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tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/best/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,3 @@
+def evaluate_aux(results_dir, primary_result=None):
+    """Return auxiliary metrics as a dict."""
+    return {}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/best/results/correct.json

@@ -0,0 +1,4 @@
+{
+    "correct": true,
+    "error": null
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/best/results/metrics.json

@@ -0,0 +1,32 @@
+{
+  "combined_score": -1.5969140188371829,
+  "correct": true,
+  "primary": {
+    "combined_score": -1.5969140188371829,
+    "public": {
+      "best_value": 1.5969140188371829,
+      "best_length": 768,
+      "num_runs": 1
+    },
+    "private": {
+      "all_values": [
+        1.5969140188371829
+      ],
+      "all_lengths": [
+        768
+      ]
+    },
+    "text_feedback": "Lower evaluate_sequence value is better. combined_score = -best_value.",
+    "execution_time_mean": 9.62328302115202,
+    "execution_time_std": 0.0,
+    "num_valid_runs": 1,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": true,
+    "validation_error": null
+  },
+  "auxiliary": {},
+  "auxiliary_descriptions": {},
+  "timestamp": 1770952577.177127,
+  "generation": 72
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_0/results/correct.json

@@ -0,0 +1,4 @@
+{
+    "correct": true,
+    "error": null
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_0/results/job_log.err

@@ -0,0 +1,9 @@
+/home/tengxiao/pj/ShinkaEvolve/.venv/lib/python3.13/site-packages/instructor/providers/gemini/client.py:5: FutureWarning: 
+
+All support for the `google.generativeai` package has ended. It will no longer be receiving 
+updates or bug fixes. Please switch to the `google.genai` package as soon as possible.
+See README for more details:
+
+https://github.com/google-gemini/deprecated-generative-ai-python/blob/main/README.md
+
+  import google.generativeai as genai  # type: ignore[import-not-found]

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_0/results/job_log.out

@@ -0,0 +1,9 @@
+Evaluating program: tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_0/main.py
+Saving results to: tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_0/results
+Number of runs: 1
+Running with timeout: 120s
+Run 1/1 completed in 9.82 seconds
+Correctness and error status saved to tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_0/results/correct.json
+Metrics saved to tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_0/results/metrics.json
+Evaluation completed successfully.
+combined_score=-1.7189562728029888

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_0/results/metrics.json

@@ -0,0 +1,22 @@
+{
+    "combined_score": -1.7189562728029888,
+    "public": {
+        "best_value": 1.7189562728029888,
+        "best_length": 768,
+        "num_runs": 1
+    },
+    "private": {
+        "all_values": [
+            1.7189562728029888
+        ],
+        "all_lengths": [
+            768
+        ]
+    },
+    "text_feedback": "Lower evaluate_sequence value is better. combined_score = -best_value.",
+    "execution_time_mean": 9.824491088278592,
+    "execution_time_std": 0.0,
+    "num_valid_runs": 1,
+    "num_invalid_runs": 0,
+    "all_validation_errors": []
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_1/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,3 @@
+def evaluate_aux(results_dir, primary_result=None):
+    """Return auxiliary metrics as a dict."""
+    return {}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_1/results/correct.json

@@ -0,0 +1,4 @@
+{
+    "correct": false,
+    "error": "ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_1/results/metrics.json

@@ -0,0 +1,19 @@
+{
+  "combined_score": 0.0,
+  "correct": false,
+  "primary": {
+    "combined_score": 0.0,
+    "execution_time_mean": 0.0,
+    "execution_time_std": 0.0,
+    "num_successful_runs": 0,
+    "num_valid_runs": 0,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": false,
+    "validation_error": "ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
+  },
+  "auxiliary": {},
+  "auxiliary_descriptions": {},
+  "timestamp": 1770945966.9155874,
+  "generation": 1
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_10/edit.diff

@@ -0,0 +1,177 @@
+--- a/original.py
++++ b/original.py
+@@ -1,111 +1,127 @@
+ # EVOLVE-BLOCK-START
+ """Initial search baseline for the AlphaEvolve AC inequality task."""
+ 
+ import time
+ import numpy as np
+ 
+ 
+ def evaluate_sequence(sequence: list[float]) -> float:
+     """
+     Evaluate coefficient sequence with safety checks.
+     Lower is better.
+     """
+     if not isinstance(sequence, list):
+         return float(np.inf)
+     if not sequence:
+         return float(np.inf)
+ 
+     clean: list[float] = []
+     for x in sequence:
+         if isinstance(x, bool) or not isinstance(x, (int, float)):
+             return float(np.inf)
+         if np.isnan(x) or np.isinf(x):
+             return float(np.inf)
+         clean.append(float(x))
+ 
+     clean = [max(0.0, min(1000.0, x)) for x in clean]
+     n = len(clean)
+     conv = np.convolve(clean, clean)
+     max_b = float(np.max(conv))
+     sum_a = float(np.sum(clean))
+     if sum_a < 0.01:
+         return float(np.inf)
+     return float(2.0 * n * max_b / (sum_a**2))
+ 
+ 
+ def _normalize_scale(seq: np.ndarray) -> np.ndarray:
+     """Scale sequence to stable magnitude while keeping nonnegative shape."""
+     s = np.asarray(seq, dtype=np.float64)
+     s = np.clip(s, 0.0, None)
+     total = float(np.sum(s))
+     if total <= 1e-12:
+         return np.ones_like(s)
+     # objective is scale-invariant theoretically, but this keeps numerics stable.
+     return s / total * len(s)
+ 
+ 
+ def _seed_sequence(rng: np.random.Generator, n: int) -> np.ndarray:
+-    x = np.linspace(0.0, 1.0, n, endpoint=False)
+-    base = (
+-        0.9
+-        + 0.30 * np.sin(2.0 * np.pi * x)
+-        + 0.12 * np.sin(6.0 * np.pi * x + rng.uniform(0.0, 2.0 * np.pi))
+-    )
+-    noise = rng.normal(0.0, 0.08, n)
+-    return _normalize_scale(base + noise)
++    """Diverse initializations: sine waves, random, or power laws."""
++    r = rng.random()
++    if r < 0.4:
++        x = np.linspace(0.0, 1.0, n, endpoint=False)
++        base = 1.0 + 0.4 * np.sin(2.0 * np.pi * x * rng.uniform(1, 3))
++    elif r < 0.7:
++        p = rng.uniform(0.3, 0.6)
++        base = (np.arange(n) + 1.0)**(-p)
++    else:
++        p = rng.uniform(0.3, 0.6)
++        base = (np.arange(n) + 1.0)**(-p) + (n - np.arange(n))**(-p)
++    base += rng.normal(0, 0.05, n)
++    return _normalize_scale(base)
+ 
+ 
+ def run(seed: int = 42, budget_s: float = 10.0, **kwargs) -> list[float]:
+-    """
+-    Search for a low-value nonnegative sequence.
+-
+-    Returns:
+-        list[float]: candidate coefficient sequence
+-    """
++    """Search for a low-value nonnegative sequence using gradient and smoothing moves."""
+     del kwargs
+-
+     rng = np.random.default_rng(seed)
+     start = time.time()
+     deadline = start + max(0.1, budget_s * 0.98)
+ 
+-    # Multi-start initial pool.
+-    candidate_lengths = [128, 192, 256, 320, 384]
+-    best_seq = _seed_sequence(rng, n=256)
++    candidate_lengths = [128, 256, 512, 768]
++    best_seq = _seed_sequence(rng, 256)
+     best_val = evaluate_sequence(best_seq.tolist())
+ 
+     for n in candidate_lengths:
+-        seq = _seed_sequence(rng, n=n)
+-        val = evaluate_sequence(seq.tolist())
++        for _ in range(2):
++            seq = _seed_sequence(rng, n)
++            val = evaluate_sequence(seq.tolist())
++            if val < best_val:
++                best_seq, best_val = seq.copy(), val
++
++    current_seq = best_seq.copy()
++    momentum = np.zeros_like(current_seq)
++
++    while time.time() < deadline:
++        n = len(current_seq)
++        conv = np.convolve(current_seq, current_seq)
++        p_max = np.max(conv)
++        sum_a = np.sum(current_seq)
++
++        # Gradient targeting peaks to flatten the convolution
++        peaks = np.where(conv >= 0.995 * p_max)[0]
++        grad_p = np.zeros(n)
++        for pk in peaks:
++            s_j, e_j = max(0, pk - (n - 1)), min(n - 1, pk)
++            grad_p[s_j : e_j + 1] += current_seq[pk - e_j : pk - s_j + 1][::-1]
++        grad_p /= len(peaks)
++
++        # Combined gradient: dV/da_j proportional to (S * a_rev - P)
++        grad = (sum_a * grad_p - p_max)
++        if len(momentum) != n: momentum = np.zeros(n)
++        momentum = 0.8 * momentum + 0.2 * grad
++
++        step_size = 0.002 + 0.01 * rng.random()
++        current_seq -= step_size * momentum
++
++        # Occasional smoothing or upsampling
++        if rng.random() < 0.1:
++            current_seq = 0.15 * np.roll(current_seq, 1) + 0.7 * current_seq + 0.15 * np.roll(current_seq, -1)
++        if rng.random() < 0.05 and n < 800:
++            current_seq = np.repeat(current_seq, 2)[:800]
++            momentum = np.zeros(len(current_seq))
++
++        current_seq = _normalize_scale(current_seq)
++        val = evaluate_sequence(current_seq.tolist())
++
+         if val < best_val:
+-            best_seq, best_val = seq, val
+-
+-    # Local stochastic search: block perturbation + smoothing.
+-    while time.time() < deadline:
+-        seq = best_seq.copy()
+-        n = len(seq)
+-
+-        block = int(rng.integers(max(4, n // 64), max(8, n // 10)))
+-        idx = rng.choice(n, size=block, replace=False)
+-        seq[idx] += rng.normal(0.0, 0.10, size=block)
+-
+-        if rng.random() < 0.35:
+-            # low-pass smooth
+-            seq = (
+-                0.25 * np.roll(seq, 1)
+-                + 0.50 * seq
+-                + 0.25 * np.roll(seq, -1)
+-            )
+-
+-        if rng.random() < 0.15 and n < 800:
+-            # upsample once in a while to explore larger dimensions
+-            seq = np.repeat(seq, 2)[: min(800, 2 * n)]
+-
+-        seq = _normalize_scale(seq)
+-        val = evaluate_sequence(seq.tolist())
+-        if val < best_val:
+-            best_seq, best_val = seq, val
++            best_seq, best_val = current_seq.copy(), val
++        elif rng.random() < 0.1: # Explore
++            pass
++        else: # Greedy descent
++            current_seq = best_seq.copy()
++            momentum *= 0.5
+ 
+     return [float(x) for x in best_seq.tolist()]
+ 
+ 
+ # EVOLVE-BLOCK-END

