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tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_100/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,169 @@
+
+import numpy as np
+import os
+import json
+from typing import Dict, Any
+import math
+
+def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:
+    metrics = {}
+    
+    # Initialize centers and radii to empty arrays for robustness
+    centers = np.array([])
+    radii = np.array([])
+    N_EXPECTED = 26 # Define N_EXPECTED early for consistent use
+
+    # Try to load the extra.npz file for detailed data
+    try:
+        extra_data_path = os.path.join(results_dir, "extra.npz")
+        if os.path.exists(extra_data_path):
+            with np.load(extra_data_path) as data:
+                centers = data["centers"]
+                radii = data["radii"]
+        else:
+            print(f"Warning: {extra_data_path} not found. Some metrics might be 0.")
+    except Exception as e:
+        print(f"Error loading extra.npz: {e}")
+        # If error, centers and radii remain empty arrays, handled below
+
+    # Get primary validation status
+    primary_correct_flag = False
+    if primary_result and "correct" in primary_result:
+        primary_correct_flag = primary_result["correct"]
+    else: # Fallback to metrics.json if primary_result is incomplete
+        metrics_json_path = os.path.join(results_dir, "results/metrics.json") # Correct path for metrics.json
+        if os.path.exists(metrics_json_path):
+            try:
+                with open(metrics_json_path, 'r') as f:
+                    json_data = json.load(f)
+                    if "correct" in json_data:
+                        primary_correct_flag = json_data["correct"]
+                    elif "public" in json_data and "correct" in json_data["public"]:
+                         primary_correct_flag = json_data["public"]["correct"]
+                    elif "private" in json_data and "correct" in json_data["private"]:
+                         primary_correct_flag = json_data["private"]["correct"]
+            except Exception as e:
+                print(f"Error loading metrics.json for primary_correct_flag: {e}")
+
+
+    # Metric 1: is_valid_packing (Binary correctness signal)
+    try:
+        metrics["is_valid_packing"] = 1.0 if primary_correct_flag else 0.0
+    except Exception as e:
+        print(f"Error calculating is_valid_packing: {e}")
+        metrics["is_valid_packing"] = 0.0
+
+    # Metric: has_nan_or_inf (NEW)
+    try:
+        has_nan = np.isnan(centers).any() or np.isnan(radii).any()
+        has_inf = np.isinf(centers).any() or np.isinf(radii).any()
+        metrics["has_nan_or_inf"] = 1.0 if (has_nan or has_inf) else 0.0
+    except Exception as e:
+        print(f"Error calculating has_nan_or_inf: {e}")
+        metrics["has_nan_or_inf"] = 0.0 # Default to no error if calculation fails
+
+    # If no valid data or incorrect number of circles, set dependent metrics to 0
+    # Also set num_circles_generated to actual count even if it's not N_EXPECTED
+    if centers.size == 0 or radii.size == 0:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["num_circles_generated"] = 0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics
+
+    # Metric: num_circles_generated (actual count)
+    try:
+        metrics["num_circles_generated"] = centers.shape[0]
+    except Exception as e:
+        print(f"Error calculating num_circles_generated: {e}")
+        metrics["num_circles_generated"] = 0
+        
+    # Check if the number of circles matches N_EXPECTED before calculating other metrics
+    if centers.shape[0] != N_EXPECTED or radii.shape[0] != N_EXPECTED:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics # Exit early if counts are wrong, as other metrics might be nonsensical
+
+    # All subsequent metrics assume N_EXPECTED circles are present and no NaNs/Infs
+
+    # Metric 2: mean_radius
+    try:
+        metrics["mean_radius"] = float(np.mean(radii))
+    except Exception as e:
+        print(f"Error calculating mean_radius: {e}")
+        metrics["mean_radius"] = 0.0
+
+    # Metric 3: radius_std_dev
+    try:
+        metrics["radius_std_dev"] = float(np.std(radii)) if len(radii) > 1 else 0.0
+    except Exception as e:
+        print(f"Error calculating radius_std_dev: {e}")
+        metrics["radius_std_dev"] = 0.0
+
+    # Metric 4: total_packed_area
+    try:
+        metrics["total_packed_area"] = float(np.sum(np.pi * radii**2))
+    except Exception as e:
+        print(f"Error calculating total_packed_area: {e}")
+        metrics["total_packed_area"] = 0.0
+
+    # Metric 5: min_clearance_to_boundary
+    try:
+        min_clearance = float('inf')
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            clearances = [x - r, 1 - (x + r), y - r, 1 - (y + r)]
+            min_clearance = min(min_clearance, *clearances)
+        metrics["min_clearance_to_boundary"] = min_clearance if math.isfinite(min_clearance) else 0.0
+    except Exception as e:
+        print(f"Error calculating min_clearance_to_boundary: {e}")
+        metrics["min_clearance_to_boundary"] = 0.0
+
+    # Metric 6: max_overlap_value
+    try:
+        max_overlap = 0.0 # Changed from -inf, as overlap should be >= 0 for this metric
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                overlap = (radii[i] + radii[j]) - dist
+                if overlap > max_overlap:
+                    max_overlap = overlap
+        metrics["max_overlap_value"] = max_overlap
+    except Exception as e:
+        print(f"Error calculating max_overlap_value: {e}")
+        metrics["max_overlap_value"] = 0.0
+
+    # Metric 8: min_radius
+    try:
+        metrics["min_radius"] = float(np.min(radii))
+    except Exception as e:
+        print(f"Error calculating min_radius: {e}")
+        metrics["min_radius"] = 0.0
+
+    # Metric 10: boundary_contact_count
+    try:
+        contact_count = 0
+        epsilon = 1e-6 # Tolerance for floating point comparisons
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            # Check if touching any of the four boundaries
+            if abs(x - r) < epsilon or abs(1 - (x + r)) < epsilon or \
+               abs(y - r) < epsilon or abs(1 - (y + r)) < epsilon:
+                contact_count += 1
+        metrics["boundary_contact_count"] = float(contact_count)
+    except Exception as e:
+        print(f"Error calculating boundary_contact_count: {e}")
+        metrics["boundary_contact_count"] = 0
+
+    return metrics

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_100/results/correct.json

@@ -0,0 +1,4 @@
+{
+    "correct": false,
+    "error": "ValueError: too many values to unpack (expected 2)"
+}

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_100/results/metrics.json

@@ -0,0 +1,41 @@
+{
+  "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: too many values to unpack (expected 2)"
+  },
+  "auxiliary": {
+    "is_valid_packing": 0.0,
+    "has_nan_or_inf": 0.0,
+    "mean_radius": 0.0,
+    "radius_std_dev": 0.0,
+    "total_packed_area": 0.0,
+    "min_clearance_to_boundary": 0.0,
+    "max_overlap_value": 0.0,
+    "num_circles_generated": 0,
+    "min_radius": 0.0,
+    "boundary_contact_count": 0
+  },
+  "auxiliary_descriptions": {
+    "mean_radius": "Average radius of all circles.",
+    "radius_std_dev": "Standard deviation of radii.",
+    "total_packed_area": "Total area covered by all circles.",
+    "min_clearance_to_boundary": "Minimum distance of any circle to the unit square boundary.",
+    "max_overlap_value": "Maximum overlap between any two circles.",
+    "is_valid_packing": "Binary flag indicating if the packing is valid (no overlaps, in bounds, correct shapes).",
+    "num_circles_generated": "The actual number of circles generated by the solution.",
+    "min_radius": "The minimum radius among all circles in the packing.",
+    "has_nan_or_inf": "Binary flag indicating if any center or radius contains NaN or Inf values, a sign of numerical instability.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries, indicating effective utilization of space."
+  },
+  "timestamp": 1770931407.4188106,
+  "generation": 100
+}

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_101/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,169 @@
+
+import numpy as np
+import os
+import json
+from typing import Dict, Any
+import math
+
+def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:
+    metrics = {}
+    
+    # Initialize centers and radii to empty arrays for robustness
+    centers = np.array([])
+    radii = np.array([])
+    N_EXPECTED = 26 # Define N_EXPECTED early for consistent use
+
+    # Try to load the extra.npz file for detailed data
+    try:
+        extra_data_path = os.path.join(results_dir, "extra.npz")
+        if os.path.exists(extra_data_path):
+            with np.load(extra_data_path) as data:
+                centers = data["centers"]
+                radii = data["radii"]
+        else:
+            print(f"Warning: {extra_data_path} not found. Some metrics might be 0.")
+    except Exception as e:
+        print(f"Error loading extra.npz: {e}")
+        # If error, centers and radii remain empty arrays, handled below
+
+    # Get primary validation status
+    primary_correct_flag = False
+    if primary_result and "correct" in primary_result:
+        primary_correct_flag = primary_result["correct"]
+    else: # Fallback to metrics.json if primary_result is incomplete
+        metrics_json_path = os.path.join(results_dir, "results/metrics.json") # Correct path for metrics.json
+        if os.path.exists(metrics_json_path):
+            try:
+                with open(metrics_json_path, 'r') as f:
+                    json_data = json.load(f)
+                    if "correct" in json_data:
+                        primary_correct_flag = json_data["correct"]
+                    elif "public" in json_data and "correct" in json_data["public"]:
+                         primary_correct_flag = json_data["public"]["correct"]
+                    elif "private" in json_data and "correct" in json_data["private"]:
+                         primary_correct_flag = json_data["private"]["correct"]
+            except Exception as e:
+                print(f"Error loading metrics.json for primary_correct_flag: {e}")
+
+
+    # Metric 1: is_valid_packing (Binary correctness signal)
+    try:
+        metrics["is_valid_packing"] = 1.0 if primary_correct_flag else 0.0
+    except Exception as e:
+        print(f"Error calculating is_valid_packing: {e}")
+        metrics["is_valid_packing"] = 0.0
+
+    # Metric: has_nan_or_inf (NEW)
+    try:
+        has_nan = np.isnan(centers).any() or np.isnan(radii).any()
+        has_inf = np.isinf(centers).any() or np.isinf(radii).any()
+        metrics["has_nan_or_inf"] = 1.0 if (has_nan or has_inf) else 0.0
+    except Exception as e:
+        print(f"Error calculating has_nan_or_inf: {e}")
+        metrics["has_nan_or_inf"] = 0.0 # Default to no error if calculation fails
+
+    # If no valid data or incorrect number of circles, set dependent metrics to 0
+    # Also set num_circles_generated to actual count even if it's not N_EXPECTED
+    if centers.size == 0 or radii.size == 0:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["num_circles_generated"] = 0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics
+
+    # Metric: num_circles_generated (actual count)
+    try:
+        metrics["num_circles_generated"] = centers.shape[0]
+    except Exception as e:
+        print(f"Error calculating num_circles_generated: {e}")
+        metrics["num_circles_generated"] = 0
+        
+    # Check if the number of circles matches N_EXPECTED before calculating other metrics
+    if centers.shape[0] != N_EXPECTED or radii.shape[0] != N_EXPECTED:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics # Exit early if counts are wrong, as other metrics might be nonsensical
+
+    # All subsequent metrics assume N_EXPECTED circles are present and no NaNs/Infs
+
+    # Metric 2: mean_radius
+    try:
+        metrics["mean_radius"] = float(np.mean(radii))
+    except Exception as e:
+        print(f"Error calculating mean_radius: {e}")
+        metrics["mean_radius"] = 0.0
+
+    # Metric 3: radius_std_dev
+    try:
+        metrics["radius_std_dev"] = float(np.std(radii)) if len(radii) > 1 else 0.0
+    except Exception as e:
+        print(f"Error calculating radius_std_dev: {e}")
+        metrics["radius_std_dev"] = 0.0
+
+    # Metric 4: total_packed_area
+    try:
+        metrics["total_packed_area"] = float(np.sum(np.pi * radii**2))
+    except Exception as e:
+        print(f"Error calculating total_packed_area: {e}")
+        metrics["total_packed_area"] = 0.0
+
+    # Metric 5: min_clearance_to_boundary
+    try:
+        min_clearance = float('inf')
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            clearances = [x - r, 1 - (x + r), y - r, 1 - (y + r)]
+            min_clearance = min(min_clearance, *clearances)
+        metrics["min_clearance_to_boundary"] = min_clearance if math.isfinite(min_clearance) else 0.0
+    except Exception as e:
+        print(f"Error calculating min_clearance_to_boundary: {e}")
+        metrics["min_clearance_to_boundary"] = 0.0
+
+    # Metric 6: max_overlap_value
+    try:
+        max_overlap = 0.0 # Changed from -inf, as overlap should be >= 0 for this metric
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                overlap = (radii[i] + radii[j]) - dist
+                if overlap > max_overlap:
+                    max_overlap = overlap
+        metrics["max_overlap_value"] = max_overlap
+    except Exception as e:
+        print(f"Error calculating max_overlap_value: {e}")
+        metrics["max_overlap_value"] = 0.0
+
+    # Metric 8: min_radius
+    try:
+        metrics["min_radius"] = float(np.min(radii))
+    except Exception as e:
+        print(f"Error calculating min_radius: {e}")
+        metrics["min_radius"] = 0.0
+
+    # Metric 10: boundary_contact_count
+    try:
+        contact_count = 0
+        epsilon = 1e-6 # Tolerance for floating point comparisons
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            # Check if touching any of the four boundaries
+            if abs(x - r) < epsilon or abs(1 - (x + r)) < epsilon or \
+               abs(y - r) < epsilon or abs(1 - (y + r)) < epsilon:
+                contact_count += 1
+        metrics["boundary_contact_count"] = float(contact_count)
+    except Exception as e:
+        print(f"Error calculating boundary_contact_count: {e}")
+        metrics["boundary_contact_count"] = 0
+
+    return metrics

