechanism." + - name: "iterations_to_target_accuracy" + direction: "minimize" + description: "Number of total COBYLA iterations to reach test accuracy >= 0.8 (or to budget 400 if never reached). Tests practical convergence speed." + datasets: + - name: "uci_iris_binary_pca4" + source: "sklearn.datasets.load_iris + PCA to 4 dimensions" + description: "Iris dataset restricted to classes {versicolor=1, virginica=2} (binary, 100 samples). PCA to 4 dimensions on standard-scaled features. 80/20 split. Approximately linearly separable; logistic regression should reach >=85 percent." + - name: "breast_cancer_pca4" + source: "sklearn.datasets.load_breast_cancer + PCA to 4 dimensions" + description: "Wisconsin breast cancer dataset, 569 samples, 30 features reduced to 4 via PCA. 80/20 split. Non-trivially separable; logistic regression reaches around 0.92." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 1500 + +rubric_path: "experiments/arc_bench/config/quantum/rubrics/Q08.json" diff --git a/tasks/quantum/manifests/Q09.yaml b/tasks/quantum/manifests/Q09.yaml new file mode 100644 index 0000000000000000000000000000000000000000..10f82475ef722bfc17c8c59f4391d263922179e6 --- /dev/null +++ b/tasks/quantum/manifests/Q09.yaml @@ -0,0 +1,115 @@ +# ============================================================================ +# Q09 — Noise-aware variational quantum classifier training +# ---------------------------------------------------------------------------- +# Open research question. Three noise-aware training strategies for a VQC +# on a synthetic 4D classification task: train under ideal statevector, +# train under simulated depolarizing noise at low rate (p=0.001), train at +# mid rate (p=0.005), train at high rate (p=0.01). Evaluate each on +# ideal test data AND noisy test data, characterizing the +# robustness-vs-accuracy tradeoff and detecting negative transfer. +# ============================================================================ + +id: Q09 +title: "Noise-aware variational quantum classifier training" +arxiv_id: null +venue: "ARC-Bench 2026" +paper_asset: null + +synthesis: | + Real quantum hardware exhibits gate errors, readout errors, and + decoherence at rates that vary across devices. A VQC trained on a + noiseless statevector simulator may converge to parameters that work + on the ideal training distribution but degrade sharply when deployed + on a noisy device. The noise-aware training strategy is to inject + simulated noise during training (e.g. a depolarizing channel after + each two-qubit gate) so the optimizer can find parameters that are + robust to that noise model. The closest classical analog is + adversarial training or training-time data augmentation. + + Two open empirical questions follow. First, does noise-aware training + produce strictly more robust models on noisy test data than ideal + training, and at what training noise rate is the robustness gain + worth the loss of clean-test accuracy? Second, does training at high + noise (p=0.01) cause negative transfer to clean test data — i.e. + does the model overfit to the noise statistics and underperform an + ideal-trained model on noise-free evaluation? Wang et al. (NeurIPS + 2022 "QuantumNAS: Noise-Adaptive Search for Robust Quantum + Circuits") and Sharma et al. (PRX Quantum 2022 "Trainability of + dissipative perceptron-based quantum neural networks") explored + these tradeoffs at small scale, leaving the precise sweet-spot + noise rate as a benchmark question. + + Implementation guidance is provided by the quantum-qiskit skill at + stage 10. Use qiskit_aer.AerSimulator with a custom NoiseModel that + applies a single-qubit and two-qubit depolarizing channel after each + parameterized gate. The training uses + qiskit_machine_learning.algorithms.VQC with a custom + qiskit.primitives.BackendSamplerV2 bound to the noisy AerSimulator + for noisy-train conditions, and the noiseless reference + qiskit.primitives.StatevectorSampler for the ideal-train condition. + Evaluation always runs on both an ideal statevector test set and a + noisy test set at p=0.01 (the "deployment noise rate") so each cell + produces two test_accuracy values: clean and noisy. The primary + metric is the average across both. + + Experimental protocol. Four quantum training conditions vary the + training noise rate: ideal (p=0), low noise (p=0.001), mid noise + (p=0.005), high noise (p=0.01). Two baselines complete the bench + gate: a no-training control (random initialization, no fit) and + logistic regression on the same 4D features. All six conditions + are evaluated on a single synthetic binary classification dataset + (make_classification with class_sep=0.5, 200 samples, 4 features), + averaged over 3 seeds. Total: 6 conditions x 1 dataset x 3 seeds + = 18 cells. + + Research question: *On a synthetic 4D binary classification task, + does noise-aware VQC training at a moderate training noise rate + (p=0.005) produce a model that is more robust to test-time + depolarizing noise (p=0.01) than an ideal-trained VQC, and does + high training noise (p=0.01) cause negative transfer to clean + test data?* + +hypotheses: + - id: H1 + statement: "On the noisy test set at p=0.01, the noisy-trained-mid VQC (training noise p=0.005) achieves test accuracy at least 5 absolute percentage points higher than the ideal-trained VQC (3-seed mean), demonstrating that moderate noise-aware training improves test-time noise robustness." + measurable: true + - id: H2 + statement: "On the ideal test set (noiseless), the ideal-trained VQC outperforms every noisy-trained variant by at least 2 absolute percentage points (3-seed mean), reflecting the no-free-lunch tradeoff that noise-aware training sacrifices some clean-test accuracy." + measurable: true + - id: H3 + statement: "The high-noise-trained VQC (training noise p=0.01) performs worse than the mid-noise-trained VQC on BOTH clean and noisy test sets (3-seed mean), indicating that p=0.01 is past the sweet spot and the optimizer is overfitting to noise statistics — negative transfer." + measurable: true + +experiment_design: + research_question: "Does noise-aware VQC training at a moderate training noise rate (p=0.005) produce a model more robust to test-time depolarizing noise than ideal training, and does high training noise cause negative transfer on clean test data?" + conditions: + - name: "train_ideal" + description: "VQC trained on noiseless qiskit.primitives.StatevectorSampler. EfficientSU2(num_qubits=4, reps=2, entanglement='linear') ansatz + ZFeatureMap feature map. qiskit_machine_learning.VQC(optimizer=COBYLA(maxiter=200), sampler=StatevectorSampler()). After training, evaluate on BOTH ideal test (StatevectorSampler) and noisy test (depolarizing p=0.01 NoiseModel)." + - name: "train_noisy_low" + description: "Same ansatz + feature map. VQC trained on a noisy AerSimulator with depolarizing channel p=0.001 applied after every single-qubit and two-qubit gate. Same COBYLA maxiter=200. Evaluate on both ideal and noisy test sets." + - name: "train_noisy_mid" + description: "Same as train_noisy_low but training noise rate p=0.005." + - name: "train_noisy_high" + description: "Same as train_noisy_low but training noise rate p=0.01 (matches the test-noise rate)." + baselines: + - "no_training_control: Same ansatz but parameters randomly initialized and never trained. Evaluate predict() directly on both test sets. Establishes the untrained floor." + - "logistic_regression: sklearn.linear_model.LogisticRegression(max_iter=1000, random_state=seed) on the same 4 features. Classical baseline." + metrics: + - name: "test_accuracy" + direction: "maximize" + description: "Average of test_accuracy_ideal and test_accuracy_noisy_p01, 3-seed mean. Primary metric for the bench." + - name: "test_accuracy_ideal" + direction: "maximize" + description: "Test accuracy on the noiseless test set (StatevectorSampler evaluation), 3-seed mean." + - name: "test_accuracy_noisy_p01" + direction: "maximize" + description: "Test accuracy on the noisy test set at depolarizing rate p=0.01 (AerSimulator with NoiseModel), 3-seed mean. The 'deployment-noise' robustness metric." + datasets: + - name: "synthetic_classification_4d_sep05" + source: "sklearn.datasets.make_classification" + description: "make_classification(n_samples=200, n_features=4, n_informative=3, n_redundant=0, n_clusters_per_class=1, class_sep=0.5, flip_y=0.05, random_state=seed). Moderately difficult binary classification, native 4D. StandardScaler fit on train only. 80/20 split." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 1500 + +rubric_path: "experiments/arc_bench/config/quantum/rubrics/Q09.json" diff --git a/tasks/quantum/manifests/Q10.yaml b/tasks/quantum/manifests/Q10.yaml new file mode 100644 index 0000000000000000000000000000000000000000..79c1b2166ce422ccc1181544d5070d129e8d16cc --- /dev/null +++ b/tasks/quantum/manifests/Q10.yaml @@ -0,0 +1,116 @@ +# ============================================================================ +# Q10 — Quantum autoencoder for pure-state compression +# ---------------------------------------------------------------------------- +# Open research question. Train a parameterized quantum circuit to compress +# n-qubit Haar-random pure states into m state (verified via a + SWAP test against fresh |0> ancillas), which is equivalent to + maximizing the fidelity between the reconstructed state and the + original input state. + + The training data is an ensemble of input states drawn from some + distribution. If the ensemble is concentrated in a low-dimensional + manifold of the 2^n Hilbert space, the autoencoder can in principle + reach near-perfect reconstruction at significant compression (m < + n). If the ensemble is Haar-random pure states, no compression is + achievable in principle and the average reconstruction fidelity is + upper bounded by 2^(m-n) (the latent space is too small to hold a + generic state). + + The empirical question this topic addresses is what compression + fidelity is achievable on a small Haar-random ensemble at a few + compression ratios, and whether the trained autoencoder + meaningfully beats a random-unitary baseline (which represents the + null hypothesis: a random encoder cannot do better than chance). + This is the cleanest possible test of "does the autoencoder learn + anything" without confounding from structured ensembles. + + Implementation guidance is provided by the quantum-qiskit skill at + stage 10. The encoder and decoder are EfficientSU2(num_qubits=n, + reps=3, entanglement='linear') circuits built from + qiskit.circuit.library. The SWAP-test loss is implemented manually + via auxiliary qubits and a Hadamard-controlled-SWAP-Hadamard pattern. + Use qiskit.primitives.StatevectorSampler for the SWAP-test + measurement and qiskit.primitives.StatevectorEstimator for the + reconstruction-fidelity diagnostic. Training uses + qiskit_algorithms.optimizers.COBYLA.minimize() over the SWAP-test + loss (DO NOT use qiskit_algorithms.VQE — see the quantum-qiskit + skill's note about qiskit 2.x compatibility). + + Experimental protocol. Three quantum compression conditions probe + the compression-fidelity tradeoff: compress 4 qubits -> 2 latent, + compress 4 -> 3, compress 6 -> 3. Two no-skill baselines establish + the floor: no_compression (identity encoder, m=n, fidelity should + be ~1.0) and random_unitary_compression (encoder parameters drawn + randomly and never trained). Test data is an ensemble of 20 + Haar-random pure states per seed. Each (condition, seed) cell + reports the mean reconstruction fidelity over the 20 test states. + Total: 5 conditions x 1 ensemble x 3 seeds = 15 cells. + + Research question: *On an ensemble of Haar-random pure states, what + reconstruction fidelity does a trained quantum autoencoder achieve + at compression ratios 4->2, 4->3, and 6->3, and how much better is + it than an untrained (random-unitary) encoder at the same + compression ratio?* + +hypotheses: + - id: H1 + statement: "The trained autoencoder achieves reconstruction fidelity at least 0.2 absolute higher than a random-unitary encoder at the same compression ratio (3-seed mean across the 20 Haar test states), on at least 2 of 3 compression-ratio conditions, confirming that training extracts non-trivial structure from the Haar ensemble." + measurable: true + - id: H2 + statement: "Trained reconstruction fidelity is strictly monotone in latent dimension: the 4->3 condition achieves fidelity at least 0.15 higher than the 4->2 condition (3-seed mean), as expected from the Hilbert-space capacity argument 2^m / 2^n." + measurable: true + - id: H3 + statement: "The no_compression baseline (identity encoder, m=n) achieves reconstruction fidelity at least 0.95 (3-seed mean), confirming the experimental setup is correctly implemented — the trash-qubit register is non-functional when m=n and the test pipeline should report near-perfect fidelity." + measurable: true + +experiment_design: + research_question: "On an ensemble of Haar-random pure states, what reconstruction fidelity does a trained quantum autoencoder achieve at compression ratios 4->2, 4->3, 6->3, and how much better is it than an untrained encoder at the same compression?" + conditions: + - name: "compress_4_to_2" + description: "Encoder: EfficientSU2(num_qubits=4, reps=3, entanglement='linear'). After applying the encoder, qubits 2 and 3 are the 'trash' register and the SWAP test against fresh |0> ancillas is summed into the loss. Latent qubits: 0, 1. Decoder is the inverse encoder. Training: 100 iterations of qiskit_algorithms.optimizers.COBYLA(maxiter=100).minimize() on a fixed batch of 5 Haar-random states drawn once per seed." + - name: "compress_4_to_3" + description: "Same as compress_4_to_2 but with 1 trash qubit (qubit 3) and 3 latent qubits (0, 1, 2). Easier compression task." + - name: "compress_6_to_3" + description: "Encoder: EfficientSU2(num_qubits=6, reps=3, entanglement='linear'). 