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_10/main.py

@@ -0,0 +1,127 @@
+# EVOLVE-BLOCK-START
+"""Initial search baseline for the AlphaEvolve AC inequality task."""
+
+import time
+import numpy as np
+
+
+def evaluate_sequence(sequence: list[float]) -> float:
+    """
+    Evaluate coefficient sequence with safety checks.
+    Lower is better.
+    """
+    if not isinstance(sequence, list):
+        return float(np.inf)
+    if not sequence:
+        return float(np.inf)
+
+    clean: list[float] = []
+    for x in sequence:
+        if isinstance(x, bool) or not isinstance(x, (int, float)):
+            return float(np.inf)
+        if np.isnan(x) or np.isinf(x):
+            return float(np.inf)
+        clean.append(float(x))
+
+    clean = [max(0.0, min(1000.0, x)) for x in clean]
+    n = len(clean)
+    conv = np.convolve(clean, clean)
+    max_b = float(np.max(conv))
+    sum_a = float(np.sum(clean))
+    if sum_a < 0.01:
+        return float(np.inf)
+    return float(2.0 * n * max_b / (sum_a**2))
+
+
+def _normalize_scale(seq: np.ndarray) -> np.ndarray:
+    """Scale sequence to stable magnitude while keeping nonnegative shape."""
+    s = np.asarray(seq, dtype=np.float64)
+    s = np.clip(s, 0.0, None)
+    total = float(np.sum(s))
+    if total <= 1e-12:
+        return np.ones_like(s)
+    # objective is scale-invariant theoretically, but this keeps numerics stable.
+    return s / total * len(s)
+
+
+def _seed_sequence(rng: np.random.Generator, n: int) -> np.ndarray:
+    """Diverse initializations: sine waves, random, or power laws."""
+    r = rng.random()
+    if r < 0.4:
+        x = np.linspace(0.0, 1.0, n, endpoint=False)
+        base = 1.0 + 0.4 * np.sin(2.0 * np.pi * x * rng.uniform(1, 3))
+    elif r < 0.7:
+        p = rng.uniform(0.3, 0.6)
+        base = (np.arange(n) + 1.0)**(-p)
+    else:
+        p = rng.uniform(0.3, 0.6)
+        base = (np.arange(n) + 1.0)**(-p) + (n - np.arange(n))**(-p)
+    base += rng.normal(0, 0.05, n)
+    return _normalize_scale(base)
+
+
+def run(seed: int = 42, budget_s: float = 10.0, **kwargs) -> list[float]:
+    """Search for a low-value nonnegative sequence using gradient and smoothing moves."""
+    del kwargs
+    rng = np.random.default_rng(seed)
+    start = time.time()
+    deadline = start + max(0.1, budget_s * 0.98)
+
+    candidate_lengths = [128, 256, 512, 768]
+    best_seq = _seed_sequence(rng, 256)
+    best_val = evaluate_sequence(best_seq.tolist())
+
+    for n in candidate_lengths:
+        for _ in range(2):
+            seq = _seed_sequence(rng, n)
+            val = evaluate_sequence(seq.tolist())
+            if val < best_val:
+                best_seq, best_val = seq.copy(), val
+
+    current_seq = best_seq.copy()
+    momentum = np.zeros_like(current_seq)
+
+    while time.time() < deadline:
+        n = len(current_seq)
+        conv = np.convolve(current_seq, current_seq)
+        p_max = np.max(conv)
+        sum_a = np.sum(current_seq)
+
+        # Gradient targeting peaks to flatten the convolution
+        peaks = np.where(conv >= 0.995 * p_max)[0]
+        grad_p = np.zeros(n)
+        for pk in peaks:
+            s_j, e_j = max(0, pk - (n - 1)), min(n - 1, pk)
+            grad_p[s_j : e_j + 1] += current_seq[pk - e_j : pk - s_j + 1][::-1]
+        grad_p /= len(peaks)
+
+        # Combined gradient: dV/da_j proportional to (S * a_rev - P)
+        grad = (sum_a * grad_p - p_max)
+        if len(momentum) != n: momentum = np.zeros(n)
+        momentum = 0.8 * momentum + 0.2 * grad
+
+        step_size = 0.002 + 0.01 * rng.random()
+        current_seq -= step_size * momentum
+
+        # Occasional smoothing or upsampling
+        if rng.random() < 0.1:
+            current_seq = 0.15 * np.roll(current_seq, 1) + 0.7 * current_seq + 0.15 * np.roll(current_seq, -1)
+        if rng.random() < 0.05 and n < 800:
+            current_seq = np.repeat(current_seq, 2)[:800]
+            momentum = np.zeros(len(current_seq))
+
+        current_seq = _normalize_scale(current_seq)
+        val = evaluate_sequence(current_seq.tolist())
+
+        if val < best_val:
+            best_seq, best_val = current_seq.copy(), val
+        elif rng.random() < 0.1: # Explore
+            pass
+        else: # Greedy descent
+            current_seq = best_seq.copy()
+            momentum *= 0.5
+
+    return [float(x) for x in best_seq.tolist()]
+
+
+# EVOLVE-BLOCK-END

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_10/original.py

@@ -0,0 +1,111 @@
+# EVOLVE-BLOCK-START
+"""Initial search baseline for the AlphaEvolve AC inequality task."""
+
+import time
+import numpy as np
+
+
+def evaluate_sequence(sequence: list[float]) -> float:
+    """
+    Evaluate coefficient sequence with safety checks.
+    Lower is better.
+    """
+    if not isinstance(sequence, list):
+        return float(np.inf)
+    if not sequence:
+        return float(np.inf)
+
+    clean: list[float] = []
+    for x in sequence:
+        if isinstance(x, bool) or not isinstance(x, (int, float)):
+            return float(np.inf)
+        if np.isnan(x) or np.isinf(x):
+            return float(np.inf)
+        clean.append(float(x))
+
+    clean = [max(0.0, min(1000.0, x)) for x in clean]
+    n = len(clean)
+    conv = np.convolve(clean, clean)
+    max_b = float(np.max(conv))
+    sum_a = float(np.sum(clean))
+    if sum_a < 0.01:
+        return float(np.inf)
+    return float(2.0 * n * max_b / (sum_a**2))
+
+
+def _normalize_scale(seq: np.ndarray) -> np.ndarray:
+    """Scale sequence to stable magnitude while keeping nonnegative shape."""
+    s = np.asarray(seq, dtype=np.float64)
+    s = np.clip(s, 0.0, None)
+    total = float(np.sum(s))
+    if total <= 1e-12:
+        return np.ones_like(s)
+    # objective is scale-invariant theoretically, but this keeps numerics stable.
+    return s / total * len(s)
+
+
+def _seed_sequence(rng: np.random.Generator, n: int) -> np.ndarray:
+    x = np.linspace(0.0, 1.0, n, endpoint=False)
+    base = (
+        0.9
+        + 0.30 * np.sin(2.0 * np.pi * x)
+        + 0.12 * np.sin(6.0 * np.pi * x + rng.uniform(0.0, 2.0 * np.pi))
+    )
+    noise = rng.normal(0.0, 0.08, n)
+    return _normalize_scale(base + noise)
+
+
+def run(seed: int = 42, budget_s: float = 10.0, **kwargs) -> list[float]:
+    """
+    Search for a low-value nonnegative sequence.
+
+    Returns:
+        list[float]: candidate coefficient sequence
+    """
+    del kwargs
+
+    rng = np.random.default_rng(seed)
+    start = time.time()
+    deadline = start + max(0.1, budget_s * 0.98)
+
+    # Multi-start initial pool.
+    candidate_lengths = [128, 192, 256, 320, 384]
+    best_seq = _seed_sequence(rng, n=256)
+    best_val = evaluate_sequence(best_seq.tolist())
+
+    for n in candidate_lengths:
+        seq = _seed_sequence(rng, n=n)
+        val = evaluate_sequence(seq.tolist())
+        if val < best_val:
+            best_seq, best_val = seq, val
+
+    # Local stochastic search: block perturbation + smoothing.
+    while time.time() < deadline:
+        seq = best_seq.copy()
+        n = len(seq)
+
+        block = int(rng.integers(max(4, n // 64), max(8, n // 10)))
+        idx = rng.choice(n, size=block, replace=False)
+        seq[idx] += rng.normal(0.0, 0.10, size=block)
+
+        if rng.random() < 0.35:
+            # low-pass smooth
+            seq = (
+                0.25 * np.roll(seq, 1)
+                + 0.50 * seq
+                + 0.25 * np.roll(seq, -1)
+            )
+
+        if rng.random() < 0.15 and n < 800:
+            # upsample once in a while to explore larger dimensions
+            seq = np.repeat(seq, 2)[: min(800, 2 * n)]
+
+        seq = _normalize_scale(seq)
+        val = evaluate_sequence(seq.tolist())
+        if val < best_val:
+            best_seq, best_val = seq, val
+
+    return [float(x) for x in best_seq.tolist()]
+
+
+# EVOLVE-BLOCK-END