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_101/results/correct.json

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

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_101/results/metrics.json

@@ -0,0 +1,47 @@
+{
+  "combined_score": 2.598251252706035,
+  "correct": true,
+  "primary": {
+    "combined_score": 2.598251252706035,
+    "public": {
+      "centers_str": "  centers[0] = (0.0820, 0.0813)\n  centers[1] = (0.2889, 0.1266)\n  centers[2] = (0.4401, 0.2608)\n  centers[3] = (0.5191, 0.1022)\n  centers[4] = (0.7173, 0.0960)\n  centers[5] = (0.9066, 0.0934)\n  centers[6] = (0.1040, 0.2653)\n  centers[7] = (0.2899, 0.3559)\n  centers[8] = (0.4703, 0.4225)\n  centers[9] = (0.6313, 0.2919)\n  centers[10] = (0.8635, 0.2955)\n  centers[11] = (0.1141, 0.4831)\n  centers[12] = (0.3106, 0.5516)\n  centers[13] = (0.3846, 0.7270)\n  centers[14] = (0.6451, 0.5202)\n  centers[15] = (0.8779, 0.5305)\n  centers[16] = (0.1679, 0.7104)\n  centers[17] = (0.4732, 0.5839)\n  centers[18] = (0.5768, 0.7158)\n  centers[19] = (0.7448, 0.6785)\n  centers[20] = (0.9033, 0.7478)\n  centers[21] = (0.0921, 0.9079)\n  centers[22] = (0.2855, 0.8984)\n  centers[23] = (0.4915, 0.8963)\n  centers[24] = (0.7253, 0.8769)\n  centers[25] = (0.9217, 0.9218)",
+      "num_circles": 26
+    },
+    "private": {
+      "reported_sum_of_radii": 2.598251252706035
+    },
+    "execution_time_mean": 11.946096290834248,
+    "execution_time_std": 0.0,
+    "num_valid_runs": 1,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": true,
+    "validation_error": null
+  },
+  "auxiliary": {
+    "is_valid_packing": 1.0,
+    "has_nan_or_inf": 0.0,
+    "num_circles_generated": 26,
+    "mean_radius": 0.09993274048869366,
+    "radius_std_dev": 0.015295358419589405,
+    "total_packed_area": 0.834824890504465,
+    "min_clearance_to_boundary": 0.0,
+    "max_overlap_value": 0.0,
+    "min_radius": 0.07188900122340332,
+    "boundary_contact_count": 9.0
+  },
+  "auxiliary_descriptions": {
+    "mean_radius": "Average radius of all circles.",
+    "radius_std_dev": "Standard deviation of radii.",
+    "total_packed_area": "Total area covered by all circles.",
+    "min_clearance_to_boundary": "Minimum distance of any circle to the unit square boundary.",
+    "max_overlap_value": "Maximum overlap between any two circles.",
+    "is_valid_packing": "Binary flag indicating if the packing is valid (no overlaps, in bounds, correct shapes).",
+    "num_circles_generated": "The actual number of circles generated by the solution.",
+    "min_radius": "The minimum radius among all circles in the packing.",
+    "has_nan_or_inf": "Binary flag indicating if any center or radius contains NaN or Inf values, a sign of numerical instability.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries, indicating effective utilization of space."
+  },
+  "timestamp": 1770931544.2339413,
+  "generation": 101
+}

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_103/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,172 @@
+
+import numpy as np
+import os
+import json
+from typing import Dict, Any
+import math
+
+def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:
+    metrics = {}
+    
+    # Initialize centers and radii to empty arrays for robustness
+    centers = np.array([])
+    radii = np.array([])
+    N_EXPECTED = 26 # Define N_EXPECTED early for consistent use
+
+    # Try to load the extra.npz file for detailed data
+    try:
+        extra_data_path = os.path.join(results_dir, "extra.npz")
+        if os.path.exists(extra_data_path):
+            with np.load(extra_data_path) as data:
+                centers = data["centers"]
+                radii = data["radii"]
+        else:
+            print(f"Warning: {extra_data_path} not found. Some metrics might be 0.")
+    except Exception as e:
+        print(f"Error loading extra.npz: {e}")
+        # If error, centers and radii remain empty arrays, handled below
+
+    # Get primary validation status
+    primary_correct_flag = False
+    if primary_result and "correct" in primary_result:
+        primary_correct_flag = primary_result["correct"]
+    else: # Fallback to metrics.json if primary_result is incomplete
+        metrics_json_path = os.path.join(results_dir, "results/metrics.json") # Correct path for metrics.json
+        if os.path.exists(metrics_json_path):
+            try:
+                with open(metrics_json_path, 'r') as f:
+                    json_data = json.load(f)
+                    if "correct" in json_data:
+                        primary_correct_flag = json_data["correct"]
+                    elif "public" in json_data and "correct" in json_data["public"]:
+                         primary_correct_flag = json_data["public"]["correct"]
+                    elif "private" in json_data and "correct" in json_data["private"]:
+                         primary_correct_flag = json_data["private"]["correct"]
+            except Exception as e:
+                print(f"Error loading metrics.json for primary_correct_flag: {e}")
+
+
+    # Metric 1: is_valid_packing (Binary correctness signal)
+    try:
+        metrics["is_valid_packing"] = 1.0 if primary_correct_flag else 0.0
+    except Exception as e:
+        print(f"Error calculating is_valid_packing: {e}")
+        metrics["is_valid_packing"] = 0.0
+
+    # Metric: avg_dist_to_center
+    try:
+        if centers.shape[0] == N_EXPECTED:
+            square_center = np.array([0.5, 0.5])
+            distances = np.linalg.norm(centers - square_center, axis=1)
+            metrics["avg_dist_to_center"] = float(np.mean(distances))
+        else:
+            metrics["avg_dist_to_center"] = 0.0
+    except Exception as e:
+        print(f"Error calculating avg_dist_to_center: {e}")
+        metrics["avg_dist_to_center"] = 0.0
+
+    # If no valid data or incorrect number of circles, set dependent metrics to 0
+    # Also set num_circles_generated to actual count even if it's not N_EXPECTED
+    if centers.size == 0 or radii.size == 0:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["num_circles_generated"] = 0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics
+
+    # Metric: num_circles_generated (actual count)
+    try:
+        metrics["num_circles_generated"] = centers.shape[0]
+    except Exception as e:
+        print(f"Error calculating num_circles_generated: {e}")
+        metrics["num_circles_generated"] = 0
+        
+    # Check if the number of circles matches N_EXPECTED before calculating other metrics
+    if centers.shape[0] != N_EXPECTED or radii.shape[0] != N_EXPECTED:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics # Exit early if counts are wrong, as other metrics might be nonsensical
+
+    # All subsequent metrics assume N_EXPECTED circles are present and no NaNs/Infs
+
+    # Metric 2: mean_radius
+    try:
+        metrics["mean_radius"] = float(np.mean(radii))
+    except Exception as e:
+        print(f"Error calculating mean_radius: {e}")
+        metrics["mean_radius"] = 0.0
+
+    # Metric 3: radius_std_dev
+    try:
+        metrics["radius_std_dev"] = float(np.std(radii)) if len(radii) > 1 else 0.0
+    except Exception as e:
+        print(f"Error calculating radius_std_dev: {e}")
+        metrics["radius_std_dev"] = 0.0
+
+    # Metric 4: total_packed_area
+    try:
+        metrics["total_packed_area"] = float(np.sum(np.pi * radii**2))
+    except Exception as e:
+        print(f"Error calculating total_packed_area: {e}")
+        metrics["total_packed_area"] = 0.0
+
+    # Metric 5: min_clearance_to_boundary
+    try:
+        min_clearance = float('inf')
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            clearances = [x - r, 1 - (x + r), y - r, 1 - (y + r)]
+            min_clearance = min(min_clearance, *clearances)
+        metrics["min_clearance_to_boundary"] = min_clearance if math.isfinite(min_clearance) else 0.0
+    except Exception as e:
+        print(f"Error calculating min_clearance_to_boundary: {e}")
+        metrics["min_clearance_to_boundary"] = 0.0
+
+    # Metric 6: max_overlap_value
+    try:
+        max_overlap = 0.0 # Changed from -inf, as overlap should be >= 0 for this metric
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                overlap = (radii[i] + radii[j]) - dist
+                if overlap > max_overlap:
+                    max_overlap = overlap
+        metrics["max_overlap_value"] = max_overlap
+    except Exception as e:
+        print(f"Error calculating max_overlap_value: {e}")
+        metrics["max_overlap_value"] = 0.0
+
+    # Metric 8: min_radius
+    try:
+        metrics["min_radius"] = float(np.min(radii))
+    except Exception as e:
+        print(f"Error calculating min_radius: {e}")
+        metrics["min_radius"] = 0.0
+
+    # Metric 10: boundary_contact_count
+    try:
+        contact_count = 0
+        epsilon = 1e-6 # Tolerance for floating point comparisons
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            # Check if touching any of the four boundaries
+            if abs(x - r) < epsilon or abs(1 - (x + r)) < epsilon or \
+               abs(y - r) < epsilon or abs(1 - (y + r)) < epsilon:
+                contact_count += 1
+        metrics["boundary_contact_count"] = float(contact_count)
+    except Exception as e:
+        print(f"Error calculating boundary_contact_count: {e}")
+        metrics["boundary_contact_count"] = 0
+
+    return metrics

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_103/results/correct.json

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

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_103/results/metrics.json

@@ -0,0 +1,47 @@
+{
+  "combined_score": 2.58878731265258,
+  "correct": true,
+  "primary": {
+    "combined_score": 2.58878731265258,
+    "public": {
+      "centers_str": "  centers[0] = (0.1141, 0.1142)\n  centers[1] = (0.3221, 0.0949)\n  centers[2] = (0.7227, 0.1043)\n  centers[3] = (0.9131, 0.0870)\n  centers[4] = (0.1012, 0.3292)\n  centers[5] = (0.2468, 0.2406)\n  centers[6] = (0.5179, 0.1005)\n  centers[7] = (0.6130, 0.2872)\n  centers[8] = (0.8576, 0.3020)\n  centers[9] = (0.4810, 0.4314)\n  centers[10] = (0.4633, 0.5761)\n  centers[11] = (0.4098, 0.2640)\n  centers[12] = (0.6676, 0.5028)\n  centers[13] = (0.1057, 0.5348)\n  centers[14] = (0.2863, 0.4247)\n  centers[15] = (0.5713, 0.6932)\n  centers[16] = (0.7491, 0.6766)\n  centers[17] = (0.8884, 0.5468)\n  centers[18] = (0.0981, 0.7371)\n  centers[19] = (0.3000, 0.6520)\n  centers[20] = (0.5008, 0.8905)\n  centers[21] = (0.9051, 0.7525)\n  centers[22] = (0.0827, 0.9172)\n  centers[23] = (0.2771, 0.8857)\n  centers[24] = (0.7307, 0.8792)\n  centers[25] = (0.9231, 0.9232)",
+      "num_circles": 26
+    },
+    "private": {
+      "reported_sum_of_radii": 2.58878731265258
+    },
+    "execution_time_mean": 13.756057593040168,
+    "execution_time_std": 0.0,
+    "num_valid_runs": 1,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": true,
+    "validation_error": null
+  },
+  "auxiliary": {
+    "is_valid_packing": 1.0,
+    "avg_dist_to_center": 0.3706455082509068,
+    "num_circles_generated": 26,
+    "mean_radius": 0.09956874279433,
+    "radius_std_dev": 0.016847858934533527,
+    "total_packed_area": 0.8329694386157515,
+    "min_clearance_to_boundary": 0.0,
+    "max_overlap_value": 0.0,
+    "min_radius": 0.059401842374791416,
+    "boundary_contact_count": 9.0
+  },
+  "auxiliary_descriptions": {
+    "mean_radius": "Average radius of all circles.",
+    "radius_std_dev": "Standard deviation of radii.",
+    "total_packed_area": "Total area covered by all circles.",
+    "min_clearance_to_boundary": "Minimum distance of any circle to the unit square boundary.",
+    "max_overlap_value": "Maximum overlap between any two circles.",
+    "is_valid_packing": "Binary flag indicating if the packing is valid (no overlaps, in bounds, correct shapes).",
+    "num_circles_generated": "The actual number of circles generated by the solution.",
+    "min_radius": "The minimum radius among all circles in the packing.",
+    "avg_dist_to_center": "Average distance of circle centers to the center of the unit square, indicating compactness.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries, indicating effective utilization of space."
+  },
+  "timestamp": 1770931681.47676,
+  "generation": 103
+}