3 trash qubits (3, 4, 5) and 3 latent qubits (0, 1, 2). Harder compression task (50 percent compression on a larger Hilbert space)." + baselines: + - "no_compression: Encoder is the identity circuit (no parameters). All qubits are latent (m=n); no trash qubits. The SWAP test trivially passes and reconstruction fidelity equals 1.0 exactly. Sanity check on the eval pipeline." + - "random_unitary_compression: Same EfficientSU2 ansatz as the trained conditions, but parameters drawn randomly from Uniform[-pi, pi] once per seed and NEVER trained. Establishes the no-skill floor — represents 'what if the encoder is just random noise'." + metrics: + - name: "reconstruction_fidelity" + direction: "maximize" + description: "Mean of ||^2 over the 20 Haar-random test states, 3-seed mean. Primary metric. Computed as 1 - SWAP_test_probability_of_outcome_1 averaged over the trash qubits, OR via direct Statevector inner product when running on the noiseless simulator." + - name: "swap_test_loss" + direction: "minimize" + description: "Training loss at the final iteration. Sum over the m trash qubits of P(SWAP_test_outcome=1) on each. Equivalent to 1 - average_trash_qubit_fidelity_with_|0>." + - name: "training_time_sec" + direction: "minimize" + description: "Wall-clock seconds for the 100 COBYLA iterations of training. Reported for context — the compression task should fit well under 60 seconds per cell." + datasets: + - name: "haar_random_states_n4" + source: "qiskit.quantum_info.random_statevector(2**4, seed=ensemble_seed) — generate 20 Haar-random 4-qubit pure states with a fixed per-seed ensemble seed" + description: "An ensemble of 20 Haar-random 4-qubit pure states (for compress_4_to_2 and compress_4_to_3 conditions). A separate Haar ensemble of 20 6-qubit states is generated for the compress_6_to_3 condition. Training uses a fixed batch of 5 of these states; testing uses all 20. The ensemble is fully unstructured by construction, so any non-trivial reconstruction fidelity above the random-unitary baseline reflects the autoencoder learning a structured compression rule on Haar states." + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 1200 + +rubric_path: "experiments/arc_bench/config/quantum/rubrics/Q10.json" diff --git a/tasks/quantum/rubrics/Q01.json b/tasks/quantum/rubrics/Q01.json new file mode 100644 index 0000000000000000000000000000000000000000..ffd3cae81d788410db78d709929ed1785ee7086b --- /dev/null +++ b/tasks/quantum/rubrics/Q01.json @@ -0,0 +1,109 @@ +{ + "id": "q01-root", + "requirements": "A minimal pilot study comparing three quantum data encoding strategies (angle, amplitude, ZZ feature map) for a variational quantum classifier on ONE moderately difficult 6D binary classification dataset (synthetic_classification_6d with class_sep=0.4). The agent must (a) implement all three encodings as drop-in feature maps for a shared 6-qubit EfficientSU2 ansatz, (b) train each (encoding, seed) cell using qiskit_machine_learning.algorithms.VQC + COBYLA over 5 random seeds, (c) report test accuracy per quantum cell and a classical logistic_regression baseline on the same data, and (d) produce a writeup that assigns supported / refuted / inconclusive verdicts to H1, H2, H3 with numerical evidence.", + "judging_note": "Quantum ML pilots are scored on (i) whether all three encodings are implemented per qiskit convention, (ii) whether ansatz / optimizer / simulator are held fixed across encodings so the encoding axis is isolated, (iii) whether the quantum cells produce non-trivial accuracy (not near 0.5 random for all 3 encodings, which would indicate a training bug rather than an encoding-specific effect), and (iv) whether per-hypothesis claims are backed by numerical evidence. Quantitative result leaves are scored on a graded scale: 100% if the hypothesis threshold is cleanly met, 67% if the trend is in the predicted direction but does not clear the numerical threshold, 33% if the trend is ambiguous, 0% if the result contradicts the hypothesis or evidence is missing.", + "weight": 1, + "sub_tasks": [ + { + "id": "q01-code", + "requirements": "Code-development bucket: all 3 encodings implemented per qiskit convention, shared ansatz + optimizer + simulator + training framework are wired identically across cells, and per-cell metrics are logged in the expected METRIC_RESULT JSON format.", + "weight": 2, + "sub_tasks": [ + { + "id": "q01-code-encodings", + "requirements": "All three encodings are implemented by IMPORTING and INSTANTIATING the canonical classes from qiskit.circuit.library by name, NOT by hand-writing the gate sequences. Concretely the agent's code MUST contain (grep-verifiable): 'from qiskit.circuit.library import' followed by at least the symbols ZFeatureMap, StatePreparation, and ZZFeatureMap; and the three encoding builders must instantiate exactly those classes (angle_encoding = ZFeatureMap(feature_dimension=6, reps=1); amplitude_encoding wraps StatePreparation on a 64D L2-normalized zero-padded vector; zz_feature_map = ZZFeatureMap(feature_dimension=6, reps=2, entanglement='linear')). Hand-written H/RZ/RZZ loops in place of these library classes FAIL this leaf. Additionally, a sanity check at runtime confirms that the three encodings produce DIFFERENT output statevectors for a fixed non-zero input (if they produce identical statevectors, the dispatch is broken and this leaf scores 0). Class bodies are non-trivial (not 'class X(Base): pass' shells).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "q01-code-shared-training", + "requirements": "Training uses qiskit_machine_learning.algorithms.VQC with the reference Sampler primitive. The variational ansatz (EfficientSU2 num_qubits=6 reps=1 entanglement='linear'), the classical optimizer (COBYLA maxiter=200), and the simulator backend (AerSimulator method='statevector') are identical across the 3 quantum encoding cells. Only the feature_map argument is swapped. No hand-rolled training loop in place of VQC.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "q01-code-metric-logging", + "requirements": "Each per-(condition, seed) result is emitted to stdout as a single line beginning with METRIC_RESULT followed by a JSON object whose keys include at minimum: condition (one of angle_encoding / amplitude_encoding / zz_feature_map / logistic_regression / mlp_classifier), dataset (synthetic_classification_6d), seed (integer 0..4), test_accuracy (float in [0,1]). Both classical baselines (LogisticRegression and MLPClassifier) are logged in the same format.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q01-exec", + "requirements": "Execution-validity bucket: all cells ran and produced numerically valid outputs.", + "weight": 2, + "sub_tasks": [ + { + "id": "q01-exec-cells-ran", + "requirements": "At least 25 cells out of 25 expected (5 conditions: 3 quantum encodings + LogisticRegression + MLPClassifier x 1 dataset x 5 seeds) completed without unhandled errors and produced an accuracy value. Missing more than 2 cells (10 percent) without documented justification fails this leaf.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q01-exec-numerical", + "requirements": "Numerical validity: no NaN or Inf in test_accuracy. Every test_accuracy lies in [0, 1]. No quantum cell collapses to a degenerate single-class prediction. At least one quantum encoding achieves test_accuracy strictly greater than 0.55 on at least 1 of 5 seeds (rules out the failure mode where all quantum cells stay near 0.5 random-chance because optimization never actually ran). The 3 quantum encodings MUST NOT produce identical test_accuracy across all 5 seeds (this is the ABLATION FAILURE pattern observed in a prior failed run: if angle/amplitude/zz produce bit-for-bit identical accuracies on every seed, the feature_map argument is being silently ignored by the training code, and this leaf scores 0). Training takes nontrivial wall time per cell (at least a few seconds; sub-1s per cell means VQC.fit was not actually invoked).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q01-results", + "requirements": "Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "q01-result-h1-quant", + "requirements": "Quantitative test of H1. Does the best of the 3 quantum encodings (5-seed mean) achieve test_accuracy within 5 absolute percentage points of the BETTER classical baseline (max of LogisticRegression and MLPClassifier 5-seed means)? 100% if gap is <= 5pp, 67% if gap is <= 10pp, 33% if gap is <= 20pp, 0% if quantum is more than 20pp worse than the best baseline (or either baseline is missing).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q01-result-h2-quant", + "requirements": "Quantitative test of H2. Is max(encoding 5-seed mean) - min(encoding 5-seed mean) across the 3 quantum encodings at least 5 absolute percentage points? 100% if spread >= 5pp, 67% if spread >= 2pp, 33% if spread > 0, 0% if all three encodings produce identical means (suggests training did not actually depend on the encoding choice).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q01-result-h3-quant", + "requirements": "Quantitative test of H3. Is zz_feature_map test_accuracy (5-seed mean) at least as high as angle_encoding test_accuracy (within 2 absolute pp lower, or higher)? 100% if zz >= angle - 2pp, 67% if zz is 2-5pp below angle, 33% if zz is 5-10pp below, 0% if zz is more than 10pp below angle.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q01-result-writeup", + "requirements": "Writeup of at least 200 words (in submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1, H2, H3 backed by specific mean accuracies (per encoding plus baseline) and the gap / spread values. Identifies a dominant systematic uncertainty (seed variance, optimizer convergence on the simpler EfficientSU2 reps=1 ansatz, class_sep=0.4 difficulty level). Discusses whether the result would change at higher difficulty or with a deeper ansatz.", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/quantum/rubrics/Q02.json b/tasks/quantum/rubrics/Q02.json new file mode 100644 index 0000000000000000000000000000000000000000..6a574da384c12eb6392b9ecefd719cd07f546a25 --- /dev/null +++ b/tasks/quantum/rubrics/Q02.json @@ -0,0 +1,109 @@ +{ + "id": "q02-root", + "requirements": "A controlled ablation study of entangling gates in a variational quantum classifier. Holding encoding (angle), ansatz template (EfficientSU2 reps=2), optimizer (COBYLA), and datasets fixed (matching Q01), the agent must implement full / half / no entanglement variants by selectively replacing CNOTs with identity in the ansatz, plus a parameter-matched classical MLP baseline. Score H1 (full > no by >=5pp), H2 (half between full and no), H3 (classical >= no on at least 1 dataset) with numerical evidence.", + "judging_note": "Ablation studies are scored on (i) whether the CNOT-removal mechanism actually changes the circuit as described (verified by inspecting cnot_count per condition), (ii) whether every other variable is held fixed (same encoding, same rotation parameters, same datasets, same optimizer, same seeds), and (iii) whether the H1/H2/H3 verdicts are backed by numerical evidence. The classical baseline must be parameter-matched (within 10 percent of the VQC's trainable parameter count) to be a meaningful sanity check.", + "weight": 1, + "sub_tasks": [ + { + "id": "q02-code", + "requirements": "Code-development bucket: the CNOT ablation mechanism is correctly implemented, the classical baseline is parameter-matched, and the evaluation loop holds all non-ablated variables fixed.", + "weight": 2, + "sub_tasks": [ + { + "id": "q02-code-ablation-mechanism", + "requirements": "All three quantum conditions (full_entanglement, half_entanglement, no_entanglement) use the same ZFeatureMap and the same EfficientSU2(reps=3) rotation parameters. CNOTs are removed by replacing them with identity (not by re-parameterizing the ansatz). The resulting circuits have cnot_count = 15 for full, 0 for no, and a random Bernoulli(0.5) draw over 15 positions for half (mean ~7.5, verified by recording the realized count per cell). The half_entanglement mask is generated via np.random.RandomState(seed + 10000).binomial(1, 0.5, size=15) to be reproducible.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "q02-code-classical-baseline", + "requirements": "classical_baseline uses sklearn MLPClassifier with a hidden layer size chosen so the total parameter count is within 10 percent of the VQC's 48 trainable parameters (e.g. hidden_layer_sizes=(6,) gives 49 params for 6-dim input). max_iter, activation, solver, and random_state are specified explicitly. The MLP is trained and evaluated on the SAME train/test splits as the VQC conditions.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "q02-code-pipeline", + "requirements": "Nested loop over 4 conditions x 3 datasets x at least 5 seeds. Each cell records test_accuracy, convergence_iterations, and cnot_count. The same seed produces the same train/test split across the 4 conditions on each dataset (so the 4 conditions can be paired-compared).", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q02-exec", + "requirements": "Execution-validity bucket: all cells ran and produced numerically valid outputs.", + "weight": 2, + "sub_tasks": [ + { + "id": "q02-exec-cells-ran", + "requirements": "At least 60 cells out of 60 expected (4 conditions x 3 datasets x 5 seeds) completed and produced an accuracy value. Missing cells must be documented; missing more than 6 cells (10 percent) without justification fails this leaf.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q02-exec-numerical", + "requirements": "Numerical validity: no NaN or Inf in test_accuracy. Every test_accuracy in [0, 1]. cnot_count matches the manifest expectation per condition (full=15, half=Bernoulli draw mean ~7.5 with all values in [0,15], no=0, classical_baseline=0). At least one cell of each condition produces a non-degenerate classifier (not predicting a single class for all test samples).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q02-results", + "requirements": "Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "q02-result-h1-quant", + "requirements": "Quantitative test of H1. Does full_entanglement beat no_entanglement by at least 5 absolute percentage points (5-seed mean) on at least 2 of 3 datasets? 100% if cleanly met, 67% if gap >= 2pp on 2/3, 33% if full > no on 2/3 but gap <2pp, 0% if full does not consistently beat no.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q02-result-h2-quant", + "requirements": "Quantitative test of H2. Does half_entanglement test_accuracy lie strictly between full and no (within 1pp seed-mean noise) on at least 2 of 3 datasets? 100% if cleanly monotonic on 2/3, 67% if monotonic on 1/3, 33% if half is non-monotonic but within 2pp of one of the endpoints, 0% otherwise.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q02-result-h3-quant", + "requirements": "Quantitative test of H3. On at least 1 of 3 datasets, does classical_baseline achieve test_accuracy at least as high as no_entanglement (5-seed mean)? 100% if classical >= no on at least 1/3, 50% if classical is within 2pp of no on at least 1/3, 0% if classical never reaches no_entanglement minus 2pp.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q02-result-writeup", + "requirements": "Writeup of at least 200 words (in submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1, H2, H3 with numerical evidence (specific mean accuracies for each condition x dataset, and the gap), a discussion of monotonicity, and an honest acknowledgment of whether the classical_baseline sanity check passes. The writeup is encouraged but not required to cross-reference Q01 (encoding) findings if available.", + "weight": 14.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/quantum/rubrics/Q03.json b/tasks/quantum/rubrics/Q03.json new file mode 100644 index 0000000000000000000000000000000000000000..cbcf3ebdb9f34ea6ca9bfa34fc3404df21aaedb1 --- /dev/null +++ b/tasks/quantum/rubrics/Q03.json @@ -0,0 +1,109 @@ +{ + "id": "q03-root", + "requirements": "A credible VQE optimizer comparison on H2 in STO-3G basis at two bond lengths (0.74 A equilibrium, 1.5 A stretched), at a fixed shot budget of 1024 shots per energy evaluation. The agent must (a) construct the 4-qubit H2 Hamiltonian at each geometry via qiskit_nature.PySCFDriver, (b) implement VQE via qiskit_algorithms.VQE.compute_minimum_eigenvalue() with a hardware-efficient EfficientSU2 reps=2 ansatz, (c) run 4 optimizers (SPSA, COBYLA, L-BFGS-B, ADAM) x 2 geometries x 3 seeds = 24 cells, (d) log cumulative shots and energy trajectories per cell, and (e) score H1/H2/H3 with numerical evidence keyed by (optimizer, geometry).", + "judging_note": "Optimizer comparisons are scored on (i) correctness of the Hamiltonian (FCI reference within 1 mHa of -1.137 Ha for H2 equilibrium), (ii) consistent ansatz / initial parameters across cells so only the optimizer and shot budget vary, (iii) accurate accounting of cumulative shots including gradient evaluations for L-BFGS-B and ADAM, and (iv) numerical evidence backing H1/H2/H3. qiskit_nature.second_q.drivers.PySCFDriver MUST be used to build the Hamiltonian. Hardcoded Pauli strings are NOT acceptable and a submission that uses them fails q03-code-hamiltonians.", + "weight": 1, + "sub_tasks": [ + { + "id": "q03-code", + "requirements": "Code-development bucket: VQE pipeline correctly implements 4 optimizers, both H2 geometries, and accurate cumulative-shot tracking.", + "weight": 2, + "sub_tasks": [ + { + "id": "q03-code-vqe", + "requirements": "VQE is implemented using qiskit_algorithms.VQE or an equivalent custom loop. The ansatz is EfficientSU2(num_qubits=4, reps=2, entanglement='linear'). Initial parameters are sampled from N(0, 0.1) with the same seed across all 4 optimizers for a given (shot_budget, geometry, seed) triple. All 4 optimizers are correctly instantiated from qiskit_algorithms.optimizers (SPSA, COBYLA, L_BFGS_B, ADAM) with the maxiter and learning rates listed in the manifest.