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_10/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,3 @@
+def evaluate_aux(results_dir, primary_result=None):
+    """Return auxiliary metrics as a dict."""
+    return {}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_10/results/correct.json

@@ -0,0 +1,4 @@
+{
+    "correct": true,
+    "error": null
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_10/results/metrics.json

@@ -0,0 +1,32 @@
+{
+  "combined_score": -1.7332795776418672,
+  "correct": true,
+  "primary": {
+    "combined_score": -1.7332795776418672,
+    "public": {
+      "best_value": 1.7332795776418672,
+      "best_length": 800,
+      "num_runs": 1
+    },
+    "private": {
+      "all_values": [
+        1.7332795776418672
+      ],
+      "all_lengths": [
+        800
+      ]
+    },
+    "text_feedback": "Lower evaluate_sequence value is better. combined_score = -best_value.",
+    "execution_time_mean": 9.828074638731778,
+    "execution_time_std": 0.0,
+    "num_valid_runs": 1,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": true,
+    "validation_error": null
+  },
+  "auxiliary": {},
+  "auxiliary_descriptions": {},
+  "timestamp": 1770947329.8182533,
+  "generation": 10
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_10/search_replace.txt

@@ -0,0 +1,159 @@
+<NAME>
+gradient_guided_search
+</NAME>
+
+<DESCRIPTION>
+Optimized the search by focusing on a gradient-guided approach to flatten the self-convolution of the sequence.
+1. Re-introduced a refined gradient calculation that targets the peaks of the convolution, which are the main bottlenecks for the evaluator.
+2. Improved initialization with a more diverse set of starting shapes (power-law, U-shape, sine waves).
+3. Used a more aggressive gradient descent loop with momentum to efficiently minimize the objective.
+4. Simplified the normalization step for numerical stability.
+5. The gradient move $a_i \leftarrow a_i - \eta (S a_{k-i} - P)$ directly reduces the maximum of the convolution while maintaining the sum of the sequence.
+6. Multi-start exploration combined with focused local search ensures better coverage of the search space.
+</DESCRIPTION>
+
+<DIFF>
+<<<<<<< SEARCH
+def _seed_sequence(rng: np.random.Generator, n: int) -> np.ndarray:
+    x = np.linspace(0.0, 1.0, n, endpoint=False)
+    base = (
+        0.9
+        + 0.30 * np.sin(2.0 * np.pi * x)
+        + 0.12 * np.sin(6.0 * np.pi * x + rng.uniform(0.0, 2.0 * np.pi))
+    )
+    noise = rng.normal(0.0, 0.08, n)
+    return _normalize_scale(base + noise)
+
+
+def run(seed: int = 42, budget_s: float = 10.0, **kwargs) -> list[float]:
+    """
+    Search for a low-value nonnegative sequence.
+
+    Returns:
+        list[float]: candidate coefficient sequence
+    """
+    del kwargs
+
+    rng = np.random.default_rng(seed)
+    start = time.time()
+    deadline = start + max(0.1, budget_s * 0.98)
+
+    # Multi-start initial pool.
+    candidate_lengths = [128, 192, 256, 320, 384]
+    best_seq = _seed_sequence(rng, n=256)
+    best_val = evaluate_sequence(best_seq.tolist())
+
+    for n in candidate_lengths:
+        seq = _seed_sequence(rng, n=n)
+        val = evaluate_sequence(seq.tolist())
+        if val < best_val:
+            best_seq, best_val = seq, val
+
+    # Local stochastic search: block perturbation + smoothing.
+    while time.time() < deadline:
+        seq = best_seq.copy()
+        n = len(seq)
+
+        block = int(rng.integers(max(4, n // 64), max(8, n // 10)))
+        idx = rng.choice(n, size=block, replace=False)
+        seq[idx] += rng.normal(0.0, 0.10, size=block)
+
+        if rng.random() < 0.35:
+            # low-pass smooth
+            seq = (
+                0.25 * np.roll(seq, 1)
+                + 0.50 * seq
+                + 0.25 * np.roll(seq, -1)
+            )
+
+        if rng.random() < 0.15 and n < 800:
+            # upsample once in a while to explore larger dimensions
+            seq = np.repeat(seq, 2)[: min(800, 2 * n)]
+
+        seq = _normalize_scale(seq)
+        val = evaluate_sequence(seq.tolist())
+        if val < best_val:
+            best_seq, best_val = seq, val
+
+    return [float(x) for x in best_seq.tolist()]
+=======
+def _seed_sequence(rng: np.random.Generator, n: int) -> np.ndarray:
+    """Diverse initializations: sine waves, random, or power laws."""
+    r = rng.random()
+    if r < 0.4:
+        x = np.linspace(0.0, 1.0, n, endpoint=False)
+        base = 1.0 + 0.4 * np.sin(2.0 * np.pi * x * rng.uniform(1, 3))
+    elif r < 0.7:
+        p = rng.uniform(0.3, 0.6)
+        base = (np.arange(n) + 1.0)**(-p)
+    else:
+        p = rng.uniform(0.3, 0.6)
+        base = (np.arange(n) + 1.0)**(-p) + (n - np.arange(n))**(-p)
+    base += rng.normal(0, 0.05, n)
+    return _normalize_scale(base)
+
+
+def run(seed: int = 42, budget_s: float = 10.0, **kwargs) -> list[float]:
+    """Search for a low-value nonnegative sequence using gradient and smoothing moves."""
+    del kwargs
+    rng = np.random.default_rng(seed)
+    start = time.time()
+    deadline = start + max(0.1, budget_s * 0.98)
+
+    candidate_lengths = [128, 256, 512, 768]
+    best_seq = _seed_sequence(rng, 256)
+    best_val = evaluate_sequence(best_seq.tolist())
+
+    for n in candidate_lengths:
+        for _ in range(2):
+            seq = _seed_sequence(rng, n)
+            val = evaluate_sequence(seq.tolist())
+            if val < best_val:
+                best_seq, best_val = seq.copy(), val
+
+    current_seq = best_seq.copy()
+    momentum = np.zeros_like(current_seq)
+
+    while time.time() < deadline:
+        n = len(current_seq)
+        conv = np.convolve(current_seq, current_seq)
+        p_max = np.max(conv)
+        sum_a = np.sum(current_seq)
+
+        # Gradient targeting peaks to flatten the convolution
+        peaks = np.where(conv >= 0.995 * p_max)[0]
+        grad_p = np.zeros(n)
+        for pk in peaks:
+            s_j, e_j = max(0, pk - (n - 1)), min(n - 1, pk)
+            grad_p[s_j : e_j + 1] += current_seq[pk - e_j : pk - s_j + 1][::-1]
+        grad_p /= len(peaks)
+
+        # Combined gradient: dV/da_j proportional to (S * a_rev - P)
+        grad = (sum_a * grad_p - p_max)
+        if len(momentum) != n: momentum = np.zeros(n)
+        momentum = 0.8 * momentum + 0.2 * grad
+
+        step_size = 0.002 + 0.01 * rng.random()
+        current_seq -= step_size * momentum
+
+        # Occasional smoothing or upsampling
+        if rng.random() < 0.1:
+            current_seq = 0.15 * np.roll(current_seq, 1) + 0.7 * current_seq + 0.15 * np.roll(current_seq, -1)
+        if rng.random() < 0.05 and n < 800:
+            current_seq = np.repeat(current_seq, 2)[:800]
+            momentum = np.zeros(len(current_seq))
+
+        current_seq = _normalize_scale(current_seq)
+        val = evaluate_sequence(current_seq.tolist())
+
+        if val < best_val:
+            best_seq, best_val = current_seq.copy(), val
+        elif rng.random() < 0.1: # Explore
+            pass
+        else: # Greedy descent
+            current_seq = best_seq.copy()
+            momentum *= 0.5
+
+    return [float(x) for x in best_seq.tolist()]
+>>>>>>> REPLACE
+</DIFF>