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_105/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,172 @@
+
+import numpy as np
+import os
+import json
+from typing import Dict, Any
+import math
+
+def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:
+    metrics = {}
+    
+    # Initialize centers and radii to empty arrays for robustness
+    centers = np.array([])
+    radii = np.array([])
+    N_EXPECTED = 26 # Define N_EXPECTED early for consistent use
+
+    # Try to load the extra.npz file for detailed data
+    try:
+        extra_data_path = os.path.join(results_dir, "extra.npz")
+        if os.path.exists(extra_data_path):
+            with np.load(extra_data_path) as data:
+                centers = data["centers"]
+                radii = data["radii"]
+        else:
+            print(f"Warning: {extra_data_path} not found. Some metrics might be 0.")
+    except Exception as e:
+        print(f"Error loading extra.npz: {e}")
+        # If error, centers and radii remain empty arrays, handled below
+
+    # Get primary validation status
+    primary_correct_flag = False
+    if primary_result and "correct" in primary_result:
+        primary_correct_flag = primary_result["correct"]
+    else: # Fallback to metrics.json if primary_result is incomplete
+        metrics_json_path = os.path.join(results_dir, "results/metrics.json") # Correct path for metrics.json
+        if os.path.exists(metrics_json_path):
+            try:
+                with open(metrics_json_path, 'r') as f:
+                    json_data = json.load(f)
+                    if "correct" in json_data:
+                        primary_correct_flag = json_data["correct"]
+                    elif "public" in json_data and "correct" in json_data["public"]:
+                         primary_correct_flag = json_data["public"]["correct"]
+                    elif "private" in json_data and "correct" in json_data["private"]:
+                         primary_correct_flag = json_data["private"]["correct"]
+            except Exception as e:
+                print(f"Error loading metrics.json for primary_correct_flag: {e}")
+
+
+    # Metric 1: is_valid_packing (Binary correctness signal)
+    try:
+        metrics["is_valid_packing"] = 1.0 if primary_correct_flag else 0.0
+    except Exception as e:
+        print(f"Error calculating is_valid_packing: {e}")
+        metrics["is_valid_packing"] = 0.0
+
+    # Metric: avg_dist_to_center
+    try:
+        if centers.shape[0] == N_EXPECTED:
+            square_center = np.array([0.5, 0.5])
+            distances = np.linalg.norm(centers - square_center, axis=1)
+            metrics["avg_dist_to_center"] = float(np.mean(distances))
+        else:
+            metrics["avg_dist_to_center"] = 0.0
+    except Exception as e:
+        print(f"Error calculating avg_dist_to_center: {e}")
+        metrics["avg_dist_to_center"] = 0.0
+
+    # If no valid data or incorrect number of circles, set dependent metrics to 0
+    # Also set num_circles_generated to actual count even if it's not N_EXPECTED
+    if centers.size == 0 or radii.size == 0:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["num_circles_generated"] = 0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics
+
+    # Metric: num_circles_generated (actual count)
+    try:
+        metrics["num_circles_generated"] = centers.shape[0]
+    except Exception as e:
+        print(f"Error calculating num_circles_generated: {e}")
+        metrics["num_circles_generated"] = 0
+        
+    # Check if the number of circles matches N_EXPECTED before calculating other metrics
+    if centers.shape[0] != N_EXPECTED or radii.shape[0] != N_EXPECTED:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics # Exit early if counts are wrong, as other metrics might be nonsensical
+
+    # All subsequent metrics assume N_EXPECTED circles are present and no NaNs/Infs
+
+    # Metric 2: mean_radius
+    try:
+        metrics["mean_radius"] = float(np.mean(radii))
+    except Exception as e:
+        print(f"Error calculating mean_radius: {e}")
+        metrics["mean_radius"] = 0.0
+
+    # Metric 3: radius_std_dev
+    try:
+        metrics["radius_std_dev"] = float(np.std(radii)) if len(radii) > 1 else 0.0
+    except Exception as e:
+        print(f"Error calculating radius_std_dev: {e}")
+        metrics["radius_std_dev"] = 0.0
+
+    # Metric 4: total_packed_area
+    try:
+        metrics["total_packed_area"] = float(np.sum(np.pi * radii**2))
+    except Exception as e:
+        print(f"Error calculating total_packed_area: {e}")
+        metrics["total_packed_area"] = 0.0
+
+    # Metric 5: min_clearance_to_boundary
+    try:
+        min_clearance = float('inf')
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            clearances = [x - r, 1 - (x + r), y - r, 1 - (y + r)]
+            min_clearance = min(min_clearance, *clearances)
+        metrics["min_clearance_to_boundary"] = min_clearance if math.isfinite(min_clearance) else 0.0
+    except Exception as e:
+        print(f"Error calculating min_clearance_to_boundary: {e}")
+        metrics["min_clearance_to_boundary"] = 0.0
+
+    # Metric 6: max_overlap_value
+    try:
+        max_overlap = 0.0 # Changed from -inf, as overlap should be >= 0 for this metric
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                overlap = (radii[i] + radii[j]) - dist
+                if overlap > max_overlap:
+                    max_overlap = overlap
+        metrics["max_overlap_value"] = max_overlap
+    except Exception as e:
+        print(f"Error calculating max_overlap_value: {e}")
+        metrics["max_overlap_value"] = 0.0
+
+    # Metric 8: min_radius
+    try:
+        metrics["min_radius"] = float(np.min(radii))
+    except Exception as e:
+        print(f"Error calculating min_radius: {e}")
+        metrics["min_radius"] = 0.0
+
+    # Metric 10: boundary_contact_count
+    try:
+        contact_count = 0
+        epsilon = 1e-6 # Tolerance for floating point comparisons
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            # Check if touching any of the four boundaries
+            if abs(x - r) < epsilon or abs(1 - (x + r)) < epsilon or \
+               abs(y - r) < epsilon or abs(1 - (y + r)) < epsilon:
+                contact_count += 1
+        metrics["boundary_contact_count"] = float(contact_count)
+    except Exception as e:
+        print(f"Error calculating boundary_contact_count: {e}")
+        metrics["boundary_contact_count"] = 0
+
+    return metrics

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_105/results/correct.json

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

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_105/results/metrics.json

@@ -0,0 +1,47 @@
+{
+  "combined_score": 2.6022372719739915,
+  "correct": true,
+  "primary": {
+    "combined_score": 2.6022372719739915,
+    "public": {
+      "centers_str": "  centers[0] = (0.8982, 0.2462)\n  centers[1] = (0.3016, 0.0736)\n  centers[2] = (0.5011, 0.1335)\n  centers[3] = (0.4557, 0.3579)\n  centers[4] = (0.7498, 0.1039)\n  centers[5] = (0.9258, 0.0734)\n  centers[6] = (0.1074, 0.3367)\n  centers[7] = (0.2945, 0.2470)\n  centers[8] = (0.5600, 0.4863)\n  centers[9] = (0.6805, 0.3286)\n  centers[10] = (0.8916, 0.8929)\n  centers[11] = (0.1068, 0.5513)\n  centers[12] = (0.2824, 0.4451)\n  centers[13] = (0.7052, 0.9199)\n  centers[14] = (0.7036, 0.5469)\n  centers[15] = (0.8863, 0.4609)\n  centers[16] = (0.0993, 0.7571)\n  centers[17] = (0.3102, 0.6709)\n  centers[18] = (0.5271, 0.6407)\n  centers[19] = (0.6939, 0.7376)\n  centers[20] = (0.8942, 0.6801)\n  centers[21] = (0.0730, 0.9271)\n  centers[22] = (0.4302, 0.5176)\n  centers[23] = (0.4910, 0.8641)\n  centers[24] = (0.2492, 0.8957)\n  centers[25] = (0.1168, 0.1148)",
+      "num_circles": 26
+    },
+    "private": {
+      "reported_sum_of_radii": 2.6022372719739915
+    },
+    "execution_time_mean": 12.359428913332522,
+    "execution_time_std": 0.0,
+    "num_valid_runs": 1,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": true,
+    "validation_error": null
+  },
+  "auxiliary": {
+    "is_valid_packing": 1.0,
+    "avg_dist_to_center": 0.3652892177516893,
+    "num_circles_generated": 26,
+    "mean_radius": 0.10008604892207659,
+    "radius_std_dev": 0.019429407832541483,
+    "total_packed_area": 0.8490553003446314,
+    "min_clearance_to_boundary": 0.0,
+    "max_overlap_value": 0.0,
+    "min_radius": 0.06616651730878995,
+    "boundary_contact_count": 13.0
+  },
+  "auxiliary_descriptions": {
+    "mean_radius": "Average radius of all circles.",
+    "radius_std_dev": "Standard deviation of radii.",
+    "total_packed_area": "Total area covered by all circles.",
+    "min_clearance_to_boundary": "Minimum distance of any circle to the unit square boundary.",
+    "max_overlap_value": "Maximum overlap between any two circles.",
+    "is_valid_packing": "Binary flag indicating if the packing is valid (no overlaps, in bounds, correct shapes).",
+    "num_circles_generated": "The actual number of circles generated by the solution.",
+    "min_radius": "The minimum radius among all circles in the packing.",
+    "avg_dist_to_center": "Average distance of circle centers to the center of the unit square, indicating compactness.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries, indicating effective utilization of space."
+  },
+  "timestamp": 1770931808.6927269,
+  "generation": 105
+}