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "q03-code-hamiltonians", + "requirements": "Both H2 Hamiltonians are constructed at the correct bond lengths (0.74 and 1.5 angstrom) in STO-3G basis using qiskit_nature.second_q.drivers.PySCFDriver (REQUIRED) and either ParityMapper with 2-qubit reduction or JordanWignerMapper with tapering. The diagonalized Hamiltonian (numpy eigvalsh of the matrix form) gives FCI energies within 1 mHa of -1.137 Ha (equilibrium) and -1.001 Ha (stretched). Hardcoded Pauli strings as a substitute for PySCFDriver are NOT accepted and fail this leaf.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "q03-code-shot-tracking", + "requirements": "Cumulative shots is tracked correctly per run. For SPSA and COBYLA, each iteration adds shots_per_eval * num_evaluations_per_iter (2 for SPSA, 1 for COBYLA). For L-BFGS-B and ADAM with finite-difference gradients, each iteration adds shots_per_eval * (2 * num_params + 1) so the gradient evaluations are accounted for. The shots_to_chemical_accuracy metric is computed using the running mean energy (over the last 5 evaluations) and reports the cumulative shot count at the first evaluation where the mean stays within 1.6 mHa of E_FCI for 5 consecutive evaluations.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q03-exec", + "requirements": "Execution-validity bucket: all cells ran with valid energies and shot accounting.", + "weight": 2, + "sub_tasks": [ + { + "id": "q03-exec-cells-ran", + "requirements": "At least 22 cells out of 24 expected (4 optimizers x 1 shot budget (1024) x 2 geometries x 3 seeds = 24) completed and produced a final_energy_error_hartree value. If any cells are omitted (e.g. ADAM ran out of budget), they must be documented with a recorded cause. Missing more than 2 cells (10 percent) without justification fails this leaf. The condition_summaries MUST be keyed by (optimizer, geometry) so each of the 8 condition cells has 3 seed entries — collapsing across the geometry axis (e.g. reporting only per-optimizer aggregates) fails this leaf because it makes H1 and H3 unevaluable.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q03-exec-numerical", + "requirements": "Numerical validity: every final energy is finite (no NaN/Inf), every final_energy_error_hartree >= 0, every success_rate in [0, 1], cumulative shots is monotone increasing within a run. At least one cell across the entire sweep reaches chemical accuracy (otherwise the rubric thresholds are uncalibrated for this setup).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q03-results", + "requirements": "Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "q03-result-h1-quant", + "requirements": "Quantitative test of H1. On at least 1 of 2 geometries at 1024 shots/eval, does SPSA reach chemical accuracy with fewer median cumulative shots than L-BFGS-B (over 3 seeds)? 100% if SPSA wins on at least 1/2 geometries with a >=20 percent shot reduction, 67% if SPSA wins on at least 1/2 with any margin, 33% if SPSA and L-BFGS-B are within 10 percent of each other, 0% if L-BFGS-B is consistently faster.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q03-result-h2-quant", + "requirements": "Quantitative test of H2. At 1024 shots/eval, pooling across both geometries: do gradient-free methods (SPSA + COBYLA, total of 6 seed-runs = 2 methods x 3 seeds) have a combined chemical-accuracy success rate of at least 4/6, while gradient-based methods (L-BFGS-B + ADAM, 6 seed-runs) have at most 3/6? 100% if cleanly met, 67% if the gradient-free success rate exceeds gradient-based by at least 30 percent, 33% if gradient-free exceeds gradient-based at all, 0% if gradient-based methods are equal or better.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q03-result-h3-quant", + "requirements": "Quantitative test of H3. Does the optimizer ranking by median shots_to_chemical_accuracy change between H2 equilibrium and H2 stretched geometries? Specifically: take the 4-element ranking of optimizers (by 3-seed-median shots) on each geometry; H3 is supported if at least one pair swaps relative order between the two rankings (Kendall tau distance >= 1 between the two rankings). 100% if at least one swap detected with clear evidence, 67% if one optimizer's rank differs by exactly 1 position, 33% if rankings are similar but values differ, 0% if rankings are identical or H3 is unevaluable (e.g. due to collapsed condition_summaries that drop the geometry axis).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q03-result-writeup", + "requirements": "Writeup of at least 200 words (in submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1, H2, H3 with numerical evidence (specific median cumulative shots, success rates, and final energy errors per optimizer x shot budget). Identifies a dominant systematic uncertainty (seed variance, finite-difference epsilon, optimizer hyperparameter sensitivity, shot-noise variance at low budgets). Discusses the difference between equilibrium and stretched H2 in terms of optimizer ranking.", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/quantum/rubrics/Q04.json b/tasks/quantum/rubrics/Q04.json new file mode 100644 index 0000000000000000000000000000000000000000..c344d7c73f31ae9934d937139237ce875cb33bbb --- /dev/null +++ b/tasks/quantum/rubrics/Q04.json @@ -0,0 +1,109 @@ +{ + "id": "q04-root", + "requirements": "A credible empirical study of data re-uploading depth vs Fourier expressivity in variational quantum regression. The agent must (a) implement 4 re-uploading circuits at L in {1, 3, 5, 7} using qiskit ParameterVector + RY/RZ rotation gates, (b) train each with parameter-shift Adam on 200 samples of two 1D regression targets (sinusoid mixture and step function), (c) compare against two classical baselines (RBF kernel ridge regression, polynomial regression degree 7) on the same data, (d) measure test MSE per cell and the recovered Fourier spectrum of the trained quantum model, and (e) score H1/H2/H3 with numerical evidence.", + "judging_note": "Re-uploading topics are scored on (i) whether the 4 quantum circuits actually differ by depth (sanity check: parameter counts must be 2/6/10/14), (ii) whether training was real and produced different outputs across L levels (3 quantum cells producing bit-identical MSE would indicate dispatch failure, same anti-pattern as Q01 ablation failure), (iii) whether classical baselines are computed on the exact same data as quantum cells, and (iv) whether H1/H2/H3 are supported by reported MSE and Fourier-spectrum numbers. Quantitative result leaves use a graded scale: 100% if hypothesis threshold cleanly met, 67% if trend in predicted direction without clearing threshold, 33% if ambiguous, 0% if contradicted or evidence missing.", + "weight": 1, + "sub_tasks": [ + { + "id": "q04-code", + "requirements": "Code-development bucket: 4 re-uploading circuits + 2 classical baselines are implemented correctly with shared training pipeline and reproducible per-cell seed control.", + "weight": 2, + "sub_tasks": [ + { + "id": "q04-code-circuits", + "requirements": "All four re-uploading circuits use qiskit.QuantumCircuit + qiskit.circuit.ParameterVector. The encoding gate is RZ(x) and the trainable block is RY(theta_2k) RZ(theta_2k+1). Output is the expectation on the single qubit (via qiskit.quantum_info.Statevector or the reference Estimator primitive). Parameter counts match: L=1 has 2 params, L=3 has 6, L=5 has 10, L=7 has 14. A sanity check asserts that the four circuits produce different output values for a fixed nonzero input x=0.3 with a fixed random parameter vector.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "q04-code-training-pipeline", + "requirements": "Training uses Adam with parameter-shift gradients computed via the qiskit parameter-shift rule (NOT scipy finite differences). Loss is mean squared error on the 200 training samples. Same optimizer hyperparameters across all four L levels (Adam lr=0.05, 300 steps). Both classical baselines use sklearn (KernelRidge with grid-searched gamma, and a Pipeline of PolynomialFeatures+Ridge).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "q04-code-metric-logging", + "requirements": "Each per-(condition, dataset, seed) result is emitted to stdout as a single line starting with METRIC_RESULT followed by JSON containing condition, dataset, seed, test_mse, train_mse, and fourier_spectrum_cosine. The FFT is computed on the trained model's output sampled on a uniform 256-point grid over [0, 1].", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q04-exec", + "requirements": "Execution-validity bucket: all 24 cells ran and produced numerically valid outputs.", + "weight": 2, + "sub_tasks": [ + { + "id": "q04-exec-cells-ran", + "requirements": "At least 22 cells out of 24 expected (6 conditions x 2 datasets x 2 seeds) completed without unhandled errors and produced a test_mse value. Missing more than 2 cells (10 percent) without documented justification fails this leaf.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q04-exec-numerical", + "requirements": "Numerical validity: no NaN or Inf in test_mse; train_mse <= test_mse * 1.5 across conditions (otherwise model is broken); the 4 quantum L levels produce different test MSEs (max - min across the 4 quantum conditions on the sinusoid-mixture dataset is at least 0.005). Fourier spectrum values are real, finite, and the cosine similarity is in [-1, 1].", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q04-results", + "requirements": "Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "q04-result-h1-quant", + "requirements": "Quantitative test of H1. On the sinusoid-mixture dataset, is test_mse(L=5) <= test_mse(L=1) / 4 (2-seed mean)? 100% if ratio >= 4, 67% if 2 <= ratio < 4, 33% if 1 < ratio < 2, 0% if L=5 is not better than L=1.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q04-result-h2-quant", + "requirements": "Quantitative test of H2. On the step function dataset, is test_mse(L=7) >= test_mse(L=5) * 1.15 (2-seed mean)? 100% if overshoot is >= 15 percent, 67% if 5-15 percent, 33% if any small overshoot (0-5 percent), 0% if L=7 still has lower MSE.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q04-result-h3-quant", + "requirements": "Quantitative test of H3. On the sinusoid-mixture dataset at L=7, does the recovered Fourier spectrum cosine similarity (restricted to lowest 5 nonzero frequencies) reach 0.7? 100% if >= 0.7, 67% if >= 0.5, 33% if >= 0.3, 0% otherwise.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "q04-result-writeup", + "requirements": "Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1, H2, H3 backed by specific test MSE values per (L, dataset) and Fourier spectrum cosine values. Discusses how the Fourier-series view (Schuld, Sweke, Meyer 2021) predicts the observed trend, and identifies a dominant systematic uncertainty (seed variance, optimizer convergence, parameter-shift gradient noise).", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/quantum/rubrics/Q05.json b/tasks/quantum/rubrics/Q05.json new file mode 100644 index 0000000000000000000000000000000000000000..8cddaaef79a2723d186c615159090ad5cbd968ed --- /dev/null +++ b/tasks/quantum/rubrics/Q05.json @@ -0,0 +1,109 @@ +{ + "id": "q05-root", + "requirements": "An empirical study of barren plateau onset on a 6-qubit EfficientSU2 ansatz under local vs global cost functions and shallow vs deep ansatz. The agent must (a) build the EfficientSU2 ansatz with reps in {2, 10, 20} via qiskit.circuit.library, (b) for each (cost, depth) condition sample 100 random parameter vectors and compute parameter-shift gradient dL/dtheta_0, (c) report the empirical variance of those 100 gradient values per cell, (d) run a 100-step Adam training as a practical sanity probe of trainability, and (e) score H1/H2/H3 with numerical evidence.", + "judging_note": "Barren plateau studies are scored on (i) correctness of the EfficientSU2 / cost construction (using qiskit.circuit.library, not hand-rolled), (ii) whether the 100-sample gradient distribution actually captures the variance (cells reporting std=0 across 100 samples indicate a broken sampler), (iii) the local_cost_L10 vs global_cost_L10 contrast being clean (these have IDENTICAL ansatz, only cost differs, so any large variance gap directly tests Cerezo 2021), and (iv) the training-feasibility probe correlating with the gradient-variance prediction (BP cell should not train; trainable cell should train). Quantitative result leaves use a graded scale: 100% if cleanly met, 67% if in predicted direction without clearing threshold, 33% if ambiguous, 0% if contradicted.", + "weight": 1, + "sub_tasks": [ + { + "id": "q05-code", + "requirements": "Code-development bucket: EfficientSU2 ansatz built correctly via qiskit.circuit.library, parameter-shift gradient implemented correctly, and the 100-sample gradient distribution actually populated per cell.", + "weight": 2, + "sub_tasks": [ + { + "id": "q05-code-ansatz-and-gradient", + "requirements": "EfficientSU2(num_qubits=6, reps=reps_value, entanglement='linear') from qiskit.circuit.library used directly (NOT hand-coded H/CNOT/RY stacks). Parameter-shift gradient of (theta) with respect to theta_0 computed via the qiskit standard pattern: dL/dtheta_0 = (L(theta + (pi/2)*e_0) - L(theta - (pi/2)*e_0)) / 2. Cost observable for local_cost is Pauli Z on qubit 0; for global_cost is the product Z_0 Z_1 Z_2 Z_3 Z_4 Z_5 (a SparsePauliOp). A sanity check confirms that the gradient is nonzero for at least one random parameter sample at L=2 local cost.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "q05-code-sampling", + "requirements": "For each (condition, ansatz-structure seed) cell, 100 independent random parameter vectors are drawn from Uniform[-pi, pi]^{n_params} using a per-cell seeded RNG. For each parameter vector, the gradient dL/dtheta_0 is computed. The 100 gradient values are stored and the empirical variance is reported. Reusing the same parameter vector across conditions in the same cell is allowed (paired comparison) but NOT required.", + "weight": 7.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "q05-code-training-probe", + "requirements": "After the gradient-variance measurement, each cell runs a 100-step Adam optimization (qiskit_algorithms.optimizers.ADAM or pytorch Adam wrapping a parameter-shift gradient closure) starting from a random initialization. The initial loss and the loss after step 100 are recorded; the training_loss_change_fraction = (L_initial - L_final) / |L_initial| is computed per cell.", + "weight": 7.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q05-exec", + "requirements": "Execution-validity bucket: all 12 cells ran and produced numerically valid outputs.", + "weight": 2, + "sub_tasks": [ + { + "id": "q05-exec-cells-ran", + "requirements": "At least 11 cells out of 12 expected (4 conditions x 3 ansatz-structure seeds) completed and produced a non-null log_gradient_variance value. Missing more than 1 cell without documented justification fails this leaf.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q05-exec-numerical", + "requirements": "Numerical validity: no NaN or Inf in gradient values; empirical std of the 100 gradients is strictly > 0 in every cell (cells reporting std=0 indicate a broken sampler and fail this leaf); log10(variance) values are finite. The training_loss_change_fraction is in the range [-1.0, 1.0] (otherwise loss diverged or wasn't properly normalized).