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_100/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,3 @@
+def evaluate_aux(results_dir, primary_result=None):
+    """Return auxiliary metrics as a dict."""
+    return {}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_100/results/correct.json

@@ -0,0 +1,4 @@
+{
+    "correct": true,
+    "error": null
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_100/results/metrics.json

@@ -0,0 +1,32 @@
+{
+  "combined_score": -1.6159124140034282,
+  "correct": true,
+  "primary": {
+    "combined_score": -1.6159124140034282,
+    "public": {
+      "best_value": 1.6159124140034282,
+      "best_length": 768,
+      "num_runs": 1
+    },
+    "private": {
+      "all_values": [
+        1.6159124140034282
+      ],
+      "all_lengths": [
+        768
+      ]
+    },
+    "text_feedback": "Lower evaluate_sequence value is better. combined_score = -best_value.",
+    "execution_time_mean": 9.624990861862898,
+    "execution_time_std": 0.0,
+    "num_valid_runs": 1,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": true,
+    "validation_error": null
+  },
+  "auxiliary": {},
+  "auxiliary_descriptions": {},
+  "timestamp": 1770954621.617363,
+  "generation": 100
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_101/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,3 @@
+def evaluate_aux(results_dir, primary_result=None):
+    """Return auxiliary metrics as a dict."""
+    return {}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_101/results/correct.json

@@ -0,0 +1,4 @@
+{
+    "correct": true,
+    "error": null
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_101/results/metrics.json

@@ -0,0 +1,32 @@
+{
+  "combined_score": -1.706869938583198,
+  "correct": true,
+  "primary": {
+    "combined_score": -1.706869938583198,
+    "public": {
+      "best_value": 1.706869938583198,
+      "best_length": 768,
+      "num_runs": 1
+    },
+    "private": {
+      "all_values": [
+        1.706869938583198
+      ],
+      "all_lengths": [
+        768
+      ]
+    },
+    "text_feedback": "Lower evaluate_sequence value is better. combined_score = -best_value.",
+    "execution_time_mean": 9.829864730127156,
+    "execution_time_std": 0.0,
+    "num_valid_runs": 1,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": true,
+    "validation_error": null
+  },
+  "auxiliary": {},
+  "auxiliary_descriptions": {},
+  "timestamp": 1770954700.8218436,
+  "generation": 101
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_102/edit.diff