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_106/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,172 @@
+
+import numpy as np
+import os
+import json
+from typing import Dict, Any
+import math
+
+def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:
+    metrics = {}
+    
+    # Initialize centers and radii to empty arrays for robustness
+    centers = np.array([])
+    radii = np.array([])
+    N_EXPECTED = 26 # Define N_EXPECTED early for consistent use
+
+    # Try to load the extra.npz file for detailed data
+    try:
+        extra_data_path = os.path.join(results_dir, "extra.npz")
+        if os.path.exists(extra_data_path):
+            with np.load(extra_data_path) as data:
+                centers = data["centers"]
+                radii = data["radii"]
+        else:
+            print(f"Warning: {extra_data_path} not found. Some metrics might be 0.")
+    except Exception as e:
+        print(f"Error loading extra.npz: {e}")
+        # If error, centers and radii remain empty arrays, handled below
+
+    # Get primary validation status
+    primary_correct_flag = False
+    if primary_result and "correct" in primary_result:
+        primary_correct_flag = primary_result["correct"]
+    else: # Fallback to metrics.json if primary_result is incomplete
+        metrics_json_path = os.path.join(results_dir, "results/metrics.json") # Correct path for metrics.json
+        if os.path.exists(metrics_json_path):
+            try:
+                with open(metrics_json_path, 'r') as f:
+                    json_data = json.load(f)
+                    if "correct" in json_data:
+                        primary_correct_flag = json_data["correct"]
+                    elif "public" in json_data and "correct" in json_data["public"]:
+                         primary_correct_flag = json_data["public"]["correct"]
+                    elif "private" in json_data and "correct" in json_data["private"]:
+                         primary_correct_flag = json_data["private"]["correct"]
+            except Exception as e:
+                print(f"Error loading metrics.json for primary_correct_flag: {e}")
+
+
+    # Metric 1: is_valid_packing (Binary correctness signal)
+    try:
+        metrics["is_valid_packing"] = 1.0 if primary_correct_flag else 0.0
+    except Exception as e:
+        print(f"Error calculating is_valid_packing: {e}")
+        metrics["is_valid_packing"] = 0.0
+
+    # Metric: avg_dist_to_center
+    try:
+        if centers.shape[0] == N_EXPECTED:
+            square_center = np.array([0.5, 0.5])
+            distances = np.linalg.norm(centers - square_center, axis=1)
+            metrics["avg_dist_to_center"] = float(np.mean(distances))
+        else:
+            metrics["avg_dist_to_center"] = 0.0
+    except Exception as e:
+        print(f"Error calculating avg_dist_to_center: {e}")
+        metrics["avg_dist_to_center"] = 0.0
+
+    # If no valid data or incorrect number of circles, set dependent metrics to 0
+    # Also set num_circles_generated to actual count even if it's not N_EXPECTED
+    if centers.size == 0 or radii.size == 0:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["num_circles_generated"] = 0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics
+
+    # Metric: num_circles_generated (actual count)
+    try:
+        metrics["num_circles_generated"] = centers.shape[0]
+    except Exception as e:
+        print(f"Error calculating num_circles_generated: {e}")
+        metrics["num_circles_generated"] = 0
+        
+    # Check if the number of circles matches N_EXPECTED before calculating other metrics
+    if centers.shape[0] != N_EXPECTED or radii.shape[0] != N_EXPECTED:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics # Exit early if counts are wrong, as other metrics might be nonsensical
+
+    # All subsequent metrics assume N_EXPECTED circles are present and no NaNs/Infs
+
+    # Metric 2: mean_radius
+    try:
+        metrics["mean_radius"] = float(np.mean(radii))
+    except Exception as e:
+        print(f"Error calculating mean_radius: {e}")
+        metrics["mean_radius"] = 0.0
+
+    # Metric 3: radius_std_dev
+    try:
+        metrics["radius_std_dev"] = float(np.std(radii)) if len(radii) > 1 else 0.0
+    except Exception as e:
+        print(f"Error calculating radius_std_dev: {e}")
+        metrics["radius_std_dev"] = 0.0
+
+    # Metric 4: total_packed_area
+    try:
+        metrics["total_packed_area"] = float(np.sum(np.pi * radii**2))
+    except Exception as e:
+        print(f"Error calculating total_packed_area: {e}")
+        metrics["total_packed_area"] = 0.0
+
+    # Metric 5: min_clearance_to_boundary
+    try:
+        min_clearance = float('inf')
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            clearances = [x - r, 1 - (x + r), y - r, 1 - (y + r)]
+            min_clearance = min(min_clearance, *clearances)
+        metrics["min_clearance_to_boundary"] = min_clearance if math.isfinite(min_clearance) else 0.0
+    except Exception as e:
+        print(f"Error calculating min_clearance_to_boundary: {e}")
+        metrics["min_clearance_to_boundary"] = 0.0
+
+    # Metric 6: max_overlap_value
+    try:
+        max_overlap = 0.0 # Changed from -inf, as overlap should be >= 0 for this metric
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                overlap = (radii[i] + radii[j]) - dist
+                if overlap > max_overlap:
+                    max_overlap = overlap
+        metrics["max_overlap_value"] = max_overlap
+    except Exception as e:
+        print(f"Error calculating max_overlap_value: {e}")
+        metrics["max_overlap_value"] = 0.0
+
+    # Metric 8: min_radius
+    try:
+        metrics["min_radius"] = float(np.min(radii))
+    except Exception as e:
+        print(f"Error calculating min_radius: {e}")
+        metrics["min_radius"] = 0.0
+
+    # Metric 10: boundary_contact_count
+    try:
+        contact_count = 0
+        epsilon = 1e-6 # Tolerance for floating point comparisons
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            # Check if touching any of the four boundaries
+            if abs(x - r) < epsilon or abs(1 - (x + r)) < epsilon or \
+               abs(y - r) < epsilon or abs(1 - (y + r)) < epsilon:
+                contact_count += 1
+        metrics["boundary_contact_count"] = float(contact_count)
+    except Exception as e:
+        print(f"Error calculating boundary_contact_count: {e}")
+        metrics["boundary_contact_count"] = 0
+
+    return metrics

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_106/results/correct.json

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

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_106/results/metrics.json

@@ -0,0 +1,47 @@
+{
+  "combined_score": 2.603588180078524,
+  "correct": true,
+  "primary": {
+    "combined_score": 2.603588180078524,
+    "public": {
+      "centers_str": "  centers[0] = (0.0922, 0.0922)\n  centers[1] = (0.2800, 0.0958)\n  centers[2] = (0.3694, 0.2724)\n  centers[3] = (0.4757, 0.1001)\n  centers[4] = (0.6817, 0.1058)\n  centers[5] = (0.8937, 0.1060)\n  centers[6] = (0.1354, 0.3158)\n  centers[7] = (0.4734, 0.4298)\n  centers[8] = (0.3081, 0.4437)\n  centers[9] = (0.5685, 0.2735)\n  centers[10] = (0.6524, 0.4435)\n  centers[11] = (0.7574, 0.2897)\n  centers[12] = (0.9252, 0.2839)\n  centers[13] = (0.1444, 0.5965)\n  centers[14] = (0.3963, 0.5862)\n  centers[15] = (0.3269, 0.7658)\n  centers[16] = (0.5219, 0.7205)\n  centers[17] = (0.6980, 0.6324)\n  centers[18] = (0.5474, 0.5618)\n  centers[19] = (0.8712, 0.4803)\n  centers[20] = (0.1250, 0.8748)\n  centers[21] = (0.3058, 0.9345)\n  centers[22] = (0.4711, 0.9046)\n  centers[23] = (0.6981, 0.8668)\n  centers[24] = (0.8904, 0.7185)\n  centers[25] = (0.9129, 0.9134)",
+      "num_circles": 26
+    },
+    "private": {
+      "reported_sum_of_radii": 2.603588180078524
+    },
+    "execution_time_mean": 15.023704247549176,
+    "execution_time_std": 0.0,
+    "num_valid_runs": 1,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": true,
+    "validation_error": null
+  },
+  "auxiliary": {
+    "is_valid_packing": 1.0,
+    "avg_dist_to_center": 0.35282780739916286,
+    "num_circles_generated": 26,
+    "mean_radius": 0.10013800692609708,
+    "radius_std_dev": 0.019777605238614774,
+    "total_packed_area": 0.8510201484798829,
+    "min_clearance_to_boundary": 0.0,
+    "max_overlap_value": 0.0,
+    "min_radius": 0.06502791892219674,
+    "boundary_contact_count": 14.0
+  },
+  "auxiliary_descriptions": {
+    "mean_radius": "Average radius of all circles.",
+    "radius_std_dev": "Standard deviation of radii.",
+    "total_packed_area": "Total area covered by all circles.",
+    "min_clearance_to_boundary": "Minimum distance of any circle to the unit square boundary.",
+    "max_overlap_value": "Maximum overlap between any two circles.",
+    "is_valid_packing": "Binary flag indicating if the packing is valid (no overlaps, in bounds, correct shapes).",
+    "num_circles_generated": "The actual number of circles generated by the solution.",
+    "min_radius": "The minimum radius among all circles in the packing.",
+    "avg_dist_to_center": "Average distance of circle centers to the center of the unit square, indicating compactness.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries, indicating effective utilization of space."
+  },
+  "timestamp": 1770931850.5854654,
+  "generation": 106
+}

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_108/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,172 @@
+
+import numpy as np
+import os
+import json
+from typing import Dict, Any
+import math
+
+def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:
+    metrics = {}
+    
+    # Initialize centers and radii to empty arrays for robustness
+    centers = np.array([])
+    radii = np.array([])
+    N_EXPECTED = 26 # Define N_EXPECTED early for consistent use
+
+    # Try to load the extra.npz file for detailed data
+    try:
+        extra_data_path = os.path.join(results_dir, "extra.npz")
+        if os.path.exists(extra_data_path):
+            with np.load(extra_data_path) as data:
+                centers = data["centers"]
+                radii = data["radii"]
+        else:
+            print(f"Warning: {extra_data_path} not found. Some metrics might be 0.")
+    except Exception as e:
+        print(f"Error loading extra.npz: {e}")
+        # If error, centers and radii remain empty arrays, handled below
+
+    # Get primary validation status
+    primary_correct_flag = False
+    if primary_result and "correct" in primary_result:
+        primary_correct_flag = primary_result["correct"]
+    else: # Fallback to metrics.json if primary_result is incomplete
+        metrics_json_path = os.path.join(results_dir, "results/metrics.json") # Correct path for metrics.json
+        if os.path.exists(metrics_json_path):
+            try:
+                with open(metrics_json_path, 'r') as f:
+                    json_data = json.load(f)
+                    if "correct" in json_data:
+                        primary_correct_flag = json_data["correct"]
+                    elif "public" in json_data and "correct" in json_data["public"]:
+                         primary_correct_flag = json_data["public"]["correct"]
+                    elif "private" in json_data and "correct" in json_data["private"]:
+                         primary_correct_flag = json_data["private"]["correct"]
+            except Exception as e:
+                print(f"Error loading metrics.json for primary_correct_flag: {e}")
+
+
+    # Metric 1: is_valid_packing (Binary correctness signal)
+    try:
+        metrics["is_valid_packing"] = 1.0 if primary_correct_flag else 0.0
+    except Exception as e:
+        print(f"Error calculating is_valid_packing: {e}")
+        metrics["is_valid_packing"] = 0.0
+
+    # Metric: avg_dist_to_center
+    try:
+        if centers.shape[0] == N_EXPECTED:
+            square_center = np.array([0.5, 0.5])
+            distances = np.linalg.norm(centers - square_center, axis=1)
+            metrics["avg_dist_to_center"] = float(np.mean(distances))
+        else:
+            metrics["avg_dist_to_center"] = 0.0
+    except Exception as e:
+        print(f"Error calculating avg_dist_to_center: {e}")
+        metrics["avg_dist_to_center"] = 0.0
+
+    # If no valid data or incorrect number of circles, set dependent metrics to 0
+    # Also set num_circles_generated to actual count even if it's not N_EXPECTED
+    if centers.size == 0 or radii.size == 0:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["num_circles_generated"] = 0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics
+
+    # Metric: num_circles_generated (actual count)
+    try:
+        metrics["num_circles_generated"] = centers.shape[0]
+    except Exception as e:
+        print(f"Error calculating num_circles_generated: {e}")
+        metrics["num_circles_generated"] = 0
+        
+    # Check if the number of circles matches N_EXPECTED before calculating other metrics
+    if centers.shape[0] != N_EXPECTED or radii.shape[0] != N_EXPECTED:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics # Exit early if counts are wrong, as other metrics might be nonsensical
+
+    # All subsequent metrics assume N_EXPECTED circles are present and no NaNs/Infs
+
+    # Metric 2: mean_radius
+    try:
+        metrics["mean_radius"] = float(np.mean(radii))
+    except Exception as e:
+        print(f"Error calculating mean_radius: {e}")
+        metrics["mean_radius"] = 0.0
+
+    # Metric 3: radius_std_dev
+    try:
+        metrics["radius_std_dev"] = float(np.std(radii)) if len(radii) > 1 else 0.0
+    except Exception as e:
+        print(f"Error calculating radius_std_dev: {e}")
+        metrics["radius_std_dev"] = 0.0
+
+    # Metric 4: total_packed_area
+    try:
+        metrics["total_packed_area"] = float(np.sum(np.pi * radii**2))
+    except Exception as e:
+        print(f"Error calculating total_packed_area: {e}")
+        metrics["total_packed_area"] = 0.0
+
+    # Metric 5: min_clearance_to_boundary
+    try:
+        min_clearance = float('inf')
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            clearances = [x - r, 1 - (x + r), y - r, 1 - (y + r)]
+            min_clearance = min(min_clearance, *clearances)
+        metrics["min_clearance_to_boundary"] = min_clearance if math.isfinite(min_clearance) else 0.0
+    except Exception as e:
+        print(f"Error calculating min_clearance_to_boundary: {e}")
+        metrics["min_clearance_to_boundary"] = 0.0
+
+    # Metric 6: max_overlap_value
+    try:
+        max_overlap = 0.0 # Changed from -inf, as overlap should be >= 0 for this metric
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                overlap = (radii[i] + radii[j]) - dist
+                if overlap > max_overlap:
+                    max_overlap = overlap
+        metrics["max_overlap_value"] = max_overlap
+    except Exception as e:
+        print(f"Error calculating max_overlap_value: {e}")
+        metrics["max_overlap_value"] = 0.0
+
+    # Metric 8: min_radius
+    try:
+        metrics["min_radius"] = float(np.min(radii))
+    except Exception as e:
+        print(f"Error calculating min_radius: {e}")
+        metrics["min_radius"] = 0.0
+
+    # Metric 10: boundary_contact_count
+    try:
+        contact_count = 0
+        epsilon = 1e-6 # Tolerance for floating point comparisons
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            # Check if touching any of the four boundaries
+            if abs(x - r) < epsilon or abs(1 - (x + r)) < epsilon or \
+               abs(y - r) < epsilon or abs(1 - (y + r)) < epsilon:
+                contact_count += 1
+        metrics["boundary_contact_count"] = float(contact_count)
+    except Exception as e:
+        print(f"Error calculating boundary_contact_count: {e}")
+        metrics["boundary_contact_count"] = 0
+
+    return metrics