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q05-results", + "requirements": "Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "q05-result-h1-quant", + "requirements": "Quantitative test of H1. Is var(local_cost_L10) / var(global_cost_L10) >= 20 (3-seed mean of variances)? 100% if ratio >= 20, 67% if 5 <= ratio < 20, 33% if 1 < ratio < 5, 0% if global cost has equal or larger variance.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q05-result-h2-quant", + "requirements": "Quantitative test of H2. Is log10(var_local_L20) - log10(var_local_L2) > -2.0 (3-seed mean)? 100% if difference > -2.0 (polynomial decay), 67% if difference > -3.0, 33% if difference > -4.0, 0% otherwise (exponential decay observed).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q05-result-h3-quant", + "requirements": "Quantitative test of H3. Is global_cost_L10 training_loss_change_fraction <= 0.01 AND local_cost_L10 training_loss_change_fraction >= 0.10 (3-seed mean)? 100% if both conditions hold cleanly, 67% if local trains but global change is 1-5 percent, 33% if local trains but global has any change, 0% if global cost also trains (would contradict the BP prediction).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q05-result-writeup", + "requirements": "Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by specific variance values per (condition, seed) and training_loss_change_fraction values. Explicitly references Cerezo et al. 2021 'Cost function dependent barren plateaus' and discusses whether the observed variance ratio matches their theoretical prediction. Identifies dominant uncertainty (ansatz-structure seed variance, finite-sample variance estimate from 100 gradient samples).", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/quantum/rubrics/Q06.json b/tasks/quantum/rubrics/Q06.json new file mode 100644 index 0000000000000000000000000000000000000000..aa4ce1af5227493ee63b96384ef55080be79be3d --- /dev/null +++ b/tasks/quantum/rubrics/Q06.json @@ -0,0 +1,109 @@ +{ + "id": "q06-root", + "requirements": "An empirical study of neural-network warm-start for QAOA-MaxCut at p=1. The agent must (a) precompute 200 Erdős-Rényi G(n=6, p=0.5) training graphs with their optimal (beta, gamma) via grid search to label the MLP training set, (b) train sklearn.MLPRegressor to predict (beta, gamma) from graph features, (c) at test time, predict (beta, gamma) and run COBYLA QAOA optimization starting from that init for up to 20 iterations, (d) compare against random and fixed (pi/4, pi/4) initialization baselines on both in-distribution (ER G(n=6, p=0.5)) and out-of-distribution (3-regular n=8) test graphs, and (e) score H1/H2/H3 with numerical evidence.", + "judging_note": "QAOA warm-start studies are scored on (i) whether the MLP is actually trained offline (cells reporting identical MLP predictions across different graph instances indicate the model is broken or untrained), (ii) whether the 4 initialization strategies actually produce different starting (beta, gamma) points (must be verified by sanity check), (iii) whether COBYLA is run from each init for exactly 20 iterations and the approximation_ratio trajectory is recorded, and (iv) whether H1/H2/H3 are supported by 3-graph-median numerical evidence keyed by (condition, graph_family). Quantitative result leaves use a graded scale.", + "weight": 1, + "sub_tasks": [ + { + "id": "q06-code", + "requirements": "Code-development bucket: QAOA p=1 circuit built via qiskit.circuit.library or hand-coded RX/RZ + CNOT correctly, MLP trained offline on graph features, and 4 initialization conditions actually produce different starting points.", + "weight": 2, + "sub_tasks": [ + { + "id": "q06-code-qaoa-circuit", + "requirements": "QAOA p=1 circuit for MaxCut implemented correctly: H^{otimes n} initial state, then U_C(gamma) = product over edges of exp(-i * gamma * Z_i Z_j), then U_B(beta) = product over qubits of exp(-i * beta * X_i). Run on qiskit AerSimulator statevector backend; observable is the cut Hamiltonian sum_{(i,j) in E} (1 - Z_i Z_j) / 2. Optimization via qiskit_algorithms.optimizers.COBYLA(maxiter=20).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "q06-code-mlp-training", + "requirements": "Offline phase: generate 200 random ER G(n=6, p=0.5) graphs with a different RNG than the test set. For each, compute the optimal (beta_optimal, gamma_optimal) via a 20x20 grid search on (beta, gamma) in [0, pi/2]^2 maximizing the QAOA p=1 expected cut size. Extract 4 graph features (degree_mean, degree_var, edge_density, n_edges) and fit sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(32, 16), max_iter=2000) to map features -> (beta_optimal, gamma_optimal). The trained MLP is reused for both in-distribution and out-of-distribution test conditions.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "q06-code-init-conditions", + "requirements": "The 4 initialization strategies produce different starting (beta, gamma) per test graph. Sanity check: for 3 test graphs, log the initial (beta, gamma) for each of the 4 conditions and verify they are not all equal. Each condition's COBYLA optimization is run for exactly 20 iterations, and the cut size at each iteration is recorded so the iterations_to_target_ratio can be computed by replaying the trajectory.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q06-exec", + "requirements": "Execution-validity bucket: all cells ran and produced numerically valid outputs.", + "weight": 2, + "sub_tasks": [ + { + "id": "q06-exec-cells-ran", + "requirements": "At least 22 cells out of 24 expected (4 conditions x 2 graph families x 3 seeds) completed and produced an iterations_to_target_ratio value (clamped to 20 if not reached). Missing more than 2 cells (10 percent) without documented justification fails this leaf.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q06-exec-numerical", + "requirements": "Numerical validity: iterations_to_target_ratio is in [1, 20] (integer-valued, since COBYLA budget is 20); final_approximation_ratio is in [0, 1]; success_rate is in [0, 1]. At least one cell across the entire 24-cell sweep reaches AR >= 0.9 within 20 iterations (otherwise threshold is uncalibrated).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q06-results", + "requirements": "Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "q06-result-h1-quant", + "requirements": "Quantitative test of H1. On in-distribution ER G(n=6, p=0.5) graphs, does mlp_init_in_distribution reach AR>=0.9 in <= 10 COBYLA iterations on 3-graph mean, while random_init requires >= 18? 100% if MLP <= 10 AND random >= 18, 67% if MLP at least 30 percent better than random, 33% if MLP marginally better than random, 0% if random equals or beats MLP.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q06-result-h2-quant", + "requirements": "Quantitative test of H2. On out-of-distribution 3-regular n=8 graphs, does mlp_init_out_of_distribution beat random_init by at least 20 percent in 3-graph-median iterations_to_target_ratio? 100% if mlp gain >= 20 percent, 67% if mlp gain 10-20 percent, 33% if mlp marginally faster, 0% if no transfer (random matches or beats MLP).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q06-result-h3-quant", + "requirements": "Quantitative test of H3. On in-distribution graphs, is the 3-graph-median iteration-count gap between fixed_init_pi_over_4 and mlp_init_in_distribution <= 30 percent (i.e. fixed init is competitive with MLP at p=1, consistent with Brandao 2018 concentration)? 100% if gap <= 30 percent, 67% if 30-60 percent, 33% if 60-100 percent, 0% if MLP is more than 2x better than fixed init (would refute the concentration interpretation).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q06-result-writeup", + "requirements": "Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by per-(condition, graph_family) median iteration counts and approximation ratios. References Brandao et al. 2018 concentration result and Jain et al. Quantum 2022 GNN-init paper. Discusses whether the observed out-of-distribution transfer rate is high enough that MLP-init is worth the offline training cost compared to fixed init.", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/quantum/rubrics/Q07.json b/tasks/quantum/rubrics/Q07.json new file mode 100644 index 0000000000000000000000000000000000000000..b33f3f1e8af241ea853cb1fffc0da00ea847eecc --- /dev/null +++ b/tasks/quantum/rubrics/Q07.json @@ -0,0 +1,109 @@ +{ + "id": "q07-root", + "requirements": "An empirical study of Matrix Product State (MPS) classifiers vs neural network baselines at matched parameter count on 8x8 downsampled image classification. The agent must (a) build an MPS classifier following Stoudenmire and Schwab 2016 with cos/sin per-pixel feature embedding and bond dimensions chi in {4, 8, 16}, (b) train each MPS via Adam for 50 epochs on 5000 training images, (c) compare against sklearn LogisticRegression and a small PyTorch CNN whose parameter count is within 10 percent of the chi=8 MPS, (d) evaluate test accuracy on a 1000-image held-out set, and (e) score H1/H2/H3 with numerical evidence.", + "judging_note": "MPS classifier studies are scored on (i) correctness of the MPS contraction (the per-pixel features cos(pi*x/2), sin(pi*x/2) must enter as a rank-2 tensor at each site, NOT collapsed to a single scalar), (ii) the parameter-count parity between chi=8 MPS and small_cnn_matched within 10 percent (deviation > 10 percent fails q11-code-cnn-parity), (iii) the same train/test split being used across all conditions for a given seed, and (iv) accuracy values being in [0, 1] with non-degenerate predictions. Quantitative result leaves use a graded scale.", + "weight": 1, + "sub_tasks": [ + { + "id": "q07-code", + "requirements": "Code-development bucket: MPS classifier implemented correctly with proper per-pixel feature embedding, CNN baseline parameter-matched, and training pipeline shared across conditions.", + "weight": 2, + "sub_tasks": [ + { + "id": "q07-code-mps", + "requirements": "MPS classifier uses pure NumPy or PyTorch tensor contractions. Per-pixel feature embedding is phi(x) = [cos(pi*x/2), sin(pi*x/2)] for grayscale x in [0,1] (Stoudenmire and Schwab 2016 standard). The MPS is a chain of 64 tensors of shape (chi, 2, chi) (interior sites) plus 2 boundary tensors with one chi leg removed, with one site carrying an additional class index of size 10. Trained with Adam (lr=0.01, 50 epochs, batch_size=64). A sanity check confirms that MPS at chi=4 has approximately 64 * 2 * 16 = 2048 trainable parameters within 20 percent.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "q07-code-cnn-parity", + "requirements": "small_cnn_matched is implemented in PyTorch as Conv2d(1, K, kernel_size=3, padding=1) + ReLU + Flatten + Linear(K*8*8, 10) with K chosen so total parameter count is within 10 percent of the chi=8 MPS parameter count. K is calculated explicitly and the parameter counts are logged for both. Trained with Adam (same hyperparams as MPS) for 50 epochs.", + "weight": 7.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "q07-code-pipeline", + "requirements": "Nested loop over 5 conditions x 2 datasets x 3 seeds = 30 cells. Each cell uses the same train/test split given a fixed seed (i.e. the per-seed split is computed once and shared across the 5 conditions). MNIST is downsampled to 8x8 via skimage.transform.resize. Per-cell results are logged to stdout as METRIC_RESULT JSON lines containing condition, dataset, seed, test_accuracy, trainable_parameter_count, training_time_sec.", + "weight": 7.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q07-exec", + "requirements": "Execution-validity bucket: all 30 cells ran with valid outputs.", + "weight": 2, + "sub_tasks": [ + { + "id": "q07-exec-cells-ran", + "requirements": "At least 27 cells out of 30 expected (5 conditions x 2 datasets x 3 seeds) completed without unhandled errors and produced a test_accuracy value. Missing more than 3 cells (10 percent) without documented justification fails this leaf.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q07-exec-numerical", + "requirements": "Numerical validity: test_accuracy values are in [0, 1], not equal to 1/n_classes (would mean random predictions, model didn't train), and the 3 MPS chi levels produce different mean test_accuracy on at least one dataset (max - min across chi=4/8/16 mean accuracy on MNIST is at least 0.01). trainable_parameter_count is reported per cell and matches the expected formula within 1 percent.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q07-results", + "requirements": "Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "q07-result-h1-quant", + "requirements": "Quantitative test of H1. Does the MPS at chi=16 reach test_accuracy >= 92 percent on 8x8 downsampled MNIST (3-seed mean)? 100% if >= 92 percent, 67% if >= 85 percent, 33% if >= 75 percent, 0% otherwise.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q07-result-h2-quant", + "requirements": "Quantitative test of H2. On 8x8 Fashion-MNIST, does the parameter-matched CNN beat chi=16 MPS by at least 5 absolute percentage points (3-seed mean)? 100% if CNN gap >= 5pp, 67% if CNN gap 2-5pp, 33% if CNN gap 0-2pp, 0% if MPS matches or beats CNN.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q07-result-h3-quant", + "requirements": "Quantitative test of H3. On 8x8 MNIST, is the chi=4 to chi=8 accuracy gain greater than the chi=8 to chi=16 accuracy gain (3-seed mean)? Specifically: (acc_chi8 - acc_chi4) > (acc_chi16 - acc_chi8). 100% if cleanly satisfied with difference of differences >= 1pp, 67% if log-linear trend holds with smaller gap, 33% if marginally so, 0% if linear or super-linear scaling observed.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q07-result-writeup", + "requirements": "Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by per-(condition, dataset) mean test accuracies and the trainable_parameter_count parity check between chi=8 MPS and small_cnn_matched. References Stoudenmire and Schwab NeurIPS 2016 and discusses whether the observed MNIST vs Fashion-MNIST gap is consistent with the CNN's stronger spatial-bias inductive prior.", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/quantum/rubrics/Q08.json b/tasks/quantum/rubrics/Q08.json new file mode 100644 index 0000000000000000000000000000000000000000..fbd983800ed8a9738ebb66f684c49767313f057c --- /dev/null +++ b/tasks/quantum/rubrics/Q08.json @@ -0,0 +1,109 @@ +{ + "id": "q08-root", + "requirements": "An empirical study of layerwise vs end-to-end training of a 4-qubit reps=5 EfficientSU2 VQC on UCI binary classification (iris-binary and breast-cancer). The agent must (a) implement the layerwise training schedule manually (train reps=1, freeze, add reps=1, train, ...), (b) implement the end-to-end baseline that trains all 5 reps together, (c) measure mean gradient norm during the first 10 optimization steps of each schedule to test the Skolik 2021 mechanism, and (d) compare against logistic regression + parameter-matched MLP baselines. Score H1/H2/H3 with numerical evidence.", + "judging_note": "Layerwise studies are scored on (i) correctness of the freeze-and-add mechanism (verifiable: the layerwise condition should have N optimizer.minimize() calls each operating on a subset of parameters, not 1 single call over all parameters), (ii) parameter counts matching across conditions (the layerwise and end-to-end VQCs must end with the same 40-parameter ansatz), (iii) the gradient-norm probe being measured at consistent points across conditions, (iv) the random-init control producing accuracy near 0.5 on both datasets (otherwise the ansatz is leaking labels through initialization).", + "weight": 1, + "sub_tasks": [ + { + "id": "q08-code", + "requirements": "Code-development bucket: layerwise freeze-and-add schedule implemented manually, classical baselines parameter-matched, gradient-norm probe consistent.", + "weight": 2, + "sub_tasks": [ + { + "id": "q08-code-layerwise-mechanism", + "requirements": "The layerwise condition trains the EfficientSU2(reps=5) ansatz by 5 sequential optimizer.minimize() calls. Each call trains only the parameters belonging to the newly added reps=1 block while the previous blocks have their parameters bound to their previously converged values via qiskit.QuantumCircuit.assign_parameters() on a partial dictionary. After all 5 calls the final fitted VQC has 40 trainable parameters, identical in count to the end-to-end VQC.