@@ -0,0 +1,276 @@
+--- a/original.py
++++ b/original.py
+@@ -1,182 +1,169 @@
+ # EVOLVE-BLOCK-START
+-"""Initial search baseline for the AlphaEvolve AC inequality task."""
+-
+-import time
+-import numpy as np
+-
++"""
++Surgical gradient refinement for the AlphaEvolve AC inequality task.
++Optimizes a non-negative sequence to minimize 2*n*max(convolve(a,a)) / (sum(a)^2).
++"""
+ 
+ def evaluate_sequence(sequence: list[float]) -> float:
+     """
+     Evaluate coefficient sequence with safety checks.
+     Lower is better.
+     """
+-    if not isinstance(sequence, list):
+-        return float(np.inf)
+-    if not sequence:
++    if not isinstance(sequence, list) or not sequence:
+         return float(np.inf)
+ 
+     clean: list[float] = []
+     for x in sequence:
+         if isinstance(x, bool) or not isinstance(x, (int, float)):
+             return float(np.inf)
+         if np.isnan(x) or np.isinf(x):
+             return float(np.inf)
+         clean.append(float(x))
+ 
+     clean = [max(0.0, min(1000.0, x)) for x in clean]
+     n = len(clean)
+     conv = np.convolve(clean, clean)
+     max_b = float(np.max(conv))
+     sum_a = float(np.sum(clean))
+-    if sum_a < 0.01:
++    if sum_a < 1e-6:
+         return float(np.inf)
+     return float(2.0 * n * max_b / (sum_a**2))
+ 
+ 
+ def _normalize_scale(seq: np.ndarray) -> np.ndarray:
+-    """Scale sequence to sum to n while maintaining nonnegativity."""
+-    s = np.clip(np.asarray(seq, dtype=np.float64), 0.0, None)
++    """Scale sequence to sum to its length while maintaining nonnegativity."""
++    s = np.clip(np.asarray(seq, dtype=np.float64), 0.0, 1000.0)
+     total = np.sum(s)
+     if total <= 1e-12:
+         return np.ones_like(s)
+     return s * (len(s) / total)
+ 
+ 
+ def _seed_sequence(rng: np.random.Generator, n: int) -> np.ndarray:
+-    """Diverse seeds focusing on power laws and structured hills."""
++    """Seeds focusing on power-laws and symmetric hills."""
+     x = np.linspace(0.0, 1.0, n, endpoint=True)
+     mode = rng.integers(0, 6)
+     if mode == 0:
+         base = np.ones(n)
+-    elif mode == 1: # Symmetric Power Law (Promising structure for AC inequality)
+-        p = rng.uniform(0.42, 0.58)
++    elif mode == 1: # Symmetric Power Law (Ideal structure for this task)
++        p = rng.uniform(0.45, 0.55)
+         base = (np.arange(n) + 1.0)**(-p) + (n - np.arange(n))**(-p)
+-    elif mode == 2: # Asymmetric Power Law
+-        p = rng.uniform(0.42, 0.58)
++    elif mode == 2: # Root-sine hill
++        base = np.sqrt(np.sin(np.pi * x) + 0.1)
++    elif mode == 3: # Asymmetric Power Law
++        p = rng.uniform(0.45, 0.55)
+         base = (np.arange(n) + 1.0)**(-p)
+-    elif mode == 3: # Sine Hill
+-        base = 1.0 + 0.6 * np.sin(np.pi * x)
+     elif mode == 4: # Gaussian
+-        base = np.exp(-0.5 * ((x - 0.5) / 0.18)**2)
+-    else: # Quadratic Bowl
+-        base = 1.0 + 0.6 * (x - 0.5)**2
++        base = np.exp(-0.5 * ((x - 0.5) / 0.2)**2)
++    else: # Symmetric 'Bowl'
++        base = 1.0 + 0.5 * (x - 0.5)**2
+ 
+-    if rng.random() < 0.3:
+-        base += 0.1 * rng.normal(0, 1, n)
+-
+-    seq = base + rng.normal(0.0, 0.02, n)
+-    if rng.random() < 0.5:
++    noise = rng.normal(0.0, 0.02, n)
++    seq = base + noise
++    if rng.random() < 0.7:
+         seq = (seq + seq[::-1]) / 2.0
+     return _normalize_scale(seq)
+ 
+ 
+ def run(seed: int = 42, budget_s: float = 10.0, **kwargs) -> list[float]:
+-    """Search for a low-value nonnegative sequence using a sensitivity-based gradient and targeted moves."""
+-    del kwargs
++    """Search for optimal sequence using exponentiated gradient moves and resolution scaling."""
+     rng = np.random.default_rng(seed)
+     start_time = time.time()
+     deadline = start_time + max(0.1, budget_s * 0.98)
+ 
++    # Initial seeding phase at low resolution
+     best_seq = _normalize_scale(np.ones(128))
+-    best_val = 2.0 * np.max(np.convolve(best_seq, best_seq)) / best_seq.size
++    best_val = evaluate_sequence(best_seq.tolist())
+ 
+-    # Seeding
+-    for n_init in [128, 256, 512]:
++    for n_init in [128, 256, 384]:
+         for _ in range(8):
+             seq = _seed_sequence(rng, n_init)
+-            v = 2.0 * np.max(np.convolve(seq, seq)) / seq.size
+-            if v < best_val:
+-                best_seq, best_val = seq, v
++            val = evaluate_sequence(seq.tolist())
++            if val < best_val:
++                best_seq, best_val = seq, val
+ 
++    # Multi-resolution optimization
+     resolutions = [128, 256, 512, 768]
+-    time_shares = [0.08, 0.20, 0.45, 1.0]
++    time_shares = [0.06, 0.18, 0.42, 1.0] # Skew time heavily toward final res
++    
+     for idx, target_n in enumerate(resolutions):
+-        if target_n > best_seq.size:
++        if target_n != best_seq.size:
+             best_seq = np.interp(np.linspace(0, 1, target_n), np.linspace(0, 1, best_seq.size), best_seq)
+             best_seq = _normalize_scale(best_seq)
+-            best_val = 2.0 * np.max(np.convolve(best_seq, best_seq)) / best_seq.size
++            best_val = evaluate_sequence(best_seq.tolist())
+ 
+         res_deadline = start_time + time_shares[idx] * (deadline - start_time)
+         while time.time() < res_deadline:
+             progress = (time.time() - start_time) / (deadline - start_time + 1e-9)
+-            sigma = 0.15 * (1.0 - progress)**1.1
++            # sigma decays as refinement progresses
++            sigma = 0.20 * (1.0 - progress)**1.1
+             seq = best_seq.copy()
+             n = seq.size
+             was_symmetric = np.allclose(seq, seq[::-1], atol=1e-8)
++            
+             m_type = rng.random()
+-
+-            if m_type < 0.08: # Block perturbation
++            if m_type < 0.85: # Surgical Exponentiated Gradient Move
++                c = np.convolve(seq, seq)
++                max_c = np.max(c)
++                sum_a = np.sum(seq)
++                
++                # Sharpen peak focus over time
++                beta = 50.0 + 450.0 * (progress**1.2)
++                # Compute softmax weights of the convolution peaks
++                # Subtracting max(beta*(...)) for numerical stability
++                w = np.exp(beta * (c / (max_c + 1e-12) - 1.0))
++                w /= (np.sum(w) + 1e-15)
++                
++                # Gradient of sum(w*C) with respect to sequence 'a'
++                grad = np.convolve(w, seq[::-1], mode='valid')
++                
++                # rel_grad_log is the gradient of the log-objective with respect to log(a_i)
++                # d log(max_c)/d log(a_i) = (a_i/max_c) * (2*grad_i)
++                # d log(sum_a^2)/d log(a_i) = (a_i/(sum_a^2)) * (2*sum_a) = 2*a_i/sum_a
++                rel_grad_log = (grad * seq / (max_c + 1e-12)) - (seq / (sum_a + 1e-12))
++                
++                # Exponentiated gradient step
++                step_size = 0.8 * sigma
++                seq *= np.exp(-step_size * rel_grad_log * n)
++                
++            elif m_type < 0.92: # Spectral Smoothing and Low-Frequency Jitter
++                f = np.fft.rfft(seq)
++                # Suppress high-frequency noise that doesn't help flatten convolution
++                cutoff = max(1, int(len(f) * (0.15 + 0.1 * (1.0 - progress))))
++                f[cutoff:] *= (1.0 - 0.5 * sigma)
++                # Small low-frequency perturbation for global shape exploration
++                low_idx = rng.integers(1, cutoff, size=rng.integers(1, 3))
++                f[low_idx] *= (1.0 + rng.normal(0, 0.05 * sigma, size=len(low_idx)))
++                seq = np.fft.irfft(f, n=n)
++                
++            elif m_type < 0.97: # Symmetry-aware Block Perturbation
+                 blen = rng.integers(2, max(5, n // 10))
+                 bstart = rng.integers(0, n - blen)
+-                delta = rng.normal(0, sigma, size=blen)
++                delta = rng.normal(0, 0.5 * sigma, size=blen)
+                 seq[bstart : bstart + blen] += delta
+-                if was_symmetric: seq[n - bstart - blen : n - bstart] += delta[::-1]
+-            elif m_type < 0.15: # Scattered perturbation
+-                num = rng.integers(1, max(2, n // 30))
+-                pix = rng.choice(n, size=num, replace=False)
+-                seq[pix] += rng.normal(0, sigma, size=num)
+-            elif m_type < 0.55: # Unified Super-Sharpened Gradient Step
+-                c = np.convolve(seq, seq)
+-                max_c, sum_a = np.max(c), float(np.sum(seq))
+-                beta = 40.0 + 360.0 * (progress**1.5)
+-                w = np.exp(beta * (c / (max_c + 1e-12) - 1.0))
+-                w /= (np.sum(w) + 1e-15)
+-                grad = np.convolve(w, seq[::-1], mode='full')[n-1 : 2*n-1]
+-                rel_grad = (grad / (max_c + 1e-12)) - (1.0 / (sum_a + 1e-12))
+-                t_size = rng.integers(1, max(2, n // 20))
+-                if rng.random() < 0.6: # Reduction of peak-contributing indices
+-                    target_idx = np.argsort(rel_grad)[-t_size:]
+-                    seq[target_idx] *= (1.0 - sigma * rng.uniform(0.1, 0.5, size=len(target_idx)))
+-                else: # Boost of efficient indices
+-                    target_idx = np.argsort(rel_grad)[:t_size]
+-                    seq[target_idx] *= (1.0 + sigma * rng.uniform(0.1, 0.5, size=len(target_idx)))
+-            elif m_type < 0.65: # Envelope Smoothing
+-                seq = 0.8 * seq + 0.1 * (np.roll(seq, 1) + np.roll(seq, -1))
+-            elif m_type < 0.80: # Peak Pressure Move
+-                c = np.convolve(seq, seq)
+-                p_idx = np.argmax(c)
+-                i_range = np.arange(max(0, p_idx - n + 1), min(n, p_idx + 1))
+-                contrib = seq[i_range] * seq[p_idx - i_range]
+-                t_size = rng.integers(1, max(2, n // 30))
+-                target_idx = i_range[np.argsort(contrib)[-t_size:]]
+-                seq[target_idx] *= (1.0 - sigma)
+-            elif m_type < 0.88: # Coupled Peak-to-Gap Mass Migration
+-                c = np.convolve(seq, seq)
+-                max_c = np.max(c)
+-                w = np.exp(100.0 * (c / (max_c + 1e-12) - 1.0))
+-                grad = np.convolve(w, seq[::-1], mode='full')[n-1 : 2*n-1]
+-                idx_red = np.argsort(grad)[-rng.integers(1, 6):]
+-                idx_bst = np.argsort(grad)[:rng.integers(1, 6)]
+-                for r_idx in idx_red:
+-                    transfer = seq[r_idx] * sigma * rng.uniform(0.1, 0.4)
+-                    seq[r_idx] -= transfer
+-                    seq[rng.choice(idx_bst)] += transfer
+-            elif m_type < 0.94: # Selective Spectral Damping
+-                f = np.fft.rfft(seq)
+-                cutoff = max(1, int(len(f) * 0.15))
+-                f[cutoff:] *= (1.0 - 0.5 * sigma)
+-                low_idx = rng.integers(1, cutoff, size=rng.integers(1, 4))
+-                f[low_idx] *= (1.0 + rng.normal(0, 0.1 * sigma, size=len(low_idx)))
+-                seq = np.fft.irfft(f, n=n)
+-            elif m_type < 0.98: # Local scaling
+-                blen = rng.integers(5, max(10, n // 6))
+-                bstart = rng.integers(0, n - blen)
+-                seq[bstart : bstart + blen] *= rng.uniform(1.0 - sigma, 1.0 + sigma)
+-            else: # Symmetry mirror
+-                mid = n // 2
+-                if rng.random() < 0.5: seq[mid:] = seq[:mid][::-1] if n % 2 == 0 else seq[:mid+1][::-1]
+-                else: seq[:mid] = seq[n-mid:][::-1]
++                if was_symmetric or rng.random() < 0.5:
++                    seq[n - bstart - blen : n - bstart] += delta[::-1]
++            
++            else: # Mirror sequence to enforce symmetry early
++                if rng.random() < 0.5:
++                    seq[n//2:] = seq[:n-n//2][::-1]
++                else:
++                    seq[:n//2] = seq[n-n//2:][::-1]
+ 
+-            if (was_symmetric or progress > 0.88) and m_type < 0.98:
+-                if progress > 0.92 or rng.random() < (0.7 + 0.3 * progress):
+-                    seq = (seq + seq[::-1]) / 2.0
++            # Symmetry hardening in the final phase
++            if progress > 0.88:
++                seq = (seq + seq[::-1]) / 2.0
++            elif was_symmetric and m_type < 0.98:
++                seq = (seq + seq[::-1]) / 2.0
+ 
+             seq = _normalize_scale(seq)
+-            v = 2.0 * np.max(np.convolve(seq, seq)) / n
+-            if v < best_val:
+-                best_seq, best_val = seq, v
++            # Efficient evaluation of scale-invariant part
++            new_c = np.convolve(seq, seq)
++            val = 2.0 * n * np.max(new_c) / (np.sum(seq)**2)
++            
++            if val < best_val:
++                best_seq, best_val = seq, val
+ 
+     return [float(x) for x in best_seq.tolist()]
+ 
+-
+ # EVOLVE-BLOCK-END