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_108/results/correct.json

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

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_108/results/metrics.json

@@ -0,0 +1,47 @@
+{
+  "combined_score": 2.599656357093383,
+  "correct": true,
+  "primary": {
+    "combined_score": 2.599656357093383,
+    "public": {
+      "centers_str": "  centers[0] = (0.0885, 0.0884)\n  centers[1] = (0.2781, 0.1017)\n  centers[2] = (0.4987, 0.1196)\n  centers[3] = (0.7269, 0.1089)\n  centers[4] = (0.9170, 0.0830)\n  centers[5] = (0.1340, 0.3060)\n  centers[6] = (0.3583, 0.2780)\n  centers[7] = (0.4836, 0.4035)\n  centers[8] = (0.6316, 0.2936)\n  centers[9] = (0.8776, 0.2846)\n  centers[10] = (0.1134, 0.5527)\n  centers[11] = (0.3074, 0.4633)\n  centers[12] = (0.4497, 0.5533)\n  centers[13] = (0.5938, 0.5251)\n  centers[14] = (0.7413, 0.4459)\n  centers[15] = (0.9044, 0.5317)\n  centers[16] = (0.0980, 0.7635)\n  centers[17] = (0.3109, 0.6907)\n  centers[18] = (0.5400, 0.6977)\n  centers[19] = (0.7366, 0.6382)\n  centers[20] = (0.9118, 0.7153)\n  centers[21] = (0.0704, 0.9296)\n  centers[22] = (0.2361, 0.9024)\n  centers[23] = (0.4446, 0.8886)\n  centers[24] = (0.6803, 0.8755)\n  centers[25] = (0.9016, 0.9016)",
+      "num_circles": 26
+    },
+    "private": {
+      "reported_sum_of_radii": 2.599656357093383
+    },
+    "execution_time_mean": 23.100646714679897,
+    "execution_time_std": 0.0,
+    "num_valid_runs": 1,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": true,
+    "validation_error": null
+  },
+  "auxiliary": {
+    "is_valid_packing": 1.0,
+    "avg_dist_to_center": 0.36462229842860683,
+    "num_circles_generated": 26,
+    "mean_radius": 0.09998678296513011,
+    "radius_std_dev": 0.016554252962000086,
+    "total_packed_area": 0.8389824291407547,
+    "min_clearance_to_boundary": 0.0,
+    "max_overlap_value": 0.0,
+    "min_radius": 0.06821299107750015,
+    "boundary_contact_count": 6.0
+  },
+  "auxiliary_descriptions": {
+    "mean_radius": "Average radius of all circles.",
+    "radius_std_dev": "Standard deviation of radii.",
+    "total_packed_area": "Total area covered by all circles.",
+    "min_clearance_to_boundary": "Minimum distance of any circle to the unit square boundary.",
+    "max_overlap_value": "Maximum overlap between any two circles.",
+    "is_valid_packing": "Binary flag indicating if the packing is valid (no overlaps, in bounds, correct shapes).",
+    "num_circles_generated": "The actual number of circles generated by the solution.",
+    "min_radius": "The minimum radius among all circles in the packing.",
+    "avg_dist_to_center": "Average distance of circle centers to the center of the unit square, indicating compactness.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries, indicating effective utilization of space."
+  },
+  "timestamp": 1770931983.354642,
+  "generation": 108
+}

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_110/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,172 @@
+
+import numpy as np
+import os
+import json
+from typing import Dict, Any
+import math
+
+def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:
+    metrics = {}
+    
+    # Initialize centers and radii to empty arrays for robustness
+    centers = np.array([])
+    radii = np.array([])
+    N_EXPECTED = 26 # Define N_EXPECTED early for consistent use
+
+    # Try to load the extra.npz file for detailed data
+    try:
+        extra_data_path = os.path.join(results_dir, "extra.npz")
+        if os.path.exists(extra_data_path):
+            with np.load(extra_data_path) as data:
+                centers = data["centers"]
+                radii = data["radii"]
+        else:
+            print(f"Warning: {extra_data_path} not found. Some metrics might be 0.")
+    except Exception as e:
+        print(f"Error loading extra.npz: {e}")
+        # If error, centers and radii remain empty arrays, handled below
+
+    # Get primary validation status
+    primary_correct_flag = False
+    if primary_result and "correct" in primary_result:
+        primary_correct_flag = primary_result["correct"]
+    else: # Fallback to metrics.json if primary_result is incomplete
+        metrics_json_path = os.path.join(results_dir, "results/metrics.json") # Correct path for metrics.json
+        if os.path.exists(metrics_json_path):
+            try:
+                with open(metrics_json_path, 'r') as f:
+                    json_data = json.load(f)
+                    if "correct" in json_data:
+                        primary_correct_flag = json_data["correct"]
+                    elif "public" in json_data and "correct" in json_data["public"]:
+                         primary_correct_flag = json_data["public"]["correct"]
+                    elif "private" in json_data and "correct" in json_data["private"]:
+                         primary_correct_flag = json_data["private"]["correct"]
+            except Exception as e:
+                print(f"Error loading metrics.json for primary_correct_flag: {e}")
+
+
+    # Metric 1: is_valid_packing (Binary correctness signal)
+    try:
+        metrics["is_valid_packing"] = 1.0 if primary_correct_flag else 0.0
+    except Exception as e:
+        print(f"Error calculating is_valid_packing: {e}")
+        metrics["is_valid_packing"] = 0.0
+
+    # Metric: avg_dist_to_center
+    try:
+        if centers.shape[0] == N_EXPECTED:
+            square_center = np.array([0.5, 0.5])
+            distances = np.linalg.norm(centers - square_center, axis=1)
+            metrics["avg_dist_to_center"] = float(np.mean(distances))
+        else:
+            metrics["avg_dist_to_center"] = 0.0
+    except Exception as e:
+        print(f"Error calculating avg_dist_to_center: {e}")
+        metrics["avg_dist_to_center"] = 0.0
+
+    # If no valid data or incorrect number of circles, set dependent metrics to 0
+    # Also set num_circles_generated to actual count even if it's not N_EXPECTED
+    if centers.size == 0 or radii.size == 0:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["num_circles_generated"] = 0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics
+
+    # Metric: num_circles_generated (actual count)
+    try:
+        metrics["num_circles_generated"] = centers.shape[0]
+    except Exception as e:
+        print(f"Error calculating num_circles_generated: {e}")
+        metrics["num_circles_generated"] = 0
+        
+    # Check if the number of circles matches N_EXPECTED before calculating other metrics
+    if centers.shape[0] != N_EXPECTED or radii.shape[0] != N_EXPECTED:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics # Exit early if counts are wrong, as other metrics might be nonsensical
+
+    # All subsequent metrics assume N_EXPECTED circles are present and no NaNs/Infs
+
+    # Metric 2: mean_radius
+    try:
+        metrics["mean_radius"] = float(np.mean(radii))
+    except Exception as e:
+        print(f"Error calculating mean_radius: {e}")
+        metrics["mean_radius"] = 0.0
+
+    # Metric 3: radius_std_dev
+    try:
+        metrics["radius_std_dev"] = float(np.std(radii)) if len(radii) > 1 else 0.0
+    except Exception as e:
+        print(f"Error calculating radius_std_dev: {e}")
+        metrics["radius_std_dev"] = 0.0
+
+    # Metric 4: total_packed_area
+    try:
+        metrics["total_packed_area"] = float(np.sum(np.pi * radii**2))
+    except Exception as e:
+        print(f"Error calculating total_packed_area: {e}")
+        metrics["total_packed_area"] = 0.0
+
+    # Metric 5: min_clearance_to_boundary
+    try:
+        min_clearance = float('inf')
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            clearances = [x - r, 1 - (x + r), y - r, 1 - (y + r)]
+            min_clearance = min(min_clearance, *clearances)
+        metrics["min_clearance_to_boundary"] = min_clearance if math.isfinite(min_clearance) else 0.0
+    except Exception as e:
+        print(f"Error calculating min_clearance_to_boundary: {e}")
+        metrics["min_clearance_to_boundary"] = 0.0
+
+    # Metric 6: max_overlap_value
+    try:
+        max_overlap = 0.0 # Changed from -inf, as overlap should be >= 0 for this metric
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                overlap = (radii[i] + radii[j]) - dist
+                if overlap > max_overlap:
+                    max_overlap = overlap
+        metrics["max_overlap_value"] = max_overlap
+    except Exception as e:
+        print(f"Error calculating max_overlap_value: {e}")
+        metrics["max_overlap_value"] = 0.0
+
+    # Metric 8: min_radius
+    try:
+        metrics["min_radius"] = float(np.min(radii))
+    except Exception as e:
+        print(f"Error calculating min_radius: {e}")
+        metrics["min_radius"] = 0.0
+
+    # Metric 10: boundary_contact_count
+    try:
+        contact_count = 0
+        epsilon = 1e-6 # Tolerance for floating point comparisons
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            # Check if touching any of the four boundaries
+            if abs(x - r) < epsilon or abs(1 - (x + r)) < epsilon or \
+               abs(y - r) < epsilon or abs(1 - (y + r)) < epsilon:
+                contact_count += 1
+        metrics["boundary_contact_count"] = float(contact_count)
+    except Exception as e:
+        print(f"Error calculating boundary_contact_count: {e}")
+        metrics["boundary_contact_count"] = 0
+
+    return metrics