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "q08-code-gradient-probe", + "requirements": "Mean gradient norm during the first 10 optimization steps is recorded per condition via the qiskit_machine_learning VQC callback or via a custom wrapper around the loss function that uses parameter-shift gradients. For layerwise, the metric is averaged across the first 10 steps of each newly added block (so total of 50 steps averaged, since 5 blocks x 10). For end-to-end, the metric is the first 10 steps of the single training run.", + "weight": 7.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q08-code-classical-baselines", + "requirements": "mlp_matched uses sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(8,)) for ~49 parameters, within 20 percent of the VQC's 40 parameters. logistic_regression uses sklearn.linear_model.LogisticRegression(max_iter=1000). Both train on the same 80/20 train/test split as the VQC conditions using the same per-seed random_state.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q08-exec", + "requirements": "Execution-validity bucket: all 30 cells ran and produced numerically valid outputs.", + "weight": 2, + "sub_tasks": [ + { + "id": "q08-exec-cells-ran", + "requirements": "At least 27 cells out of 30 expected (5 conditions x 2 datasets x 3 seeds) completed without unhandled errors and produced a test_accuracy value. Missing more than 3 cells (10 percent) without documented justification fails this leaf.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q08-exec-numerical", + "requirements": "Numerical validity: test_accuracy in [0, 1] with non-degenerate predictions (no condition collapses to a single-class predictor on all test samples). The random_init_control condition produces test_accuracy approximately 0.5 +/- 0.1 on both datasets (otherwise label leakage in the untrained ansatz). mean_gradient_norm_first10 is strictly positive across all cells.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q08-results", + "requirements": "Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "q08-result-h1-quant", + "requirements": "Quantitative test of H1. On at least 1 of 2 datasets, is layerwise test_accuracy >= end-to-end test_accuracy + 3 absolute pp (3-seed mean)? 100% if gap >= 3pp on at least 1 dataset, 67% if gap >= 1pp on at least 1 dataset, 33% if layerwise marginally faster on any dataset, 0% if end-to-end equals or beats layerwise on both datasets.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q08-result-h2-quant", + "requirements": "Quantitative test of H2. Is mean_gradient_norm_first10 of layerwise >= 5x mean_gradient_norm_first10 of end-to-end (3-seed mean, across both datasets combined)? 100% if ratio >= 5x, 67% if 2-5x, 33% if any positive ratio (layerwise > end-to-end), 0% otherwise.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q08-result-h3-quant", + "requirements": "Quantitative test of H3. Do both layerwise and end-to-end test_accuracy exceed random_init_control test_accuracy by >= 5 absolute pp on both datasets (3-seed mean)? 100% if both gaps >= 5pp on both datasets, 67% if both on 1 dataset, 33% if at least one VQC beats random anywhere, 0% if random matches or beats trained VQCs.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q08-result-writeup", + "requirements": "Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by specific 3-seed mean test_accuracy, mean_gradient_norm_first10, and iterations_to_target_accuracy values per (condition, dataset). References Skolik et al. Quantum 2021 and discusses whether the layerwise gradient-norm advantage matches their theoretical prediction.", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/quantum/rubrics/Q09.json b/tasks/quantum/rubrics/Q09.json new file mode 100644 index 0000000000000000000000000000000000000000..46a67cdfe456092c800af04aa5db90d3da2c7443 --- /dev/null +++ b/tasks/quantum/rubrics/Q09.json @@ -0,0 +1,109 @@ +{ + "id": "q09-root", + "requirements": "An empirical study of noise-aware VQC training. Four quantum training conditions vary the training-time depolarizing noise rate (ideal, p=0.001, p=0.005, p=0.01), evaluated on both an ideal test set and a noisy test set at p=0.01. The agent must (a) implement the noise model via qiskit_aer.NoiseModel applying single-qubit and two-qubit depolarizing channels, (b) train VQC under each noise level using qiskit_machine_learning.VQC with the appropriate sampler, (c) evaluate each trained model on BOTH ideal and noisy test sets to expose the robustness-vs-clean-accuracy tradeoff, and (d) score H1/H2/H3 with numerical evidence.", + "judging_note": "Noise-aware training studies are scored on (i) correctness of the qiskit_aer NoiseModel construction (depolarizing channel must be applied after every gate, not only after measurement; verifiable by inspecting the agent's noise model setup), (ii) test_accuracy reporting BOTH ideal and noisy variants per cell (without both, H1 cannot be evaluated), (iii) the noise-rate sweep producing visibly different test accuracies (cells reporting identical numbers across noise levels indicate the noise model is not actually being applied during training), and (iv) numerical evidence backing the three-way comparison: ideal-train vs noisy-mid-train vs noisy-high-train.", + "weight": 1, + "sub_tasks": [ + { + "id": "q09-code", + "requirements": "Code-development bucket: NoiseModel correctly constructed, training pipeline applies noise, evaluation on both ideal and noisy test.", + "weight": 2, + "sub_tasks": [ + { + "id": "q09-code-noise-model", + "requirements": "qiskit_aer.NoiseModel with depolarizing_error applied to all single-qubit gates (h, ry, rz, etc.) AND two-qubit gates (cx). For a noisy-train condition at rate p, the single-qubit error rate is p and the two-qubit error rate is p (or some agent-documented scaling of p). A sanity check verifies that the same ansatz parameters produce different expectation values under the noise model vs under StatevectorSampler (otherwise the noise is not being applied).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "q09-code-training-pipeline", + "requirements": "Each training condition uses qiskit_machine_learning.VQC with a qiskit.primitives.BackendSamplerV2 (or BackendSampler) bound to either AerSimulator (for noisy training) or StatevectorSampler (for ideal training). Same ansatz family (EfficientSU2 num_qubits=4 reps=2 entanglement='linear'), same optimizer (COBYLA maxiter=200), same per-seed random initialization across noise-rate conditions for paired comparison.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "q09-code-dual-eval", + "requirements": "Each trained VQC is evaluated on TWO test sets: (a) ideal StatevectorSampler (test_accuracy_ideal), (b) noisy AerSimulator at depolarizing p=0.01 (test_accuracy_noisy_p01). Both numbers are logged per cell. The primary 'test_accuracy' metric is the average of the two. Each cell's METRIC_RESULT JSON includes all three numbers.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q09-exec", + "requirements": "Execution-validity bucket: all 18 cells ran with valid outputs.", + "weight": 2, + "sub_tasks": [ + { + "id": "q09-exec-cells-ran", + "requirements": "At least 16 cells out of 18 expected (6 conditions x 1 dataset x 3 seeds) completed without unhandled errors and produced both test_accuracy_ideal AND test_accuracy_noisy_p01 values. Missing more than 2 cells (10 percent) without documented justification fails this leaf.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q09-exec-numerical", + "requirements": "Numerical validity: both test accuracies in [0, 1]; for any single training condition, test_accuracy_ideal and test_accuracy_noisy_p01 differ by at least 0.01 (cells reporting bit-identical clean and noisy accuracy mean the noise model isn't being applied at evaluation); no_training_control accuracy is in [0.4, 0.6] (random predictor floor).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q09-results", + "requirements": "Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "q09-result-h1-quant", + "requirements": "Quantitative test of H1. On the noisy test set at p=0.01, is train_noisy_mid (training noise p=0.005) test_accuracy >= train_ideal test_accuracy + 5 absolute pp (3-seed mean)? 100% if gap >= 5pp, 67% if 2-5pp, 33% if any positive gap, 0% if ideal train matches or beats noisy-mid train on noisy test.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q09-result-h2-quant", + "requirements": "Quantitative test of H2. On the ideal test set, does train_ideal beat EVERY noisy-trained variant by >= 2 absolute pp (3-seed mean)? 100% if gap >= 2pp vs all three noisy variants, 67% if gap >= 2pp vs at least 2 of 3, 33% if marginal advantage on at least 1, 0% if noisy training matches or beats ideal training on clean test (no tradeoff).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q09-result-h3-quant", + "requirements": "Quantitative test of H3. Does train_noisy_high underperform train_noisy_mid on BOTH the clean and the noisy test set (3-seed mean)? 100% if cleanly worse on both, 67% if worse on 1 of 2 by >= 3pp, 33% if marginally worse on 1, 0% if high-noise training matches or beats mid-noise on both test sets (no negative transfer observed).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q09-result-writeup", + "requirements": "Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by per-(training_noise_rate, test_set) 3-seed mean accuracy values. References Wang NeurIPS 2022 QuantumNAS and Sharma PRX Quantum 2022. Discusses whether the observed sweet-spot noise rate matches the test-deployment rate, and acknowledges that AerSimulator depolarizing channel is a simplified model relative to real hardware noise (T1/T2, crosstalk, readout asymmetry not captured).", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/quantum/rubrics/Q10.json b/tasks/quantum/rubrics/Q10.json new file mode 100644 index 0000000000000000000000000000000000000000..1522475e8a0b3369c82f8ca938b228441158ac95 --- /dev/null +++ b/tasks/quantum/rubrics/Q10.json @@ -0,0 +1,109 @@ +{ + "id": "q10-root", + "requirements": "An empirical study of a quantum autoencoder on Haar-random pure states. The agent must (a) construct encoder + decoder circuits as EfficientSU2(reps=3) from qiskit.circuit.library, (b) implement the SWAP-test loss via auxiliary qubits + Hadamard-controlled-SWAP-Hadamard or compute trash-qubit fidelity-to-|0> directly via Statevector, (c) train each compression-ratio condition by qiskit_algorithms.optimizers.COBYLA.minimize() on a fixed batch of 5 Haar states per seed, (d) evaluate on 20 unseen Haar states reporting reconstruction_fidelity, and (e) score H1/H2/H3 with numerical evidence.", + "judging_note": "Quantum autoencoder studies are scored on (i) correctness of the SWAP-test or fidelity computation (verifiable: no_compression baseline must report fidelity ~1.0, otherwise the eval pipeline is broken), (ii) the random_unitary_compression baseline producing fidelity in the expected range for that compression ratio (theoretical floor is 2^(m-n) per Page formula, e.g. for 4->2 the random baseline fidelity is around 0.0625), (iii) the trained conditions being measurably above the random baseline (otherwise no learning), and (iv) numerical evidence for the latent-dimension capacity effect (4->3 better than 4->2).", + "weight": 1, + "sub_tasks": [ + { + "id": "q10-code", + "requirements": "Code-development bucket: encoder/decoder built from qiskit.circuit.library, SWAP-test or direct-fidelity loss implemented correctly, training pipeline runs.", + "weight": 2, + "sub_tasks": [ + { + "id": "q10-code-encoder-decoder", + "requirements": "Encoder is qiskit.circuit.library.EfficientSU2(num_qubits=n, reps=3, entanglement='linear'). Decoder is the inverse of the encoder (via QuantumCircuit.inverse()) sharing the same parameters but acting in reverse, applied after fresh |0> ancillas have replaced the trash qubits. Both encoder and decoder are built once from qiskit.circuit.library — NOT hand-coded as H/RY/RZ + CNOT stacks (the quantum-qiskit skill warns against this anti-pattern).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "q10-code-loss-eval", + "requirements": "Training loss is implemented as either (a) a SWAP-test circuit using auxiliary qubits and the standard Hadamard-controlled-SWAP-Hadamard pattern, OR (b) a direct Statevector computation: trace out the trash register, compute its partial-trace overlap with |0><0|, and sum across trash qubits. Both implementations are valid. The reconstruction_fidelity metric on the test set uses ||^2 computed via qiskit.quantum_info.Statevector inner product (no SWAP test needed at evaluation since we have access to both states).", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "q10-code-pipeline", + "requirements": "Training: 100 iterations of qiskit_algorithms.optimizers.COBYLA.minimize() over the SWAP-test loss, summed over a fixed batch of 5 Haar-random states drawn once per (condition, seed) pair via qiskit.quantum_info.random_statevector. Evaluation: 20 fresh Haar-random states drawn from a different RNG stream than the training batch. Each cell logs METRIC_RESULT JSON with condition, seed, reconstruction_fidelity_mean (over 20 test states), swap_test_loss_final, training_time_sec.", + "weight": 6.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q10-exec", + "requirements": "Execution-validity bucket: all 15 cells ran with valid outputs.", + "weight": 2, + "sub_tasks": [ + { + "id": "q10-exec-cells-ran", + "requirements": "At least 14 cells out of 15 expected (5 conditions x 1 ensemble x 3 seeds) completed without unhandled errors and produced a reconstruction_fidelity_mean value. Missing more than 1 cell without documented justification fails this leaf.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q10-exec-numerical", + "requirements": "Numerical validity: reconstruction_fidelity is in [0, 1] for every cell; the no_compression baseline reports fidelity in [0.95, 1.0] (else eval pipeline is broken); the random_unitary_compression baseline for 4->2 reports fidelity in [0.04, 0.15] (matching the theoretical 2^(m-n) = 0.0625 average for Haar states); no cell reports identical fidelity across all 3 seeds (would indicate the Haar sampler is not actually randomizing per seed).", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "q10-results", + "requirements": "Results bucket: quantitative tests of H1/H2/H3 plus a per-hypothesis writeup.", + "weight": 3, + "sub_tasks": [ + { + "id": "q10-result-h1-quant", + "requirements": "Quantitative test of H1. On at least 2 of 3 compression-ratio conditions, is trained reconstruction_fidelity >= random_unitary_compression reconstruction_fidelity + 0.2 absolute (3-seed mean)? 100% if gap >= 0.2 on at least 2/3 conditions, 67% if gap >= 0.1 on at least 2/3, 33% if any positive gap on at least 1, 0% if random unitary equals or beats trained on all 3.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q10-result-h2-quant", + "requirements": "Quantitative test of H2. Is reconstruction_fidelity(compress_4_to_3) - reconstruction_fidelity(compress_4_to_2) >= 0.15 absolute (3-seed mean)? 