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_102/main.py

@@ -0,0 +1,169 @@
+# EVOLVE-BLOCK-START
+"""
+Surgical gradient refinement for the AlphaEvolve AC inequality task.
+Optimizes a non-negative sequence to minimize 2*n*max(convolve(a,a)) / (sum(a)^2).
+"""
+
+def evaluate_sequence(sequence: list[float]) -> float:
+    """
+    Evaluate coefficient sequence with safety checks.
+    Lower is better.
+    """
+    if not isinstance(sequence, list) or not sequence:
+        return float(np.inf)
+
+    clean: list[float] = []
+    for x in sequence:
+        if isinstance(x, bool) or not isinstance(x, (int, float)):
+            return float(np.inf)
+        if np.isnan(x) or np.isinf(x):
+            return float(np.inf)
+        clean.append(float(x))
+
+    clean = [max(0.0, min(1000.0, x)) for x in clean]
+    n = len(clean)
+    conv = np.convolve(clean, clean)
+    max_b = float(np.max(conv))
+    sum_a = float(np.sum(clean))
+    if sum_a < 1e-6:
+        return float(np.inf)
+    return float(2.0 * n * max_b / (sum_a**2))
+
+
+def _normalize_scale(seq: np.ndarray) -> np.ndarray:
+    """Scale sequence to sum to its length while maintaining nonnegativity."""
+    s = np.clip(np.asarray(seq, dtype=np.float64), 0.0, 1000.0)
+    total = np.sum(s)
+    if total <= 1e-12:
+        return np.ones_like(s)
+    return s * (len(s) / total)
+
+
+def _seed_sequence(rng: np.random.Generator, n: int) -> np.ndarray:
+    """Seeds focusing on power-laws and symmetric hills."""
+    x = np.linspace(0.0, 1.0, n, endpoint=True)
+    mode = rng.integers(0, 6)
+    if mode == 0:
+        base = np.ones(n)
+    elif mode == 1: # Symmetric Power Law (Ideal structure for this task)
+        p = rng.uniform(0.45, 0.55)
+        base = (np.arange(n) + 1.0)**(-p) + (n - np.arange(n))**(-p)
+    elif mode == 2: # Root-sine hill
+        base = np.sqrt(np.sin(np.pi * x) + 0.1)
+    elif mode == 3: # Asymmetric Power Law
+        p = rng.uniform(0.45, 0.55)
+        base = (np.arange(n) + 1.0)**(-p)
+    elif mode == 4: # Gaussian
+        base = np.exp(-0.5 * ((x - 0.5) / 0.2)**2)
+    else: # Symmetric 'Bowl'
+        base = 1.0 + 0.5 * (x - 0.5)**2
+
+    noise = rng.normal(0.0, 0.02, n)
+    seq = base + noise
+    if rng.random() < 0.7:
+        seq = (seq + seq[::-1]) / 2.0
+    return _normalize_scale(seq)
+
+
+def run(seed: int = 42, budget_s: float = 10.0, **kwargs) -> list[float]:
+    """Search for optimal sequence using exponentiated gradient moves and resolution scaling."""
+    rng = np.random.default_rng(seed)
+    start_time = time.time()
+    deadline = start_time + max(0.1, budget_s * 0.98)
+
+    # Initial seeding phase at low resolution
+    best_seq = _normalize_scale(np.ones(128))
+    best_val = evaluate_sequence(best_seq.tolist())
+
+    for n_init in [128, 256, 384]:
+        for _ in range(8):
+            seq = _seed_sequence(rng, n_init)
+            val = evaluate_sequence(seq.tolist())
+            if val < best_val:
+                best_seq, best_val = seq, val
+
+    # Multi-resolution optimization
+    resolutions = [128, 256, 512, 768]
+    time_shares = [0.06, 0.18, 0.42, 1.0] # Skew time heavily toward final res
+    
+    for idx, target_n in enumerate(resolutions):
+        if target_n != best_seq.size:
+            best_seq = np.interp(np.linspace(0, 1, target_n), np.linspace(0, 1, best_seq.size), best_seq)
+            best_seq = _normalize_scale(best_seq)
+            best_val = evaluate_sequence(best_seq.tolist())
+
+        res_deadline = start_time + time_shares[idx] * (deadline - start_time)
+        while time.time() < res_deadline:
+            progress = (time.time() - start_time) / (deadline - start_time + 1e-9)
+            # sigma decays as refinement progresses
+            sigma = 0.20 * (1.0 - progress)**1.1
+            seq = best_seq.copy()
+            n = seq.size
+            was_symmetric = np.allclose(seq, seq[::-1], atol=1e-8)
+            
+            m_type = rng.random()
+            if m_type < 0.85: # Surgical Exponentiated Gradient Move
+                c = np.convolve(seq, seq)
+                max_c = np.max(c)
+                sum_a = np.sum(seq)
+                
+                # Sharpen peak focus over time
+                beta = 50.0 + 450.0 * (progress**1.2)
+                # Compute softmax weights of the convolution peaks
+                # Subtracting max(beta*(...)) for numerical stability
+                w = np.exp(beta * (c / (max_c + 1e-12) - 1.0))
+                w /= (np.sum(w) + 1e-15)
+                
+                # Gradient of sum(w*C) with respect to sequence 'a'
+                grad = np.convolve(w, seq[::-1], mode='valid')
+                
+                # rel_grad_log is the gradient of the log-objective with respect to log(a_i)
+                # d log(max_c)/d log(a_i) = (a_i/max_c) * (2*grad_i)
+                # d log(sum_a^2)/d log(a_i) = (a_i/(sum_a^2)) * (2*sum_a) = 2*a_i/sum_a
+                rel_grad_log = (grad * seq / (max_c + 1e-12)) - (seq / (sum_a + 1e-12))
+                
+                # Exponentiated gradient step
+                step_size = 0.8 * sigma
+                seq *= np.exp(-step_size * rel_grad_log * n)
+                
+            elif m_type < 0.92: # Spectral Smoothing and Low-Frequency Jitter
+                f = np.fft.rfft(seq)
+                # Suppress high-frequency noise that doesn't help flatten convolution
+                cutoff = max(1, int(len(f) * (0.15 + 0.1 * (1.0 - progress))))
+                f[cutoff:] *= (1.0 - 0.5 * sigma)
+                # Small low-frequency perturbation for global shape exploration
+                low_idx = rng.integers(1, cutoff, size=rng.integers(1, 3))
+                f[low_idx] *= (1.0 + rng.normal(0, 0.05 * sigma, size=len(low_idx)))
+                seq = np.fft.irfft(f, n=n)
+                
+            elif m_type < 0.97: # Symmetry-aware Block Perturbation
+                blen = rng.integers(2, max(5, n // 10))
+                bstart = rng.integers(0, n - blen)
+                delta = rng.normal(0, 0.5 * sigma, size=blen)
+                seq[bstart : bstart + blen] += delta
+                if was_symmetric or rng.random() < 0.5:
+                    seq[n - bstart - blen : n - bstart] += delta[::-1]
+            
+            else: # Mirror sequence to enforce symmetry early
+                if rng.random() < 0.5:
+                    seq[n//2:] = seq[:n-n//2][::-1]
+                else:
+                    seq[:n//2] = seq[n-n//2:][::-1]
+
+            # Symmetry hardening in the final phase
+            if progress > 0.88:
+                seq = (seq + seq[::-1]) / 2.0
+            elif was_symmetric and m_type < 0.98:
+                seq = (seq + seq[::-1]) / 2.0
+
+            seq = _normalize_scale(seq)
+            # Efficient evaluation of scale-invariant part
+            new_c = np.convolve(seq, seq)
+            val = 2.0 * n * np.max(new_c) / (np.sum(seq)**2)
+            
+            if val < best_val:
+                best_seq, best_val = seq, val
+
+    return [float(x) for x in best_seq.tolist()]
+
+# EVOLVE-BLOCK-END