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_110/results/correct.json

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

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_110/results/metrics.json

@@ -0,0 +1,47 @@
+{
+  "combined_score": 2.5853378579135846,
+  "correct": true,
+  "primary": {
+    "combined_score": 2.5853378579135846,
+    "public": {
+      "centers_str": "  centers[0] = (0.1206, 0.1207)\n  centers[1] = (0.2625, 0.2625)\n  centers[2] = (0.3409, 0.1002)\n  centers[3] = (0.5405, 0.0992)\n  centers[4] = (0.7385, 0.0987)\n  centers[5] = (0.9182, 0.0820)\n  centers[6] = (0.1070, 0.3663)\n  centers[7] = (0.3164, 0.4548)\n  centers[8] = (0.4429, 0.2737)\n  centers[9] = (0.6405, 0.2679)\n  centers[10] = (0.8681, 0.2897)\n  centers[11] = (0.1379, 0.5563)\n  centers[12] = (0.7028, 0.4540)\n  centers[13] = (0.5486, 0.3999)\n  centers[14] = (0.4951, 0.5251)\n  centers[15] = (0.8947, 0.5253)\n  centers[16] = (0.1066, 0.7459)\n  centers[17] = (0.3252, 0.6916)\n  centers[18] = (0.5453, 0.6928)\n  centers[19] = (0.7305, 0.6409)\n  centers[20] = (0.8985, 0.7323)\n  centers[21] = (0.0749, 0.9247)\n  centers[22] = (0.2501, 0.8977)\n  centers[23] = (0.4627, 0.8895)\n  centers[24] = (0.7054, 0.8661)\n  centers[25] = (0.9163, 0.9165)",
+      "num_circles": 26
+    },
+    "private": {
+      "reported_sum_of_radii": 2.5853378579135846
+    },
+    "execution_time_mean": 25.150721285492182,
+    "execution_time_std": 0.0,
+    "num_valid_runs": 1,
+    "num_invalid_runs": 0,
+    "all_validation_errors": [],
+    "correct": true,
+    "validation_error": null
+  },
+  "auxiliary": {
+    "is_valid_packing": 1.0,
+    "avg_dist_to_center": 0.3663982520663652,
+    "num_circles_generated": 26,
+    "mean_radius": 0.09943607145821479,
+    "radius_std_dev": 0.01698796410051645,
+    "total_packed_area": 0.8312000836243196,
+    "min_clearance_to_boundary": 0.0,
+    "max_overlap_value": 0.0,
+    "min_radius": 0.06393655738555767,
+    "boundary_contact_count": 11.0
+  },
+  "auxiliary_descriptions": {
+    "mean_radius": "Average radius of all circles.",
+    "radius_std_dev": "Standard deviation of radii.",
+    "total_packed_area": "Total area covered by all circles.",
+    "min_clearance_to_boundary": "Minimum distance of any circle to the unit square boundary.",
+    "max_overlap_value": "Maximum overlap between any two circles.",
+    "is_valid_packing": "Binary flag indicating if the packing is valid (no overlaps, in bounds, correct shapes).",
+    "num_circles_generated": "The actual number of circles generated by the solution.",
+    "min_radius": "The minimum radius among all circles in the packing.",
+    "avg_dist_to_center": "Average distance of circle centers to the center of the unit square, indicating compactness.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries, indicating effective utilization of space."
+  },
+  "timestamp": 1770932121.5753243,
+  "generation": 110
+}

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_111/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,172 @@
+
+import numpy as np
+import os
+import json
+from typing import Dict, Any
+import math
+
+def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:
+    metrics = {}
+    
+    # Initialize centers and radii to empty arrays for robustness
+    centers = np.array([])
+    radii = np.array([])
+    N_EXPECTED = 26 # Define N_EXPECTED early for consistent use
+
+    # Try to load the extra.npz file for detailed data
+    try:
+        extra_data_path = os.path.join(results_dir, "extra.npz")
+        if os.path.exists(extra_data_path):
+            with np.load(extra_data_path) as data:
+                centers = data["centers"]
+                radii = data["radii"]
+        else:
+            print(f"Warning: {extra_data_path} not found. Some metrics might be 0.")
+    except Exception as e:
+        print(f"Error loading extra.npz: {e}")
+        # If error, centers and radii remain empty arrays, handled below
+
+    # Get primary validation status
+    primary_correct_flag = False
+    if primary_result and "correct" in primary_result:
+        primary_correct_flag = primary_result["correct"]
+    else: # Fallback to metrics.json if primary_result is incomplete
+        metrics_json_path = os.path.join(results_dir, "results/metrics.json") # Correct path for metrics.json
+        if os.path.exists(metrics_json_path):
+            try:
+                with open(metrics_json_path, 'r') as f:
+                    json_data = json.load(f)
+                    if "correct" in json_data:
+                        primary_correct_flag = json_data["correct"]
+                    elif "public" in json_data and "correct" in json_data["public"]:
+                         primary_correct_flag = json_data["public"]["correct"]
+                    elif "private" in json_data and "correct" in json_data["private"]:
+                         primary_correct_flag = json_data["private"]["correct"]
+            except Exception as e:
+                print(f"Error loading metrics.json for primary_correct_flag: {e}")
+
+
+    # Metric 1: is_valid_packing (Binary correctness signal)
+    try:
+        metrics["is_valid_packing"] = 1.0 if primary_correct_flag else 0.0
+    except Exception as e:
+        print(f"Error calculating is_valid_packing: {e}")
+        metrics["is_valid_packing"] = 0.0
+
+    # Metric: avg_dist_to_center
+    try:
+        if centers.shape[0] == N_EXPECTED:
+            square_center = np.array([0.5, 0.5])
+            distances = np.linalg.norm(centers - square_center, axis=1)
+            metrics["avg_dist_to_center"] = float(np.mean(distances))
+        else:
+            metrics["avg_dist_to_center"] = 0.0
+    except Exception as e:
+        print(f"Error calculating avg_dist_to_center: {e}")
+        metrics["avg_dist_to_center"] = 0.0
+
+    # If no valid data or incorrect number of circles, set dependent metrics to 0
+    # Also set num_circles_generated to actual count even if it's not N_EXPECTED
+    if centers.size == 0 or radii.size == 0:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["num_circles_generated"] = 0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics
+
+    # Metric: num_circles_generated (actual count)
+    try:
+        metrics["num_circles_generated"] = centers.shape[0]
+    except Exception as e:
+        print(f"Error calculating num_circles_generated: {e}")
+        metrics["num_circles_generated"] = 0
+        
+    # Check if the number of circles matches N_EXPECTED before calculating other metrics
+    if centers.shape[0] != N_EXPECTED or radii.shape[0] != N_EXPECTED:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics # Exit early if counts are wrong, as other metrics might be nonsensical
+
+    # All subsequent metrics assume N_EXPECTED circles are present and no NaNs/Infs
+
+    # Metric 2: mean_radius
+    try:
+        metrics["mean_radius"] = float(np.mean(radii))
+    except Exception as e:
+        print(f"Error calculating mean_radius: {e}")
+        metrics["mean_radius"] = 0.0
+
+    # Metric 3: radius_std_dev
+    try:
+        metrics["radius_std_dev"] = float(np.std(radii)) if len(radii) > 1 else 0.0
+    except Exception as e:
+        print(f"Error calculating radius_std_dev: {e}")
+        metrics["radius_std_dev"] = 0.0
+
+    # Metric 4: total_packed_area
+    try:
+        metrics["total_packed_area"] = float(np.sum(np.pi * radii**2))
+    except Exception as e:
+        print(f"Error calculating total_packed_area: {e}")
+        metrics["total_packed_area"] = 0.0
+
+    # Metric 5: min_clearance_to_boundary
+    try:
+        min_clearance = float('inf')
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            clearances = [x - r, 1 - (x + r), y - r, 1 - (y + r)]
+            min_clearance = min(min_clearance, *clearances)
+        metrics["min_clearance_to_boundary"] = min_clearance if math.isfinite(min_clearance) else 0.0
+    except Exception as e:
+        print(f"Error calculating min_clearance_to_boundary: {e}")
+        metrics["min_clearance_to_boundary"] = 0.0
+
+    # Metric 6: max_overlap_value
+    try:
+        max_overlap = 0.0 # Changed from -inf, as overlap should be >= 0 for this metric
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                overlap = (radii[i] + radii[j]) - dist
+                if overlap > max_overlap:
+                    max_overlap = overlap
+        metrics["max_overlap_value"] = max_overlap
+    except Exception as e:
+        print(f"Error calculating max_overlap_value: {e}")
+        metrics["max_overlap_value"] = 0.0
+
+    # Metric 8: min_radius
+    try:
+        metrics["min_radius"] = float(np.min(radii))
+    except Exception as e:
+        print(f"Error calculating min_radius: {e}")
+        metrics["min_radius"] = 0.0
+
+    # Metric 10: boundary_contact_count
+    try:
+        contact_count = 0
+        epsilon = 1e-6 # Tolerance for floating point comparisons
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            # Check if touching any of the four boundaries
+            if abs(x - r) < epsilon or abs(1 - (x + r)) < epsilon or \
+               abs(y - r) < epsilon or abs(1 - (y + r)) < epsilon:
+                contact_count += 1
+        metrics["boundary_contact_count"] = float(contact_count)
+    except Exception as e:
+        print(f"Error calculating boundary_contact_count: {e}")
+        metrics["boundary_contact_count"] = 0
+
+    return metrics

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_111/results/correct.json

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

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_111/results/metrics.json

@@ -0,0 +1,41 @@
+{
+  "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": "NameError: name 'np' is not defined"
+  },
+  "auxiliary": {
+    "is_valid_packing": 0.0,
+    "avg_dist_to_center": 0.0,
+    "mean_radius": 0.0,
+    "radius_std_dev": 0.0,
+    "total_packed_area": 0.0,
+    "min_clearance_to_boundary": 0.0,
+    "max_overlap_value": 0.0,
+    "num_circles_generated": 0,
+    "min_radius": 0.0,
+    "boundary_contact_count": 0
+  },
+  "auxiliary_descriptions": {
+    "mean_radius": "Average radius of all circles.",
+    "radius_std_dev": "Standard deviation of radii.",
+    "total_packed_area": "Total area covered by all circles.",
+    "min_clearance_to_boundary": "Minimum distance of any circle to the unit square boundary.",
+    "max_overlap_value": "Maximum overlap between any two circles.",
+    "is_valid_packing": "Binary flag indicating if the packing is valid (no overlaps, in bounds, correct shapes).",
+    "num_circles_generated": "The actual number of circles generated by the solution.",
+    "min_radius": "The minimum radius among all circles in the packing.",
+    "avg_dist_to_center": "Average distance of circle centers to the center of the unit square, indicating compactness.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries, indicating effective utilization of space."
+  },
+  "timestamp": 1770932154.5226638,
+  "generation": 111
+}