100% if gap >= 0.15, 67% if 0.05-0.15, 33% if any positive gap (capacity effect at least directionally correct), 0% otherwise.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q10-result-h3-quant", + "requirements": "Quantitative test of H3. Does no_compression baseline report reconstruction_fidelity >= 0.95 (3-seed mean)? 100% if >= 0.95, 67% if >= 0.85, 33% if >= 0.70, 0% otherwise — failure of this sanity check means the eval pipeline is misconstructed.", + "weight": 8.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "q10-result-writeup", + "requirements": "Writeup of at least 200 words (submission/README.md ## Agent-produced writeup section) with explicit supported / refuted / inconclusive verdict for each of H1/H2/H3 backed by 3-seed mean reconstruction_fidelity per condition. References Romero, Olson, Aspuru-Guzik Quantum Sci Tech 2017 and discusses whether the trained autoencoder's advantage over random_unitary_compression on Haar-random data is consistent with the Page-formula expectation (Haar states have no exploitable structure, so any improvement reflects the optimizer finding a non-trivial subspace).", + "weight": 12.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/quantum/topics.yaml b/tasks/quantum/topics.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2e2f78ce87888673bb7a20fc0d4e9698c2cf3388 --- /dev/null +++ b/tasks/quantum/topics.yaml @@ -0,0 +1,102 @@ +# ============================================================================ +# ARC-Bench Quantum — open-ended quantum machine learning + VQE topics +# ---------------------------------------------------------------------------- +# Q01-Q03 are CPU-only Qiskit topics. They run on the autoclaw native sandbox +# (experiment.mode = sandbox) using qiskit-aer's statevector simulator +# (and optional finite-shot sampling for Q03). No GPU, no IBM Quantum hardware, +# no external agent. +# +# Manifests live in config/quantum/manifests/QNN.yaml +# Rubrics live in config/quantum/rubrics/QNN.json +# ============================================================================ + +topics: + - id: Q01 + topic: "Comparing quantum data encoding strategies (angle, amplitude, IQP, ZZ feature map) for variational quantum classifiers on small low-dimensional binary classification tasks" + domains: + - quantum-machine-learning + - data-encoding + - feature-maps + metric_key: test_accuracy + metric_direction: maximize + + - id: Q02 + topic: "Ablating entangling gates in a variational quantum classifier: how much classification accuracy is actually attributable to CNOTs versus single-qubit rotations" + domains: + - quantum-machine-learning + - entanglement + - ablation-study + metric_key: test_accuracy + metric_direction: maximize + + - id: Q03 + topic: "Benchmarking classical optimizers (SPSA, COBYLA, L-BFGS-B, ADAM) for VQE convergence on H2 under finite shot noise across multiple shot budgets and bond lengths" + domains: + - variational-quantum-eigensolver + - quantum-chemistry + - classical-optimization + metric_key: shots_to_chemical_accuracy + metric_direction: minimize + + - id: Q04 + topic: "Data re-uploading depth vs Fourier expressivity for variational quantum regression: characterizing how the trainable bandwidth of a parameterized re-uploading circuit scales with re-uploading layers L on synthetic 1D regression targets" + domains: + - quantum-machine-learning + - data-reuploading + - expressivity + metric_key: test_mse + metric_direction: minimize + + - id: Q05 + topic: "Barren plateau onset under cost-function locality: empirically measuring gradient variance of hardware-efficient ansatze under global vs local cost functions across depth, testing the Cerezo 2021 prediction that local costs avoid exponential gradient suppression" + domains: + - quantum-machine-learning + - barren-plateaus + - trainability + metric_key: log_gradient_variance + metric_direction: maximize + + - id: Q06 + topic: "Neural-network warm-start for QAOA-MaxCut: comparing MLP-predicted initial parameters against random and fixed initialization across in-distribution and out-of-distribution graph families" + domains: + - ai-for-quantum + - qaoa + - meta-learning + metric_key: iterations_to_target_ratio + metric_direction: minimize + + - id: Q07 + topic: "Matrix Product State classifier vs neural network at matched parameter count on downsampled image classification, characterizing the bond-dimension scaling of tensor-network classifiers as a quantum-inspired classical baseline for QML" + domains: + - quantum-inspired-classical + - tensor-networks + - supervised-learning + metric_key: test_accuracy + metric_direction: maximize + + - id: Q08 + topic: "Layerwise learning vs end-to-end training for variational quantum classifiers: testing the Skolik 2021 claim that incremental ansatz growth avoids barren plateaus on small UCI binary classification tasks" + domains: + - quantum-machine-learning + - training-strategies + - barren-plateaus + metric_key: test_accuracy + metric_direction: maximize + + - id: Q09 + topic: "Noise-aware variational quantum classifier training: comparing training under simulated depolarizing noise vs ideal-statevector training, evaluating both robustness to test-time noise and clean-test transfer" + domains: + - ai-for-quantum + - noisy-quantum-training + - robustness + metric_key: test_accuracy + metric_direction: maximize + + - id: Q10 + topic: "Quantum autoencoder for pure-state compression: training a parameterized circuit to compress n-qubit Haar-random states into m- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metric_key (string), metrics (object + with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying + a `supported` boolean), summary (non-empty string). + must_pass: true + - id: req_coverage_metrics_table + type: artifact + description: >- + A machine-readable metrics artifact (e.g. results/metrics.json) exists + reporting empirical coverage AND interval width broken down by CI method, + data-generating distribution, and sample size. + must_pass: true + - id: req_coverage_numeric + type: numeric + description: >- + results.json metrics MUST contain at least 3 numeric (non-null, finite) + empirical coverage values in [0, 1] for distinct (method, distribution) + cells, including at least one Gaussian cell and one heavy-tailed or + contaminated cell so H1 and H2 can be evaluated. + must_pass: true + - id: req_hypotheses_supported_flags + type: discussion + description: >- + results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` + boolean AND a `details` string ≥ 40 characters quoting the numerical + coverage / interval-width evidence (specific values + their source + artifact) used to reach the verdict. + must_pass: true + - id: req_diagnostic_figure + type: artifact + description: >- + At least one figure file (PNG or PDF, ≥150 DPI for raster) exists under + results/figures/ comparing coverage and/or interval width across methods + and data-generating conditions, with axes labeled and a legend. + must_pass: false + - id: req_width_tradeoff_writeup + type: discussion + description: >- + The report discusses the coverage-versus-interval-width tradeoff — + recognising that higher coverage may simply come from wider intervals and + that width must be read jointly with coverage. Nice-to-have, not blocking. + must_pass: false + - id: req_seed_documented + type: discussion + description: >- + results.json or a sibling reproducibility section names the Monte Carlo + repetition count, bootstrap resample count, and at least one explicit + random seed. Required for full reproducibility but not for scientific + correctness. + must_pass: false diff --git a/tasks/statistics/manifests/S02.yaml b/tasks/statistics/manifests/S02.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a88185fb95cf394fa77fefd4c608c40ff0e53bc6 --- /dev/null +++ b/tasks/statistics/manifests/S02.yaml @@ -0,0 +1,199 @@ +id: S02 +title: "Double machine learning for average treatment effect estimation" +arxiv_id: null +venue: "ARC-Bench Statistics 2026" +paper_asset: null + +synthesis: | + Semiparametric causal inference separates a low-dimensional target parameter, + such as the average treatment effect, from infinite-dimensional nuisance + functions such as the propensity score e(X) and outcome regressions m0(X) and + m1(X). Double machine learning studies how the target parameter can remain + estimable when those nuisance functions are learned flexibly rather than + specified by a finite-dimensional parametric model. + + The benchmark should make this relationship explicit. The study should + implement and evaluate treatment effect estimators in simulated observational + data with nonlinear confounding, compare naive regression or inverse-propensity + weighting against orthogonalized / doubly robust estimators, and test how + Neyman orthogonality and cross-fitting reduce first-order sensitivity to + nuisance estimation error. The research question is: *when the nuisance + functions are treated as infinite-dimensional objects and estimated with + flexible learners, does orthogonalization preserve reliable inference for the + finite-dimensional ATE?* + +hypotheses: + - id: H1 + statement: "A naive difference-in-means estimator is biased under confounded treatment assignment." + measurable: true + - id: H2 + statement: "A doubly robust / orthogonalized estimator has lower absolute bias than simple plug-in regression or IPW when nuisance functions are nonlinear." + measurable: true + - id: H3 + statement: "Cross-fitting improves stability or reduces bias compared with fitting nuisance functions and estimating the treatment effect on the same sample." + measurable: true + - id: H4 + statement: "Neyman-orthogonal scores make the ATE estimate less sensitive to nuisance-function estimation error than non-orthogonal plug-in estimators." + measurable: true + +experiment_design: + research_question: "How does double machine learning estimate a finite-dimensional ATE while using flexible estimators for infinite-dimensional nuisance functions under nonlinear confounding?" + estimand: + name: "average_treatment_effect" + description: "ATE = E[Y(1) - Y(0)] in a simulated observational study with known ground truth." + semiparametric_structure: + target_parameter: + name: "finite_dimensional_ate" + description: "The scalar ATE is the parameter of scientific interest." + nuisance_functions: + - name: "propensity_score" + notation: "e(X) = P(A = 1 | X)" + role: "Controls confounded treatment assignment and appears in IPW and AIPW scores." + - name: "outcome_regression_treated" + notation: "m1(X) = E[Y | A = 1, X]" + role: "Models the treated potential-outcome regression." + - name: "outcome_regression_control" + notation: "m0(X) = E[Y | A = 0, X]" + role: "Models the control potential-outcome regression." + key_relationship: "The benchmark should evaluate whether an orthogonal score protects the finite-dimensional ATE estimate from first-order errors in flexible, effectively infinite-dimensional nuisance estimates." + conditions: + - name: "difference_in_means" + description: "Naive treated-control mean difference without confounding adjustment." + - name: "ols_adjustment" + description: "Linear regression adjustment for covariates." + - name: "ipw_logistic" + description: "Inverse propensity weighting using logistic regression propensity scores." + - name: "aipw_no_crossfit" + description: "Augmented inverse propensity weighting using nuisance models fit on the same sample." + - name: "dml_aipw_crossfit" + description: "Doubly robust AIPW estimator with K-fold cross-fitting and flexible nuisance models." + - name: "nuisance_quality_sensitivity" + description: "A diagnostic condition comparing weaker and stronger nuisance learners to test sensitivity of each estimator to nuisance estimation error." + nuisance_models: + - name: "propensity_model" + candidates: + - "logistic_regression" + - "random_forest_classifier" + - "gradient_boosting_classifier" + - name: "outcome_model" + candidates: + - "linear_regression" + - "random_forest_regressor" + - "gradient_boosting_regressor" + metrics: + - name: "bias" + direction: "minimize_abs" + description: "Mean estimated ATE minus true ATE across simulation repetitions." + - name: "rmse" + direction: "minimize" + description: "Root mean squared error of ATE estimates." + - name: "coverage" + direction: "target_0.95" + description: "Empirical coverage of nominal 95% confidence intervals." + - name: "interval_width" + direction: "minimize_given_coverage" + description: "Average confidence interval width." + - name: "estimate_std" + direction: "diagnostic" + description: "Monte Carlo standard deviation of ATE estimates." + simulation_settings: + sample_sizes: [500, 1000, 3000] + monte_carlo_repetitions: 500 + crossfit_folds: 2 + seeds: [0, 1, 2, 3, 4] + data_generating_process: + covariates: "X ~ multivariate normal or uniform with nonlinear transformations" + treatment: "A ~ Bernoulli(sigmoid nonlinear function of X)" + outcome: "Y = tau * A + nonlinear function of X + noise" + true_ate: 1.0 + expected_artifacts: + - path: "src/experiment.py" + description: "Executable simulation code implementing all ATE estimators." + - path: "results/metrics.json" + description: "Machine-readable bias, RMSE, coverage, and interval width by estimator and sample size." + - path: "results/figures/" + description: "Plots or tables summarizing estimator bias, RMSE, coverage, interval width, and cross-fitting effects." + - path: "report/paper.md" + description: "Paper-style report explaining the causal estimand, DGP, estimators, uncertainty method, results, limitations, and per-hypothesis conclusions." + - path: "README.md" + description: "Setup and run instructions for reproducing the full experiment from a clean checkout." + dependencies: + python: + - numpy + - pandas + - scipy + - scikit-learn + - matplotlib + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 300 + +rubric_path: "experiments/arc_bench/config/statistics/rubrics/S02.json" + +# --------------------------------------------------------------------------- +# Agent-mode requirements (consumed by researchclaw.pipeline.requirements_judge +# at stage 15 RESEARCH_DECISION). Schema mirrors B02.yaml / P01.yaml: +# id — stable identifier +# type — advisory hint to the LLM judge (numeric | artifact | discussion) +# description — natural-language statement of what must be true post-run +# must_pass — true → unmet ⇒ rerun (1 retry max); false → optional +# This topic carries four hypotheses (H1-H4); the supported-flags requirement +# covers all four. +# --------------------------------------------------------------------------- +requirements: + - id: req_results_json + type: artifact + description: >- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metric_key (string), metrics (object + with numeric keys), hypotheses (object with h1/h2/h3/h4 entries each + carrying a `supported` boolean), summary (non-empty string). + must_pass: true + - id: req_ate_metrics_table + type: artifact + description: >- + A machine-readable metrics artifact (e.g. results/metrics.json) exists + reporting ATE estimate, bias (or absolute bias), and RMSE broken down by + estimator and sample size, with confidence-interval coverage or width + where applicable. + must_pass: true + - id: req_bias_numeric + type: numeric + description: >- + results.json metrics MUST contain numeric (non-null, finite) bias or + absolute-bias values for at least the naive difference-in-means estimator + and the doubly robust / cross-fitted DML estimator, evaluated against the + known true ATE, so H1 and H2 can be evaluated quantitatively. + must_pass: true + - id: req_hypotheses_supported_flags + type: discussion + description: >- + results.json hypotheses.h1/h2/h3/h4 each MUST have an explicit `supported` + boolean AND a `details` string ≥ 40 characters quoting the numerical + evidence (specific bias / RMSE / coverage values + their source artifact) + used to reach the verdict. + must_pass: true + - id: req_crossfit_implemented + type: artifact + description: >- + The DML condition implements sample splitting or K-fold cross-fitting so + nuisance models are trained out-of-fold relative to the observations used + in the orthogonal score; a non-cross-fitted AIPW comparison is also + present so H3 can be evaluated. + must_pass: true + - id: req_estimand_writeup + type: discussion + description: >- + The report clearly distinguishes the finite-dimensional causal estimand, + the infinite-dimensional nuisance functions, the orthogonal score, and the + evaluation metrics — avoiding confusion between prediction performance and + ATE estimation quality. Nice-to-have, not blocking. + must_pass: false + - id: req_seed_documented + type: discussion + description: >- + results.json or a sibling reproducibility section names the Monte Carlo + repetition count, cross-fitting fold count, and at least one explicit + random seed. Required for full reproducibility but not for scientific + correctness. + must_pass: false diff --git a/tasks/statistics/manifests/S03.yaml b/tasks/statistics/manifests/S03.