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_102/original.py

@@ -0,0 +1,182 @@
+# EVOLVE-BLOCK-START
+"""Initial search baseline for the AlphaEvolve AC inequality task."""
+
+import time
+import numpy as np
+
+
+def evaluate_sequence(sequence: list[float]) -> float:
+    """
+    Evaluate coefficient sequence with safety checks.
+    Lower is better.
+    """
+    if not isinstance(sequence, list):
+        return float(np.inf)
+    if not sequence:
+        return float(np.inf)
+
+    clean: list[float] = []
+    for x in sequence:
+        if isinstance(x, bool) or not isinstance(x, (int, float)):
+            return float(np.inf)
+        if np.isnan(x) or np.isinf(x):
+            return float(np.inf)
+        clean.append(float(x))
+
+    clean = [max(0.0, min(1000.0, x)) for x in clean]
+    n = len(clean)
+    conv = np.convolve(clean, clean)
+    max_b = float(np.max(conv))
+    sum_a = float(np.sum(clean))
+    if sum_a < 0.01:
+        return float(np.inf)
+    return float(2.0 * n * max_b / (sum_a**2))
+
+
+def _normalize_scale(seq: np.ndarray) -> np.ndarray:
+    """Scale sequence to sum to n while maintaining nonnegativity."""
+    s = np.clip(np.asarray(seq, dtype=np.float64), 0.0, None)
+    total = np.sum(s)
+    if total <= 1e-12:
+        return np.ones_like(s)
+    return s * (len(s) / total)
+
+
+def _seed_sequence(rng: np.random.Generator, n: int) -> np.ndarray:
+    """Diverse seeds focusing on power laws and structured hills."""
+    x = np.linspace(0.0, 1.0, n, endpoint=True)
+    mode = rng.integers(0, 6)
+    if mode == 0:
+        base = np.ones(n)
+    elif mode == 1: # Symmetric Power Law (Promising structure for AC inequality)
+        p = rng.uniform(0.42, 0.58)
+        base = (np.arange(n) + 1.0)**(-p) + (n - np.arange(n))**(-p)
+    elif mode == 2: # Asymmetric Power Law
+        p = rng.uniform(0.42, 0.58)
+        base = (np.arange(n) + 1.0)**(-p)
+    elif mode == 3: # Sine Hill
+        base = 1.0 + 0.6 * np.sin(np.pi * x)
+    elif mode == 4: # Gaussian
+        base = np.exp(-0.5 * ((x - 0.5) / 0.18)**2)
+    else: # Quadratic Bowl
+        base = 1.0 + 0.6 * (x - 0.5)**2
+
+    if rng.random() < 0.3:
+        base += 0.1 * rng.normal(0, 1, n)
+
+    seq = base + rng.normal(0.0, 0.02, n)
+    if rng.random() < 0.5:
+        seq = (seq + seq[::-1]) / 2.0
+    return _normalize_scale(seq)
+
+
+def run(seed: int = 42, budget_s: float = 10.0, **kwargs) -> list[float]:
+    """Search for a low-value nonnegative sequence using a sensitivity-based gradient and targeted moves."""
+    del kwargs
+    rng = np.random.default_rng(seed)
+    start_time = time.time()
+    deadline = start_time + max(0.1, budget_s * 0.98)
+
+    best_seq = _normalize_scale(np.ones(128))
+    best_val = 2.0 * np.max(np.convolve(best_seq, best_seq)) / best_seq.size
+
+    # Seeding
+    for n_init in [128, 256, 512]:
+        for _ in range(8):
+            seq = _seed_sequence(rng, n_init)
+            v = 2.0 * np.max(np.convolve(seq, seq)) / seq.size
+            if v < best_val:
+                best_seq, best_val = seq, v
+
+    resolutions = [128, 256, 512, 768]
+    time_shares = [0.08, 0.20, 0.45, 1.0]
+    for idx, target_n in enumerate(resolutions):
+        if target_n > best_seq.size:
+            best_seq = np.interp(np.linspace(0, 1, target_n), np.linspace(0, 1, best_seq.size), best_seq)
+            best_seq = _normalize_scale(best_seq)
+            best_val = 2.0 * np.max(np.convolve(best_seq, best_seq)) / best_seq.size
+
+        res_deadline = start_time + time_shares[idx] * (deadline - start_time)
+        while time.time() < res_deadline:
+            progress = (time.time() - start_time) / (deadline - start_time + 1e-9)
+            sigma = 0.15 * (1.0 - progress)**1.1
+            seq = best_seq.copy()
+            n = seq.size
+            was_symmetric = np.allclose(seq, seq[::-1], atol=1e-8)
+            m_type = rng.random()
+
+            if m_type < 0.08: # Block perturbation
+                blen = rng.integers(2, max(5, n // 10))
+                bstart = rng.integers(0, n - blen)
+                delta = rng.normal(0, sigma, size=blen)
+                seq[bstart : bstart + blen] += delta
+                if was_symmetric: seq[n - bstart - blen : n - bstart] += delta[::-1]
+            elif m_type < 0.15: # Scattered perturbation
+                num = rng.integers(1, max(2, n // 30))
+                pix = rng.choice(n, size=num, replace=False)
+                seq[pix] += rng.normal(0, sigma, size=num)
+            elif m_type < 0.55: # Unified Super-Sharpened Gradient Step
+                c = np.convolve(seq, seq)
+                max_c, sum_a = np.max(c), float(np.sum(seq))
+                beta = 40.0 + 360.0 * (progress**1.5)
+                w = np.exp(beta * (c / (max_c + 1e-12) - 1.0))
+                w /= (np.sum(w) + 1e-15)
+                grad = np.convolve(w, seq[::-1], mode='full')[n-1 : 2*n-1]
+                rel_grad = (grad / (max_c + 1e-12)) - (1.0 / (sum_a + 1e-12))
+                t_size = rng.integers(1, max(2, n // 20))
+                if rng.random() < 0.6: # Reduction of peak-contributing indices
+                    target_idx = np.argsort(rel_grad)[-t_size:]
+                    seq[target_idx] *= (1.0 - sigma * rng.uniform(0.1, 0.5, size=len(target_idx)))
+                else: # Boost of efficient indices
+                    target_idx = np.argsort(rel_grad)[:t_size]
+                    seq[target_idx] *= (1.0 + sigma * rng.uniform(0.1, 0.5, size=len(target_idx)))
+            elif m_type < 0.65: # Envelope Smoothing
+                seq = 0.8 * seq + 0.1 * (np.roll(seq, 1) + np.roll(seq, -1))
+            elif m_type < 0.80: # Peak Pressure Move
+                c = np.convolve(seq, seq)
+                p_idx = np.argmax(c)
+                i_range = np.arange(max(0, p_idx - n + 1), min(n, p_idx + 1))
+                contrib = seq[i_range] * seq[p_idx - i_range]
+                t_size = rng.integers(1, max(2, n // 30))
+                target_idx = i_range[np.argsort(contrib)[-t_size:]]
+                seq[target_idx] *= (1.0 - sigma)
+            elif m_type < 0.88: # Coupled Peak-to-Gap Mass Migration
+                c = np.convolve(seq, seq)
+                max_c = np.max(c)
+                w = np.exp(100.0 * (c / (max_c + 1e-12) - 1.0))
+                grad = np.convolve(w, seq[::-1], mode='full')[n-1 : 2*n-1]
+                idx_red = np.argsort(grad)[-rng.integers(1, 6):]
+                idx_bst = np.argsort(grad)[:rng.integers(1, 6)]
+                for r_idx in idx_red:
+                    transfer = seq[r_idx] * sigma * rng.uniform(0.1, 0.4)
+                    seq[r_idx] -= transfer
+                    seq[rng.choice(idx_bst)] += transfer
+            elif m_type < 0.94: # Selective Spectral Damping
+                f = np.fft.rfft(seq)
+                cutoff = max(1, int(len(f) * 0.15))
+                f[cutoff:] *= (1.0 - 0.5 * sigma)
+                low_idx = rng.integers(1, cutoff, size=rng.integers(1, 4))
+                f[low_idx] *= (1.0 + rng.normal(0, 0.1 * sigma, size=len(low_idx)))
+                seq = np.fft.irfft(f, n=n)
+            elif m_type < 0.98: # Local scaling
+                blen = rng.integers(5, max(10, n // 6))
+                bstart = rng.integers(0, n - blen)
+                seq[bstart : bstart + blen] *= rng.uniform(1.0 - sigma, 1.0 + sigma)
+            else: # Symmetry mirror
+                mid = n // 2
+                if rng.random() < 0.5: seq[mid:] = seq[:mid][::-1] if n % 2 == 0 else seq[:mid+1][::-1]
+                else: seq[:mid] = seq[n-mid:][::-1]
+
+            if (was_symmetric or progress > 0.88) and m_type < 0.98:
+                if progress > 0.92 or rng.random() < (0.7 + 0.3 * progress):
+                    seq = (seq + seq[::-1]) / 2.0
+
+            seq = _normalize_scale(seq)
+            v = 2.0 * np.max(np.convolve(seq, seq)) / n
+            if v < best_val:
+                best_seq, best_val = seq, v
+
+    return [float(x) for x in best_seq.tolist()]
+
+
+# EVOLVE-BLOCK-END

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_102/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,3 @@
+def evaluate_aux(results_dir, primary_result=None):
+    """Return auxiliary metrics as a dict."""
+    return {}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_102/results/correct.json

@@ -0,0 +1,4 @@
+{
+    "correct": false,
+    "error": "name 'np' is not defined"
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_102/results/metrics.json

@@ -0,0 +1,19 @@
+{
+  "combined_score": 0.0,
+  "correct": false,
+  "primary": {
+    "combined_score": 0.0,
+    "execution_time_mean": 0.0,
+    "execution_time_std": 0.0,
+    "num_successful_runs": 0,
+    "num_valid_runs": 0,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": false,
+    "validation_error": "name 'np' is not defined"
+  },
+  "auxiliary": {},
+  "auxiliary_descriptions": {},
+  "timestamp": 1770954796.5973926,
+  "generation": 102
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_102/rewrite.txt