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_114/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,172 @@
+
+import numpy as np
+import os
+import json
+from typing import Dict, Any
+import math
+
+def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:
+    metrics = {}
+    
+    # Initialize centers and radii to empty arrays for robustness
+    centers = np.array([])
+    radii = np.array([])
+    N_EXPECTED = 26 # Define N_EXPECTED early for consistent use
+
+    # Try to load the extra.npz file for detailed data
+    try:
+        extra_data_path = os.path.join(results_dir, "extra.npz")
+        if os.path.exists(extra_data_path):
+            with np.load(extra_data_path) as data:
+                centers = data["centers"]
+                radii = data["radii"]
+        else:
+            print(f"Warning: {extra_data_path} not found. Some metrics might be 0.")
+    except Exception as e:
+        print(f"Error loading extra.npz: {e}")
+        # If error, centers and radii remain empty arrays, handled below
+
+    # Get primary validation status
+    primary_correct_flag = False
+    if primary_result and "correct" in primary_result:
+        primary_correct_flag = primary_result["correct"]
+    else: # Fallback to metrics.json if primary_result is incomplete
+        metrics_json_path = os.path.join(results_dir, "results/metrics.json") # Correct path for metrics.json
+        if os.path.exists(metrics_json_path):
+            try:
+                with open(metrics_json_path, 'r') as f:
+                    json_data = json.load(f)
+                    if "correct" in json_data:
+                        primary_correct_flag = json_data["correct"]
+                    elif "public" in json_data and "correct" in json_data["public"]:
+                         primary_correct_flag = json_data["public"]["correct"]
+                    elif "private" in json_data and "correct" in json_data["private"]:
+                         primary_correct_flag = json_data["private"]["correct"]
+            except Exception as e:
+                print(f"Error loading metrics.json for primary_correct_flag: {e}")
+
+
+    # Metric 1: is_valid_packing (Binary correctness signal)
+    try:
+        metrics["is_valid_packing"] = 1.0 if primary_correct_flag else 0.0
+    except Exception as e:
+        print(f"Error calculating is_valid_packing: {e}")
+        metrics["is_valid_packing"] = 0.0
+
+    # Metric: avg_dist_to_center
+    try:
+        if centers.shape[0] == N_EXPECTED:
+            square_center = np.array([0.5, 0.5])
+            distances = np.linalg.norm(centers - square_center, axis=1)
+            metrics["avg_dist_to_center"] = float(np.mean(distances))
+        else:
+            metrics["avg_dist_to_center"] = 0.0
+    except Exception as e:
+        print(f"Error calculating avg_dist_to_center: {e}")
+        metrics["avg_dist_to_center"] = 0.0
+
+    # If no valid data or incorrect number of circles, set dependent metrics to 0
+    # Also set num_circles_generated to actual count even if it's not N_EXPECTED
+    if centers.size == 0 or radii.size == 0:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["num_circles_generated"] = 0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics
+
+    # Metric: num_circles_generated (actual count)
+    try:
+        metrics["num_circles_generated"] = centers.shape[0]
+    except Exception as e:
+        print(f"Error calculating num_circles_generated: {e}")
+        metrics["num_circles_generated"] = 0
+        
+    # Check if the number of circles matches N_EXPECTED before calculating other metrics
+    if centers.shape[0] != N_EXPECTED or radii.shape[0] != N_EXPECTED:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics # Exit early if counts are wrong, as other metrics might be nonsensical
+
+    # All subsequent metrics assume N_EXPECTED circles are present and no NaNs/Infs
+
+    # Metric 2: mean_radius
+    try:
+        metrics["mean_radius"] = float(np.mean(radii))
+    except Exception as e:
+        print(f"Error calculating mean_radius: {e}")
+        metrics["mean_radius"] = 0.0
+
+    # Metric 3: radius_std_dev
+    try:
+        metrics["radius_std_dev"] = float(np.std(radii)) if len(radii) > 1 else 0.0
+    except Exception as e:
+        print(f"Error calculating radius_std_dev: {e}")
+        metrics["radius_std_dev"] = 0.0
+
+    # Metric 4: total_packed_area
+    try:
+        metrics["total_packed_area"] = float(np.sum(np.pi * radii**2))
+    except Exception as e:
+        print(f"Error calculating total_packed_area: {e}")
+        metrics["total_packed_area"] = 0.0
+
+    # Metric 5: min_clearance_to_boundary
+    try:
+        min_clearance = float('inf')
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            clearances = [x - r, 1 - (x + r), y - r, 1 - (y + r)]
+            min_clearance = min(min_clearance, *clearances)
+        metrics["min_clearance_to_boundary"] = min_clearance if math.isfinite(min_clearance) else 0.0
+    except Exception as e:
+        print(f"Error calculating min_clearance_to_boundary: {e}")
+        metrics["min_clearance_to_boundary"] = 0.0
+
+    # Metric 6: max_overlap_value
+    try:
+        max_overlap = 0.0 # Changed from -inf, as overlap should be >= 0 for this metric
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                overlap = (radii[i] + radii[j]) - dist
+                if overlap > max_overlap:
+                    max_overlap = overlap
+        metrics["max_overlap_value"] = max_overlap
+    except Exception as e:
+        print(f"Error calculating max_overlap_value: {e}")
+        metrics["max_overlap_value"] = 0.0
+
+    # Metric 8: min_radius
+    try:
+        metrics["min_radius"] = float(np.min(radii))
+    except Exception as e:
+        print(f"Error calculating min_radius: {e}")
+        metrics["min_radius"] = 0.0
+
+    # Metric 10: boundary_contact_count
+    try:
+        contact_count = 0
+        epsilon = 1e-6 # Tolerance for floating point comparisons
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            # Check if touching any of the four boundaries
+            if abs(x - r) < epsilon or abs(1 - (x + r)) < epsilon or \
+               abs(y - r) < epsilon or abs(1 - (y + r)) < epsilon:
+                contact_count += 1
+        metrics["boundary_contact_count"] = float(contact_count)
+    except Exception as e:
+        print(f"Error calculating boundary_contact_count: {e}")
+        metrics["boundary_contact_count"] = 0
+
+    return metrics

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_116/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,187 @@
+
+import numpy as np
+import os
+import json
+from typing import Dict, Any
+import math
+
+auxiliary_metric_descriptions = {
+    "execution_successful": "Binary flag: 1.0 if the program executed without fundamental runtime errors (e.g., NameError); 0.0 otherwise.",
+    "avg_dist_to_center": "Average Euclidean distance of circle centers from the center of the unit square (0.5, 0.5), indicating compactness. Only calculated for valid packings.",
+    "mean_radius": "Average radius of all circles. Only calculated for valid packings.",
+    "radius_std_dev": "Standard deviation of radii. Only calculated for valid packings.",
+    "total_packed_area": "Total area covered by all circles (sum of pi * r^2). Only calculated for valid packings.",
+    "min_clearance_to_boundary": "Minimum distance of any circle (edge) to the unit square boundary. A negative value indicates a circle is out of bounds. Only calculated for valid packings.",
+    "max_overlap_value": "Maximum overlap between any two circles. 0.0 if no overlap. Only calculated for valid packings.",
+    "num_tangent_pairs": "Counts the number of pairs of circles that are tangent (touching without overlapping) within a small tolerance. Indicates efficient packing.",
+    "min_radius": "The minimum radius among all circles in the packing. Only calculated for valid packings.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries (within a small epsilon). Indicates effective utilization of space. Only calculated for valid packings."
+}
+def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:
+    metrics = {}
+    
+    # Initialize centers and radii to empty arrays for robustness
+    centers = np.array([])
+    radii = np.array([])
+    N_EXPECTED = 26 # Define N_EXPECTED early for consistent use
+
+    # Try to load the extra.npz file for detailed data
+    try:
+        extra_data_path = os.path.join(results_dir, "extra.npz")
+        if os.path.exists(extra_data_path):
+            with np.load(extra_data_path) as data:
+                centers = data["centers"]
+                radii = data["radii"]
+        else:
+            print(f"Warning: {extra_data_path} not found. Some metrics might be 0.")
+    except Exception as e:
+        print(f"Error loading extra.npz: {e}")
+        # If error, centers and radii remain empty arrays, handled below
+
+    # Get primary validation status
+    primary_correct_flag = False
+    validation_error_str = None
+    if primary_result:
+        if "correct" in primary_result:
+            primary_correct_flag = primary_result["correct"]
+        if "primary" in primary_result and "validation_error" in primary_result["primary"]:
+            validation_error_str = primary_result["primary"]["validation_error"]
+    else: # Fallback to metrics.json if primary_result is incomplete
+        metrics_json_path = os.path.join(results_dir, "results/metrics.json") # Correct path for metrics.json
+        if os.path.exists(metrics_json_path):
+            try:
+                with open(metrics_json_path, 'r') as f:
+                    json_data = json.load(f)
+                    if "correct" in json_data:
+                        primary_correct_flag = json_data["correct"]
+                    elif "primary" in json_data and "correct" in json_data["primary"]:
+                        primary_correct_flag = json_data["primary"]["correct"]
+                    elif "primary" in json_data and "validation_error" in json_data["primary"]:
+                         validation_error_str = json_data["primary"]["validation_error"]
+            except Exception as e:
+                print(f"Error loading metrics.json for primary_correct_flag or validation_error: {e}")
+
+    # Metric: execution_successful (Binary flag for successful program execution)
+    try:
+        metrics["execution_successful"] = 1.0 if not validation_error_str else 0.0
+    except Exception as e:
+        print(f"Error calculating execution_successful: {e}")
+        metrics["execution_successful"] = 0.0
+
+    # Metric: avg_dist_to_center
+    try:
+        if centers.shape[0] == N_EXPECTED:
+            square_center = np.array([0.5, 0.5])
+            distances = np.linalg.norm(centers - square_center, axis=1)
+            metrics["avg_dist_to_center"] = float(np.mean(distances))
+        else:
+            metrics["avg_dist_to_center"] = 0.0
+    except Exception as e:
+        print(f"Error calculating avg_dist_to_center: {e}")
+        metrics["avg_dist_to_center"] = 0.0
+
+    # If no valid data or incorrect number of circles, set dependent metrics to 0
+    # Also set num_circles_generated to actual count even if it's not N_EXPECTED
+    if centers.size == 0 or radii.size == 0:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["num_circles_generated"] = 0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics
+
+    # Metric: num_circles_generated (actual count)
+    try:
+        metrics["num_circles_generated"] = centers.shape[0]
+    except Exception as e:
+        print(f"Error calculating num_circles_generated: {e}")
+        metrics["num_circles_generated"] = 0
+        
+    # Check if the number of circles matches N_EXPECTED before calculating other metrics
+    if centers.shape[0] != N_EXPECTED or radii.shape[0] != N_EXPECTED:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics # Exit early if counts are wrong, as other metrics might be nonsensical
+
+    # All subsequent metrics assume N_EXPECTED circles are present and no NaNs/Infs
+
+    # Metric 2: mean_radius
+    try:
+        metrics["mean_radius"] = float(np.mean(radii))
+    except Exception as e:
+        print(f"Error calculating mean_radius: {e}")
+        metrics["mean_radius"] = 0.0
+
+    # Metric 3: radius_std_dev
+    try:
+        metrics["radius_std_dev"] = float(np.std(radii)) if len(radii) > 1 else 0.0
+    except Exception as e:
+        print(f"Error calculating radius_std_dev: {e}")
+        metrics["radius_std_dev"] = 0.0
+
+    # Metric 4: total_packed_area
+    try:
+        metrics["total_packed_area"] = float(np.sum(np.pi * radii**2))
+    except Exception as e:
+        print(f"Error calculating total_packed_area: {e}")
+        metrics["total_packed_area"] = 0.0
+
+    # Metric 5: min_clearance_to_boundary
+    try:
+        min_clearance = float('inf')
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            clearances = [x - r, 1 - (x + r), y - r, 1 - (y + r)]
+            min_clearance = min(min_clearance, *clearances)
+        metrics["min_clearance_to_boundary"] = min_clearance if math.isfinite(min_clearance) else 0.0
+    except Exception as e:
+        print(f"Error calculating min_clearance_to_boundary: {e}")
+        metrics["min_clearance_to_boundary"] = 0.0
+
+    # Metric 6: max_overlap_value
+    try:
+        max_overlap = 0.0 # Changed from -inf, as overlap should be >= 0 for this metric
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                overlap = (radii[i] + radii[j]) - dist
+                if overlap > max_overlap:
+                    max_overlap = overlap
+        metrics["max_overlap_value"] = max_overlap
+    except Exception as e:
+        print(f"Error calculating max_overlap_value: {e}")
+        metrics["max_overlap_value"] = 0.0
+
+    # Metric 8: min_radius
+    try:
+        metrics["min_radius"] = float(np.min(radii))
+    except Exception as e:
+        print(f"Error calculating min_radius: {e}")
+        metrics["min_radius"] = 0.0
+
+    # Metric 10: boundary_contact_count
+    try:
+        contact_count = 0
+        epsilon = 1e-6 # Tolerance for floating point comparisons
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            # Check if touching any of the four boundaries
+            if abs(x - r) < epsilon or abs(1 - (x + r)) < epsilon or \
+               abs(y - r) < epsilon or abs(1 - (y + r)) < epsilon:
+                contact_count += 1
+        metrics["boundary_contact_count"] = float(contact_count)
+    except Exception as e:
+        print(f"Error calculating boundary_contact_count: {e}")
+        metrics["boundary_contact_count"] = 0
+
+    return metrics

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_116/results/correct.json

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

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_116/results/metrics.json

@@ -0,0 +1,41 @@
+{
+  "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": "NameError: name 'np' is not defined"
+  },
+  "auxiliary": {
+    "execution_successful": 1.0,
+    "avg_dist_to_center": 0.0,
+    "mean_radius": 0.0,
+    "radius_std_dev": 0.0,
+    "total_packed_area": 0.0,
+    "min_clearance_to_boundary": 0.0,
+    "max_overlap_value": 0.0,
+    "num_circles_generated": 0,
+    "min_radius": 0.0,
+    "boundary_contact_count": 0
+  },
+  "auxiliary_descriptions": {
+    "mean_radius": "Average radius of all circles.",
+    "radius_std_dev": "Standard deviation of radii.",
+    "total_packed_area": "Total area covered by all circles.",
+    "min_clearance_to_boundary": "Minimum distance of any circle to the unit square boundary.",
+    "max_overlap_value": "Maximum overlap between any two circles.",
+    "is_valid_packing": "Binary flag indicating if the packing is valid (no overlaps, in bounds, correct shapes).",
+    "num_circles_generated": "The actual number of circles generated by the solution.",
+    "min_radius": "The minimum radius among all circles in the packing.",
+    "avg_dist_to_center": "Average distance of circle centers to the center of the unit square, indicating compactness.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries, indicating effective utilization of space."
+  },
+  "timestamp": 1770932363.1500704,
+  "generation": 116
+}