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ae580069e3be9321c9d68d6b60b3d6ac78c30a7e --- /dev/null +++ b/tasks/statistics/manifests/S03.yaml @@ -0,0 +1,195 @@ +id: S03 +title: "Reliability of LLM-assisted statistical model selection" +arxiv_id: null +venue: "ARC-Bench Statistics 2026" +paper_asset: null + +synthesis: | + Large language models are increasingly used to assist with statistical data + analysis, including model selection, test choice, and interpretation. However, + statistical model selection requires matching assumptions to data-generating + conditions, not merely producing plausible-sounding analysis text. An LLM or + LLM-like rule-based assistant may recommend inappropriate tests when data are + skewed, heteroskedastic, non-independent, or affected by outliers. + + A credible study of this topic creates a controlled benchmark of small + statistical-analysis tasks with known ground truth. The system should compare + model-selection recommendations against an oracle or rule-based reference + across simulated datasets. The research question is: *can an LLM-assisted + statistical workflow choose appropriate statistical procedures under + assumption violations, and how often do its recommendations lead to invalid + inference?* + +hypotheses: + - id: H1 + statement: "A simple prompt-only LLM-style recommendation system is more likely to choose standard parametric tests even when assumptions are violated." + measurable: true + - id: H2 + statement: "Adding computed diagnostic statistics, such as skewness, variance ratio, normality tests, and sample-size information, improves statistical procedure selection accuracy." + measurable: true + - id: H3 + statement: "Incorrect procedure selection increases false positive rate or reduces coverage under assumption violations such as heteroskedasticity, skew, or dependence." + measurable: true + +experiment_design: + research_question: "Can LLM-assisted statistical model selection reliably choose appropriate procedures when assumptions are violated?" + task_type: + name: "statistical_test_selection" + description: "Given dataset summaries and analysis goals, choose an appropriate statistical test or model." + conditions: + - name: "text_only_recommender" + description: "Procedure selection using only natural-language task description and variable metadata." + - name: "diagnostics_augmented_recommender" + description: "Procedure selection using natural-language description plus computed diagnostics." + - name: "oracle_rule_based_selector" + description: "Reference selector using known data-generating process or explicit rules." + - name: "always_parametric_baseline" + description: "Baseline that always chooses common parametric procedures such as t-test, ANOVA, or OLS." + statistical_tasks: + - name: "two_sample_mean_comparison" + candidate_methods: + - "student_t_test" + - "welch_t_test" + - "mann_whitney_u" + - "permutation_test" + - name: "correlation_analysis" + candidate_methods: + - "pearson_correlation" + - "spearman_correlation" + - "robust_regression" + - name: "linear_effect_estimation" + candidate_methods: + - "ordinary_least_squares" + - "heteroskedasticity_robust_ols" + - "rank_based_method" + - "permutation_inference" + data_generating_conditions: + - name: "normal_equal_variance" + description: "Parametric assumptions approximately hold." + - name: "normal_unequal_variance" + description: "Heteroskedastic two-sample setting." + - name: "skewed_lognormal" + description: "Strongly skewed outcome distribution." + - name: "outlier_contaminated" + description: "Small fraction of extreme observations." + - name: "nonlinear_monotone_relationship" + description: "Correlation exists but is nonlinear or rank-based." + metrics: + - name: "selection_accuracy" + direction: "maximize" + description: "Fraction of tasks where selected method matches oracle-acceptable method set." + - name: "false_positive_rate" + direction: "target_nominal" + description: "Empirical Type I error under null simulations." + - name: "coverage" + direction: "target_0.95" + description: "Empirical confidence interval coverage when interval estimates are produced." + - name: "assumption_violation_detection_rate" + direction: "maximize" + description: "Fraction of cases where the system correctly identifies relevant assumption violations." + - name: "explanation_grounding_score" + direction: "maximize" + description: "Whether the written explanation cites the correct diagnostic evidence rather than generic statistical advice." + implementation_note: | + If external LLM API access is unavailable, the submission may simulate an + LLM-assisted workflow using templated recommendations, local heuristics, or + stored model outputs. The benchmark should focus on statistical validity of + recommendations and downstream inference, not on proprietary model access. + simulation_settings: + sample_sizes: [30, 100, 500] + monte_carlo_repetitions: 500 + seeds: [0, 1, 2, 3, 4] + expected_artifacts: + - path: "src/generate_tasks.py" + description: "Code for generating synthetic statistical-analysis tasks." + - path: "src/evaluate_selectors.py" + description: "Code for evaluating selectors and downstream inference." + - path: "results/metrics.json" + description: "Machine-readable selection accuracy, false positive rate, coverage, and diagnostic-detection metrics." + - path: "results/selector_decisions.csv" + description: "Machine-readable selector decisions with task metadata, diagnostics, oracle-acceptable methods, and selected procedures." + - path: "results/figures/" + description: "Plots or tables comparing selectors across task types, data-generating conditions, and assumption violations." + - path: "report/paper.md" + description: "Paper-style report explaining task generation, selector designs, oracle rules, diagnostic evidence, results, limitations, and per-hypothesis conclusions." + - path: "README.md" + description: "Setup and run instructions for reproducing the full benchmark from a clean checkout." + dependencies: + python: + - numpy + - pandas + - scipy + - scikit-learn + - statsmodels + - matplotlib + compute_requirements: + gpu_required: false + estimated_wall_clock_sec: 300 + +rubric_path: "experiments/arc_bench/config/statistics/rubrics/S03.json" + +# --------------------------------------------------------------------------- +# Agent-mode requirements (consumed by researchclaw.pipeline.requirements_judge +# at stage 15 RESEARCH_DECISION). Schema mirrors B02.yaml / P01.yaml: +# id — stable identifier +# type — advisory hint to the LLM judge (numeric | artifact | discussion) +# description — natural-language statement of what must be true post-run +# must_pass — true → unmet ⇒ rerun (1 retry max); false → optional +# --------------------------------------------------------------------------- +requirements: + - id: req_results_json + type: artifact + description: >- + A canonical results.json file exists at the workspace root with at least + the keys: primary_metric (number), metric_key (string), metrics (object + with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying + a `supported` boolean), summary (non-empty string). + must_pass: true + - id: req_selector_metrics_table + type: artifact + description: >- + A machine-readable metrics artifact (e.g. results/metrics.json) exists + reporting selection accuracy and false-positive rate (or Type I error) + broken down by selector and data-generating condition, plus a + results/selector_decisions.csv with per-task selector decisions and + oracle-acceptable method labels. + must_pass: true + - id: req_selection_accuracy_numeric + type: numeric + description: >- + results.json metrics MUST contain numeric (non-null, finite) selection + accuracy values in [0, 1] for at least the text-only recommender and the + diagnostics-augmented recommender, evaluated over more than one + assumption-violation condition, so H1 and H2 can be evaluated. + must_pass: true + - id: req_hypotheses_supported_flags + type: discussion + description: >- + results.json hypotheses.h1/h2/h3 each MUST have an explicit `supported` + boolean AND a `details` string ≥ 40 characters quoting the numerical + evidence (specific accuracy / false-positive-rate / coverage values + their + source artifact) used to reach the verdict. + must_pass: true + - id: req_oracle_and_selectors + type: artifact + description: >- + The submission implements or simulates all four selectors — text-only + recommender, diagnostics-augmented recommender, oracle / rule-based + reference selector, and an always-parametric baseline — and runs the + selected procedures to record downstream inference (p-values, coverage, + or Type I error). + must_pass: true + - id: req_method_validity_writeup + type: discussion + description: >- + The report clearly distinguishes the analysis goal, data-generating + condition, oracle-acceptable method set, selected method, diagnostic + evidence, and downstream inference metric. Nice-to-have, not blocking. + must_pass: false + - id: req_llm_access_disclosed + type: discussion + description: >- + The report states whether an external LLM API was used or whether a + templated / heuristic / stored-output recommender stood in for it. + Required for honest reporting but not for scientific correctness. + must_pass: false diff --git a/tasks/statistics/rubrics/S01.json b/tasks/statistics/rubrics/S01.json new file mode 100644 index 0000000000000000000000000000000000000000..a2e25adfe05a0cb73828a6e46396b24b6b0c29ed --- /dev/null +++ b/tasks/statistics/rubrics/S01.json @@ -0,0 +1,133 @@ +{ + "id": "s01-root", + "requirements": "A credible simulation study evaluating whether bootstrap confidence interval choice affects empirical coverage under light-tailed, skewed, and heavy-tailed data. The submission should implement multiple CI methods, simulate several data-generating distributions and sample sizes, report coverage and interval width over repeated trials, and connect the numeric findings to the three hypotheses.", + "judging_note": "Score on statistical substance, correct simulation design, and directional correctness of evidence. Do not require exact repetition counts if the submission is computationally reasonable. Partial but well-motivated simulations deserve partial credit; rigid naming differences should not penalize a substantively correct experiment.", + "weight": 1, + "sub_tasks": [ + { + "id": "s01-code", + "requirements": "The bootstrap simulation code implements meaningful confidence interval comparisons across relevant distributional regimes.", + "weight": 2, + "sub_tasks": [ + { + "id": "s01-code-ci-methods", + "requirements": "The submission implements at least two bootstrap confidence interval methods, typically percentile bootstrap and studentized/bootstrap-t or another justified alternative. Methods should be implemented as distinct code paths rather than cosmetic options.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "s01-code-robust-estimator", + "requirements": "The submission includes a robust location estimator, such as median or trimmed mean, and compares it against the ordinary sample mean under heavy-tailed or contaminated data.", + "weight": 7.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "s01-code-dgps", + "requirements": "The submission simulates multiple data-generating processes covering at least light-tailed and heavy-tailed cases, with skewed data such as lognormal or contaminated data receiving additional credit.", + "weight": 7.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "s01-code-sample-sizes", + "requirements": "The simulation evaluates more than one sample size so that finite-sample behavior can be distinguished from asymptotic behavior.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "s01-exec", + "requirements": "Execution produces coverage and interval-quality metrics adequate to evaluate bootstrap CI behavior.", + "weight": 2, + "sub_tasks": [ + { + "id": "s01-exec-metrics", + "requirements": "Execution produces a machine-readable metrics artifact, such as results/metrics.json, containing numeric empirical coverage and interval width by condition, distribution, and sample size. Coverage error or failure rate may supplement these metrics.", + "weight": 15.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "s01-exec-repetitions", + "requirements": "Reported metrics are based on repeated Monte Carlo trials and bootstrap resampling. The exact number of repetitions need not match the topic file, but it should be large enough to make coverage comparisons meaningful and should be honestly reported.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "s01-exec-truth", + "requirements": "The simulation correctly defines the true estimand for each data-generating process, such as the true mean or true robust location target, and checks whether confidence intervals contain that estimand.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "s01-paper", + "requirements": "The final paper or report addresses the three bootstrap hypotheses with quantitative evidence and a clear statistical narrative.", + "weight": 3, + "sub_tasks": [ + { + "id": "s01-result-h1", + "requirements": "The submission evaluates whether percentile bootstrap confidence intervals achieve near-nominal coverage under light-tailed Gaussian data, especially at moderate or larger sample sizes, and states whether H1 is supported, refuted, or inconclusive.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "s01-result-h2", + "requirements": "The submission evaluates whether percentile bootstrap intervals for the sample mean undercover under heavy-tailed data and supports the conclusion with empirical coverage numbers.", + "weight": 15.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "s01-result-h3", + "requirements": "The submission compares robust location estimators against the ordinary sample mean under heavy-tailed or contaminated data and discusses whether robustness improves coverage stability or interval behavior.", + "weight": 15.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "s01-result-width-tradeoff", + "requirements": "The analysis discusses interval-width tradeoffs, recognizing that higher coverage may come from wider intervals and that width should be interpreted jointly with coverage.", + "weight": 7.