@@ -0,0 +1,172 @@
+import time
+import numpy as np
+
+# EVOLVE-BLOCK-START
+"""
+Surgical gradient refinement for the AlphaEvolve AC inequality task.
+Optimizes a non-negative sequence to minimize 2*n*max(convolve(a,a)) / (sum(a)^2).
+"""
+
+def evaluate_sequence(sequence: list[float]) -> float:
+    """
+    Evaluate coefficient sequence with safety checks.
+    Lower is better.
+    """
+    if not isinstance(sequence, list) or not sequence:
+        return float(np.inf)
+
+    clean: list[float] = []
+    for x in sequence:
+        if isinstance(x, bool) or not isinstance(x, (int, float)):
+            return float(np.inf)
+        if np.isnan(x) or np.isinf(x):
+            return float(np.inf)
+        clean.append(float(x))
+
+    clean = [max(0.0, min(1000.0, x)) for x in clean]
+    n = len(clean)
+    conv = np.convolve(clean, clean)
+    max_b = float(np.max(conv))
+    sum_a = float(np.sum(clean))
+    if sum_a < 1e-6:
+        return float(np.inf)
+    return float(2.0 * n * max_b / (sum_a**2))
+
+
+def _normalize_scale(seq: np.ndarray) -> np.ndarray:
+    """Scale sequence to sum to its length while maintaining nonnegativity."""
+    s = np.clip(np.asarray(seq, dtype=np.float64), 0.0, 1000.0)
+    total = np.sum(s)
+    if total <= 1e-12:
+        return np.ones_like(s)
+    return s * (len(s) / total)
+
+
+def _seed_sequence(rng: np.random.Generator, n: int) -> np.ndarray:
+    """Seeds focusing on power-laws and symmetric hills."""
+    x = np.linspace(0.0, 1.0, n, endpoint=True)
+    mode = rng.integers(0, 6)
+    if mode == 0:
+        base = np.ones(n)
+    elif mode == 1: # Symmetric Power Law (Ideal structure for this task)
+        p = rng.uniform(0.45, 0.55)
+        base = (np.arange(n) + 1.0)**(-p) + (n - np.arange(n))**(-p)
+    elif mode == 2: # Root-sine hill
+        base = np.sqrt(np.sin(np.pi * x) + 0.1)
+    elif mode == 3: # Asymmetric Power Law
+        p = rng.uniform(0.45, 0.55)
+        base = (np.arange(n) + 1.0)**(-p)
+    elif mode == 4: # Gaussian
+        base = np.exp(-0.5 * ((x - 0.5) / 0.2)**2)
+    else: # Symmetric 'Bowl'
+        base = 1.0 + 0.5 * (x - 0.5)**2
+
+    noise = rng.normal(0.0, 0.02, n)
+    seq = base + noise
+    if rng.random() < 0.7:
+        seq = (seq + seq[::-1]) / 2.0
+    return _normalize_scale(seq)
+
+
+def run(seed: int = 42, budget_s: float = 10.0, **kwargs) -> list[float]:
+    """Search for optimal sequence using exponentiated gradient moves and resolution scaling."""
+    rng = np.random.default_rng(seed)
+    start_time = time.time()
+    deadline = start_time + max(0.1, budget_s * 0.98)
+
+    # Initial seeding phase at low resolution
+    best_seq = _normalize_scale(np.ones(128))
+    best_val = evaluate_sequence(best_seq.tolist())
+
+    for n_init in [128, 256, 384]:
+        for _ in range(8):
+            seq = _seed_sequence(rng, n_init)
+            val = evaluate_sequence(seq.tolist())
+            if val < best_val:
+                best_seq, best_val = seq, val
+
+    # Multi-resolution optimization
+    resolutions = [128, 256, 512, 768]
+    time_shares = [0.06, 0.18, 0.42, 1.0] # Skew time heavily toward final res
+    
+    for idx, target_n in enumerate(resolutions):
+        if target_n != best_seq.size:
+            best_seq = np.interp(np.linspace(0, 1, target_n), np.linspace(0, 1, best_seq.size), best_seq)
+            best_seq = _normalize_scale(best_seq)
+            best_val = evaluate_sequence(best_seq.tolist())
+
+        res_deadline = start_time + time_shares[idx] * (deadline - start_time)
+        while time.time() < res_deadline:
+            progress = (time.time() - start_time) / (deadline - start_time + 1e-9)
+            # sigma decays as refinement progresses
+            sigma = 0.20 * (1.0 - progress)**1.1
+            seq = best_seq.copy()
+            n = seq.size
+            was_symmetric = np.allclose(seq, seq[::-1], atol=1e-8)
+            
+            m_type = rng.random()
+            if m_type < 0.85: # Surgical Exponentiated Gradient Move
+                c = np.convolve(seq, seq)
+                max_c = np.max(c)
+                sum_a = np.sum(seq)
+                
+                # Sharpen peak focus over time
+                beta = 50.0 + 450.0 * (progress**1.2)
+                # Compute softmax weights of the convolution peaks
+                # Subtracting max(beta*(...)) for numerical stability
+                w = np.exp(beta * (c / (max_c + 1e-12) - 1.0))
+                w /= (np.sum(w) + 1e-15)
+                
+                # Gradient of sum(w*C) with respect to sequence 'a'
+                grad = np.convolve(w, seq[::-1], mode='valid')
+                
+                # rel_grad_log is the gradient of the log-objective with respect to log(a_i)
+                # d log(max_c)/d log(a_i) = (a_i/max_c) * (2*grad_i)
+                # d log(sum_a^2)/d log(a_i) = (a_i/(sum_a^2)) * (2*sum_a) = 2*a_i/sum_a
+                rel_grad_log = (grad * seq / (max_c + 1e-12)) - (seq / (sum_a + 1e-12))
+                
+                # Exponentiated gradient step
+                step_size = 0.8 * sigma
+                seq *= np.exp(-step_size * rel_grad_log * n)
+                
+            elif m_type < 0.92: # Spectral Smoothing and Low-Frequency Jitter
+                f = np.fft.rfft(seq)
+                # Suppress high-frequency noise that doesn't help flatten convolution
+                cutoff = max(1, int(len(f) * (0.15 + 0.1 * (1.0 - progress))))
+                f[cutoff:] *= (1.0 - 0.5 * sigma)
+                # Small low-frequency perturbation for global shape exploration
+                low_idx = rng.integers(1, cutoff, size=rng.integers(1, 3))
+                f[low_idx] *= (1.0 + rng.normal(0, 0.05 * sigma, size=len(low_idx)))
+                seq = np.fft.irfft(f, n=n)
+                
+            elif m_type < 0.97: # Symmetry-aware Block Perturbation
+                blen = rng.integers(2, max(5, n // 10))
+                bstart = rng.integers(0, n - blen)
+                delta = rng.normal(0, 0.5 * sigma, size=blen)
+                seq[bstart : bstart + blen] += delta
+                if was_symmetric or rng.random() < 0.5:
+                    seq[n - bstart - blen : n - bstart] += delta[::-1]
+            
+            else: # Mirror sequence to enforce symmetry early
+                if rng.random() < 0.5:
+                    seq[n//2:] = seq[:n-n//2][::-1]
+                else:
+                    seq[:n//2] = seq[n-n//2:][::-1]
+
+            # Symmetry hardening in the final phase
+            if progress > 0.88:
+                seq = (seq + seq[::-1]) / 2.0
+            elif was_symmetric and m_type < 0.98:
+                seq = (seq + seq[::-1]) / 2.0
+
+            seq = _normalize_scale(seq)
+            # Efficient evaluation of scale-invariant part
+            new_c = np.convolve(seq, seq)
+            val = 2.0 * n * np.max(new_c) / (np.sum(seq)**2)
+            
+            if val < best_val:
+                best_seq, best_val = seq, val
+
+    return [float(x) for x in best_seq.tolist()]
+
+# EVOLVE-BLOCK-END

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_103/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,3 @@
+def evaluate_aux(results_dir, primary_result=None):
+    """Return auxiliary metrics as a dict."""
+    return {}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_103/results/correct.json

@@ -0,0 +1,4 @@
+{
+    "correct": true,
+    "error": null
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_103/results/metrics.json

@@ -0,0 +1,32 @@
+{
+  "combined_score": -1.7035919462851012,
+  "correct": true,
+  "primary": {
+    "combined_score": -1.7035919462851012,
+    "public": {
+      "best_value": 1.7035919462851012,
+      "best_length": 768,
+      "num_runs": 1
+    },
+    "private": {
+      "all_values": [
+        1.7035919462851012
+      ],
+      "all_lengths": [
+        768
+      ]
+    },
+    "text_feedback": "Lower evaluate_sequence value is better. combined_score = -best_value.",
+    "execution_time_mean": 9.629194633103907,
+    "execution_time_std": 0.0,
+    "num_valid_runs": 1,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": true,
+    "validation_error": null
+  },
+  "auxiliary": {},
+  "auxiliary_descriptions": {},
+  "timestamp": 1770954954.2171297,
+  "generation": 103
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_104/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,3 @@
+def evaluate_aux(results_dir, primary_result=None):
+    """Return auxiliary metrics as a dict."""
+    return {}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_104/results/correct.json

@@ -0,0 +1,4 @@
+{
+    "correct": false,
+    "error": "name 'np' is not defined"
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_104/results/metrics.json

@@ -0,0 +1,19 @@
+{
+  "combined_score": 0.0,
+  "correct": false,
+  "primary": {
+    "combined_score": 0.0,
+    "execution_time_mean": 0.0,
+    "execution_time_std": 0.0,
+    "num_successful_runs": 0,
+    "num_valid_runs": 0,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": false,
+    "validation_error": "name 'np' is not defined"
+  },
+  "auxiliary": {},
+  "auxiliary_descriptions": {},
+  "timestamp": 1770955012.723826,
+  "generation": 104
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_105/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,3 @@
+def evaluate_aux(results_dir, primary_result=None):
+    """Return auxiliary metrics as a dict."""
+    return {}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_105/results/correct.json

@@ -0,0 +1,4 @@
+{
+    "correct": true,
+    "error": null
+}

tasks/alphaevolve_ac/results/results_alphaevolve_ac_baseline_gen200_periodic1000_20260213_012331/gen_105/results/metrics.json

@@ -0,0 +1,32 @@
+{
+  "combined_score": -1.616379538404345,
+  "correct": true,
+  "primary": {
+    "combined_score": -1.616379538404345,
+    "public": {
+      "best_value": 1.616379538404345,
+      "best_length": 768,
+      "num_runs": 1
+    },
+    "private": {
+      "all_values": [
+        1.616379538404345
+      ],
+      "all_lengths": [
+        768
+      ]
+    },
+    "text_feedback": "Lower evaluate_sequence value is better. combined_score = -best_value.",
+    "execution_time_mean": 9.625271708704531,
+    "execution_time_std": 0.0,
+    "num_valid_runs": 1,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": true,
+    "validation_error": null
+  },
+  "auxiliary": {},
+  "auxiliary_descriptions": {},
+  "timestamp": 1770955062.0079255,
+  "generation": 105
+}