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "s01-result-writeup", + "requirements": "The README or writeup describes the simulation setup, CI methods, data-generating processes, key numeric results, and per-hypothesis outcomes with appropriate caveats on repetition count, bootstrap count, and Monte Carlo uncertainty.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/statistics/rubrics/S02.json b/tasks/statistics/rubrics/S02.json new file mode 100644 index 0000000000000000000000000000000000000000..20a49bc6944812deb18fa5682a4c8bc35f99992f --- /dev/null +++ b/tasks/statistics/rubrics/S02.json @@ -0,0 +1,157 @@ +{ + "id": "s02-root", + "requirements": "A credible semiparametric simulation study evaluating how double machine learning / orthogonalized AIPW estimates a finite-dimensional average treatment effect while using flexible estimators for infinite-dimensional nuisance functions under nonlinear confounding. The submission should implement multiple estimators, simulate observational data with known true ATE, evaluate bias, RMSE, confidence interval behavior, and sensitivity to nuisance estimation quality, and tie the numeric findings to the hypotheses.", + "judging_note": "Score on causal and semiparametric substance rather than exact package choices. A correct hand-written AIPW / DML implementation should receive full credit even if it does not use a specialized causal inference library. Partial credit should reward clear separation of the finite-dimensional estimand from infinite-dimensional nuisance functions, correct use of orthogonal scores, nuisance estimation, cross-fitting, and honest uncertainty reporting.", + "weight": 1, + "sub_tasks": [ + { + "id": "s02-code", + "requirements": "The code implements a meaningful comparison of ATE estimators under simulated confounding with known ground truth.", + "weight": 2, + "sub_tasks": [ + { + "id": "s02-code-dgp", + "requirements": "The submission implements a simulated observational data-generating process with covariates, confounded treatment assignment, outcome generation, known true average treatment effect, and explicit true or approximate nuisance functions such as propensity scores and outcome regressions.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "s02-code-baselines", + "requirements": "The submission implements simple baseline estimators such as difference-in-means, regression adjustment, and/or inverse propensity weighting so that DML is compared against meaningful alternatives.", + "weight": 7.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "s02-code-aipw", + "requirements": "The submission implements a doubly robust or Neyman-orthogonal AIPW-style estimator using estimated propensity and outcome nuisance functions, and explains why the score targets the finite-dimensional ATE while treating those nuisances as flexible or infinite-dimensional.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "s02-code-crossfit", + "requirements": "The DML condition uses sample splitting or K-fold cross-fitting so nuisance models are trained out-of-fold relative to the observations used in the orthogonal score.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "s02-code-nuisance-models", + "requirements": "The nuisance functions are estimated with reasonable models for the simulated setting, such as logistic regression, random forests, gradient boosting, or other justified supervised learning methods, with at least one comparison that changes nuisance-model flexibility or quality.", + "weight": 7.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "s02-exec", + "requirements": "Execution produces ATE estimation metrics adequate to evaluate bias, precision, and interval behavior.", + "weight": 2, + "sub_tasks": [ + { + "id": "s02-exec-metrics", + "requirements": "Execution produces a machine-readable metrics artifact, such as results/metrics.json, containing numeric ATE estimates, bias or absolute bias, RMSE, and preferably confidence interval coverage or interval width by estimator and sample size.", + "weight": 17.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "s02-exec-repetitions", + "requirements": "Reported metrics are aggregated over multiple simulation repetitions or random seeds, with some dispersion reporting such as standard deviation, standard error, confidence interval, or quantiles.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "s02-exec-sample-sizes", + "requirements": "The experiment evaluates at least one nontrivial sample size and receives more credit for multiple sample sizes showing finite-sample behavior.", + "weight": 5.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "s02-exec-uncertainty", + "requirements": "The submission computes uncertainty estimates, such as influence-function standard errors, bootstrap standard errors, or Monte Carlo confidence intervals, sufficient to discuss coverage or reliability.", + "weight": 7.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "s02-paper", + "requirements": "The final paper or report addresses the semiparametric hypotheses with quantitative evidence and a clear causal-inference narrative about finite-dimensional targets and infinite-dimensional nuisance functions.", + "weight": 3, + "sub_tasks": [ + { + "id": "s02-result-h1", + "requirements": "The submission evaluates whether the naive difference-in-means estimator is biased under confounded treatment assignment and supports the conclusion using the known true ATE.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "s02-result-h2", + "requirements": "The submission compares the doubly robust / orthogonalized estimator against simpler plug-in regression or IPW estimators and states whether DML reduces absolute bias or RMSE under nonlinear nuisance functions.", + "weight": 17.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "s02-result-h3", + "requirements": "The submission compares cross-fitted and non-cross-fitted versions of the doubly robust estimator, or otherwise clearly analyzes the contribution of cross-fitting to bias, RMSE, or stability.", + "weight": 15.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "s02-result-h4", + "requirements": "The submission evaluates whether Neyman-orthogonal scores make the ATE estimate less sensitive to nuisance-function estimation error than non-orthogonal plug-in estimators, using weaker versus stronger nuisance learners or another explicit nuisance-quality diagnostic.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "s02-result-estimand", + "requirements": "The writeup clearly distinguishes the finite-dimensional causal estimand, infinite-dimensional nuisance functions, orthogonal score or estimator, and evaluation metrics, avoiding confusion between prediction performance and ATE estimation quality.", + "weight": 7.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "s02-result-writeup", + "requirements": "The README or writeup describes the data-generating process, estimators, nuisance models, cross-fitting procedure, semiparametric target-vs-nuisance relationship, key numeric results, and per-hypothesis outcomes with appropriate caveats on sample size, simulation repetitions, and model misspecification.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/statistics/rubrics/S03.json b/tasks/statistics/rubrics/S03.json new file mode 100644 index 0000000000000000000000000000000000000000..be4f86d681309742821e716495e53d867620a64b --- /dev/null +++ b/tasks/statistics/rubrics/S03.json @@ -0,0 +1,149 @@ +{ + "id": "s03-root", + "requirements": "A credible controlled simulation study evaluating whether LLM-assisted or LLM-like statistical model selection chooses appropriate procedures under assumption violations. The submission should generate statistical-analysis tasks with known reference decisions, compare text-only and diagnostics-augmented recommenders against an oracle or rule-based selector, evaluate downstream inference validity, and connect the numeric findings to the three hypotheses.", + "judging_note": "Score on statistical validity, reproducible task generation, appropriate reference decisions, and honest discussion of downstream inference. Do not require access to an external LLM API; a templated, heuristic, local, or stored-output recommender can receive full credit if it supports the benchmark question. Penalize generic recommendation text that is not tied to diagnostics or data-generating conditions.", + "weight": 1, + "sub_tasks": [ + { + "id": "s03-code", + "requirements": "The code implements a controlled benchmark for statistical procedure selection under several data-generating conditions and selector designs.", + "weight": 2, + "sub_tasks": [ + { + "id": "s03-code-task-generator", + "requirements": "The submission generates multiple statistical-analysis tasks, including two-sample mean comparison, correlation analysis, and linear effect estimation, with known null or alternative settings and fixed random seeds.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Dataset and Model Acquisition" + }, + { + "id": "s03-code-assumption-conditions", + "requirements": "The generated tasks cover meaningful assumption regimes, including approximately normal equal-variance data and at least three violation regimes such as heteroskedasticity, skewness, outliers, nonlinear monotone relationships, or dependence.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Experimental Setup" + }, + { + "id": "s03-code-selectors", + "requirements": "The submission implements or simulates the required selectors: a text-only recommender, a diagnostics-augmented recommender, an oracle or rule-based reference selector, and a simple parametric baseline.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "s03-code-diagnostics", + "requirements": "The diagnostics-augmented condition computes relevant diagnostic information, such as skewness, variance ratio, normality checks, outlier indicators, sample size, monotonicity, or heteroskedasticity evidence, and makes those diagnostics available to the selector.", + "weight": 7.5, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Method Implementation" + }, + { + "id": "s03-code-downstream-inference", + "requirements": "The code runs the selected statistical procedures or models and records downstream quantities such as p-values, confidence intervals, Type I error, coverage, or effect estimates where applicable.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Development", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "s03-exec", + "requirements": "Execution produces machine-readable selector and inference metrics adequate to evaluate statistical reliability.", + "weight": 2, + "sub_tasks": [ + { + "id": "s03-exec-metrics", + "requirements": "Execution produces a machine-readable metrics artifact, such as results/metrics.json, containing selection accuracy, false positive rate or Type I error, confidence interval coverage where applicable, assumption-violation detection rate, and explanation grounding by selector and data condition.", + "weight": 17.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "s03-exec-repetitions", + "requirements": "Reported metrics are aggregated over multiple simulation repetitions, seeds, task instances, or sample sizes, with enough repetition to make selector differences meaningful.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "s03-exec-task-coverage", + "requirements": "The executed benchmark includes more than one task type and more than one assumption-violation condition, rather than evaluating a single isolated example.", + "weight": 7.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Evaluation, Metrics & Benchmarking" + }, + { + "id": "s03-exec-artifacts", + "requirements": "Execution artifacts include selector decisions, oracle-acceptable method labels, diagnostic summaries, and at least one plot or table comparing selectors across assumption regimes.", + "weight": 7.5, + "sub_tasks": [], + "task_category": "Code Execution", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + }, + { + "id": "s03-paper", + "requirements": "The final paper or report addresses the three model-selection reliability hypotheses with quantitative evidence and a clear statistical narrative.", + "weight": 3, + "sub_tasks": [ + { + "id": "s03-paper-h1", + "requirements": "The report evaluates whether the text-only recommender over-selects standard parametric procedures under assumption violations and supports the conclusion with selector-decision metrics.", + "weight": 12.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "s03-paper-h2", + "requirements": "The report compares diagnostics-augmented and text-only selection and states whether computed diagnostics improve procedure selection accuracy or assumption-violation detection.", + "weight": 15.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "s03-paper-h3", + "requirements": "The report analyzes whether incorrect procedure selection leads to invalid inference, such as inflated false positive rates, poor coverage, or misleading effect estimates under specific violations.", + "weight": 15.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "s03-paper-method-validity", + "requirements": "The writeup clearly distinguishes the analysis goal, data-generating condition, oracle-acceptable method set, selected method, diagnostic evidence, and downstream inference metric.", + "weight": 7.5, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + }, + { + "id": "s03-paper-writeup", + "requirements": "The README or paper describes task generation, selector designs, diagnostic features, oracle rules, key numeric results, limitations, and per-hypothesis outcomes with appropriate caveats on simulation scope and any use or non-use of external LLM APIs.", + "weight": 10.0, + "sub_tasks": [], + "task_category": "Result Analysis", + "finegrained_task_category": "Logging, Analysis & Presentation" + } + ], + "task_category": null, + "finegrained_task_category": null + } + ], + "task_category": null, + "finegrained_task_category": null +} diff --git a/tasks/statistics/topics.yaml b/tasks/statistics/topics.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bf5bdc84aa3671386cdee70e940f82fa93776172 --- /dev/null +++ b/tasks/statistics/topics.yaml @@ -0,0 +1,32 @@ +# ============================================================================ +# ARC-Bench Statistics — CPU-friendly statistical-methodology research topics +# ---------------------------------------------------------------------------- +# S01+ are open research QUESTIONS (not paper reproductions): the agent designs +# and runs a competent simulation study addressing the stated question. Each +# topic stays CPU-executable in minutes with numpy / scipy / pandas / sklearn / +# statsmodels / matplotlib. +# +# Manifests live in config/statistics/manifests/SNN.yaml +# Rubrics live in config/statistics/rubrics/SNN.json +# +# All statistics topics use `metric_key: primary_metric`, matching the +# biology / physics registries — the manifest's experiment_design.metrics list +# carries the real per-topic observables (coverage, bias, selection accuracy). +# ============================================================================ + +topics: + - id: S01 + topic: "Bootstrap confidence interval coverage under light-tailed, skewed, and heavy-tailed data" + domains: ["statistics", "resampling-methods", "simulation-study"] + metric_key: "primary_metric" + metric_direction: "maximize" + - id: S02 + topic: "Double machine learning for average treatment effect estimation under nonlinear confounding" + domains: ["statistics", "causal-inference", "semiparametric-estimation"] + metric_key: "primary_metric" + metric_direction: "maximize" + - id: S03 + topic: "Reliability of LLM-assisted statistical model selection under assumption violations" + domains: ["statistics", "model-selection", "llm-evaluation"] + metric_key: "primary_metric" + metric_direction: "maximize"