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_118/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,198 @@
+
+import numpy as np
+import os
+import json
+from typing import Dict, Any
+import math
+
+auxiliary_metric_descriptions = {
+    "execution_successful": "Binary flag: 1.0 if the program executed without fundamental runtime errors (e.g., NameError); 0.0 otherwise.",
+    "avg_dist_to_center": "Average Euclidean distance of circle centers from the center of the unit square (0.5, 0.5), indicating compactness. Only calculated for valid packings.",
+    "mean_radius": "Average radius of all circles. Only calculated for valid packings.",
+    "radius_std_dev": "Standard deviation of radii. Only calculated for valid packings.",
+    "total_packed_area": "Total area covered by all circles (sum of pi * r^2). Only calculated for valid packings.",
+    "min_clearance_to_boundary": "Minimum distance of any circle (edge) to the unit square boundary. A negative value indicates a circle is out of bounds. Only calculated for valid packings.",
+    "max_overlap_value": "Maximum overlap between any two circles. 0.0 if no overlap. Only calculated for valid packings.",
+    "num_tangent_pairs": "Counts the number of pairs of circles that are tangent (touching without overlapping) within a small tolerance. Indicates efficient packing.",
+    "min_radius": "The minimum radius among all circles in the packing. Only calculated for valid packings.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries (within a small epsilon). Indicates effective utilization of space. Only calculated for valid packings."
+}
+def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:
+    metrics = {}
+    
+    # Initialize centers and radii to empty arrays for robustness
+    centers = np.array([])
+    radii = np.array([])
+    N_EXPECTED = 26 # Define N_EXPECTED early for consistent use
+
+    # Try to load the extra.npz file for detailed data
+    try:
+        extra_data_path = os.path.join(results_dir, "extra.npz")
+        if os.path.exists(extra_data_path):
+            with np.load(extra_data_path) as data:
+                centers = data["centers"]
+                radii = data["radii"]
+        else:
+            print(f"Warning: {extra_data_path} not found. Some metrics might be 0.")
+    except Exception as e:
+        print(f"Error loading extra.npz: {e}")
+        # If error, centers and radii remain empty arrays, handled below
+
+    # Get primary validation status
+    primary_correct_flag = False
+    validation_error_str = None
+    if primary_result:
+        if "correct" in primary_result:
+            primary_correct_flag = primary_result["correct"]
+        if "primary" in primary_result and "validation_error" in primary_result["primary"]:
+            validation_error_str = primary_result["primary"]["validation_error"]
+    else: # Fallback to metrics.json if primary_result is incomplete
+        metrics_json_path = os.path.join(results_dir, "results/metrics.json") # Correct path for metrics.json
+        if os.path.exists(metrics_json_path):
+            try:
+                with open(metrics_json_path, 'r') as f:
+                    json_data = json.load(f)
+                    if "correct" in json_data:
+                        primary_correct_flag = json_data["correct"]
+                    elif "primary" in json_data and "correct" in json_data["primary"]:
+                        primary_correct_flag = json_data["primary"]["correct"]
+                    elif "primary" in json_data and "validation_error" in json_data["primary"]:
+                         validation_error_str = json_data["primary"]["validation_error"]
+            except Exception as e:
+                print(f"Error loading metrics.json for primary_correct_flag or validation_error: {e}")
+
+    # Metric: execution_successful (Binary flag for successful program execution)
+    try:
+        metrics["execution_successful"] = 1.0 if not validation_error_str else 0.0
+    except Exception as e:
+        print(f"Error calculating execution_successful: {e}")
+        metrics["execution_successful"] = 0.0
+
+    # Metric: avg_dist_to_center
+    try:
+        if centers.shape[0] == N_EXPECTED:
+            square_center = np.array([0.5, 0.5])
+            distances = np.linalg.norm(centers - square_center, axis=1)
+            metrics["avg_dist_to_center"] = float(np.mean(distances))
+        else:
+            metrics["avg_dist_to_center"] = 0.0
+    except Exception as e:
+        print(f"Error calculating avg_dist_to_center: {e}")
+        metrics["avg_dist_to_center"] = 0.0
+
+    # If no valid data or incorrect number of circles, set dependent metrics to 0
+    # Also set num_circles_generated to actual count even if it's not N_EXPECTED
+    if centers.size == 0 or radii.size == 0:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics
+
+
+        
+    # Check if the number of circles matches N_EXPECTED before calculating other metrics
+    if centers.shape[0] != N_EXPECTED or radii.shape[0] != N_EXPECTED:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics # Exit early if counts are wrong, as other metrics might be nonsensical
+
+    # All subsequent metrics assume N_EXPECTED circles are present and no NaNs/Infs
+
+    # Metric 2: mean_radius
+    try:
+        metrics["mean_radius"] = float(np.mean(radii))
+    except Exception as e:
+        print(f"Error calculating mean_radius: {e}")
+        metrics["mean_radius"] = 0.0
+
+    # Metric 3: radius_std_dev
+    try:
+        metrics["radius_std_dev"] = float(np.std(radii)) if len(radii) > 1 else 0.0
+    except Exception as e:
+        print(f"Error calculating radius_std_dev: {e}")
+        metrics["radius_std_dev"] = 0.0
+
+    # Metric 4: total_packed_area
+    try:
+        metrics["total_packed_area"] = float(np.sum(np.pi * radii**2))
+    except Exception as e:
+        print(f"Error calculating total_packed_area: {e}")
+        metrics["total_packed_area"] = 0.0
+
+    # Metric 5: min_clearance_to_boundary
+    try:
+        min_clearance = float('inf')
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            clearances = [x - r, 1 - (x + r), y - r, 1 - (y + r)]
+            min_clearance = min(min_clearance, *clearances)
+        metrics["min_clearance_to_boundary"] = min_clearance if math.isfinite(min_clearance) else 0.0
+    except Exception as e:
+        print(f"Error calculating min_clearance_to_boundary: {e}")
+        metrics["min_clearance_to_boundary"] = 0.0
+
+    # Metric 6: max_overlap_value
+    try:
+        max_overlap = 0.0 # Changed from -inf, as overlap should be >= 0 for this metric
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                overlap = (radii[i] + radii[j]) - dist
+                if overlap > max_overlap:
+                    max_overlap = overlap
+        metrics["max_overlap_value"] = max_overlap
+    except Exception as e:
+        print(f"Error calculating max_overlap_value: {e}")
+        metrics["max_overlap_value"] = 0.0
+
+    # Metric: num_tangent_pairs
+    try:
+        tangent_count = 0
+        epsilon = 1e-4 # Tolerance for tangency
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                sum_radii = radii[i] + radii[j]
+                # Check for tangency: distance is approximately sum of radii
+                if abs(dist - sum_radii) < epsilon:
+                    tangent_count += 1
+        metrics["num_tangent_pairs"] = float(tangent_count)
+    except Exception as e:
+        print(f"Error calculating num_tangent_pairs: {e}")
+        metrics["num_tangent_pairs"] = 0.0
+
+    # Metric 8: min_radius
+    try:
+        metrics["min_radius"] = float(np.min(radii))
+    except Exception as e:
+        print(f"Error calculating min_radius: {e}")
+        metrics["min_radius"] = 0.0
+
+    # Metric 10: boundary_contact_count
+    try:
+        contact_count = 0
+        epsilon = 1e-6 # Tolerance for floating point comparisons
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            # Check if touching any of the four boundaries
+            if abs(x - r) < epsilon or abs(1 - (x + r)) < epsilon or \
+               abs(y - r) < epsilon or abs(1 - (y + r)) < epsilon:
+                contact_count += 1
+        metrics["boundary_contact_count"] = float(contact_count)
+    except Exception as e:
+        print(f"Error calculating boundary_contact_count: {e}")
+        metrics["boundary_contact_count"] = 0
+
+    return metrics

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_118/results/correct.json

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

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_118/results/metrics.json

@@ -0,0 +1,40 @@
+{
+  "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": "NameError: name 'np' is not defined"
+  },
+  "auxiliary": {
+    "execution_successful": 1.0,
+    "avg_dist_to_center": 0.0,
+    "mean_radius": 0.0,
+    "radius_std_dev": 0.0,
+    "total_packed_area": 0.0,
+    "min_clearance_to_boundary": 0.0,
+    "max_overlap_value": 0.0,
+    "min_radius": 0.0,
+    "boundary_contact_count": 0
+  },
+  "auxiliary_descriptions": {
+    "mean_radius": "Average radius of all circles.",
+    "radius_std_dev": "Standard deviation of radii.",
+    "total_packed_area": "Total area covered by all circles.",
+    "min_clearance_to_boundary": "Minimum distance of any circle to the unit square boundary.",
+    "max_overlap_value": "Maximum overlap between any two circles.",
+    "is_valid_packing": "Binary flag indicating if the packing is valid (no overlaps, in bounds, correct shapes).",
+    "min_radius": "The minimum radius among all circles in the packing.",
+    "avg_dist_to_center": "Average distance of circle centers to the center of the unit square, indicating compactness.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries, indicating effective utilization of space.",
+    "num_tangent_pairs": "Counts the number of pairs of circles that are tangent (touching without overlapping) within a small tolerance. Indicates efficient packing."
+  },
+  "timestamp": 1770932465.668164,
+  "generation": 118
+}

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_119/results/auxiliary_metrics_snapshot.py

@@ -0,0 +1,198 @@
+
+import numpy as np
+import os
+import json
+from typing import Dict, Any
+import math
+
+auxiliary_metric_descriptions = {
+    "execution_successful": "Binary flag: 1.0 if the program executed without fundamental runtime errors (e.g., NameError); 0.0 otherwise.",
+    "avg_dist_to_center": "Average Euclidean distance of circle centers from the center of the unit square (0.5, 0.5), indicating compactness. Only calculated for valid packings.",
+    "mean_radius": "Average radius of all circles. Only calculated for valid packings.",
+    "radius_std_dev": "Standard deviation of radii. Only calculated for valid packings.",
+    "total_packed_area": "Total area covered by all circles (sum of pi * r^2). Only calculated for valid packings.",
+    "min_clearance_to_boundary": "Minimum distance of any circle (edge) to the unit square boundary. A negative value indicates a circle is out of bounds. Only calculated for valid packings.",
+    "max_overlap_value": "Maximum overlap between any two circles. 0.0 if no overlap. Only calculated for valid packings.",
+    "num_tangent_pairs": "Counts the number of pairs of circles that are tangent (touching without overlapping) within a small tolerance. Indicates efficient packing.",
+    "min_radius": "The minimum radius among all circles in the packing. Only calculated for valid packings.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries (within a small epsilon). Indicates effective utilization of space. Only calculated for valid packings."
+}
+def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:
+    metrics = {}
+    
+    # Initialize centers and radii to empty arrays for robustness
+    centers = np.array([])
+    radii = np.array([])
+    N_EXPECTED = 26 # Define N_EXPECTED early for consistent use
+
+    # Try to load the extra.npz file for detailed data
+    try:
+        extra_data_path = os.path.join(results_dir, "extra.npz")
+        if os.path.exists(extra_data_path):
+            with np.load(extra_data_path) as data:
+                centers = data["centers"]
+                radii = data["radii"]
+        else:
+            print(f"Warning: {extra_data_path} not found. Some metrics might be 0.")
+    except Exception as e:
+        print(f"Error loading extra.npz: {e}")
+        # If error, centers and radii remain empty arrays, handled below
+
+    # Get primary validation status
+    primary_correct_flag = False
+    validation_error_str = None
+    if primary_result:
+        if "correct" in primary_result:
+            primary_correct_flag = primary_result["correct"]
+        if "primary" in primary_result and "validation_error" in primary_result["primary"]:
+            validation_error_str = primary_result["primary"]["validation_error"]
+    else: # Fallback to metrics.json if primary_result is incomplete
+        metrics_json_path = os.path.join(results_dir, "results/metrics.json") # Correct path for metrics.json
+        if os.path.exists(metrics_json_path):
+            try:
+                with open(metrics_json_path, 'r') as f:
+                    json_data = json.load(f)
+                    if "correct" in json_data:
+                        primary_correct_flag = json_data["correct"]
+                    elif "primary" in json_data and "correct" in json_data["primary"]:
+                        primary_correct_flag = json_data["primary"]["correct"]
+                    elif "primary" in json_data and "validation_error" in json_data["primary"]:
+                         validation_error_str = json_data["primary"]["validation_error"]
+            except Exception as e:
+                print(f"Error loading metrics.json for primary_correct_flag or validation_error: {e}")
+
+    # Metric: execution_successful (Binary flag for successful program execution)
+    try:
+        metrics["execution_successful"] = 1.0 if not validation_error_str else 0.0
+    except Exception as e:
+        print(f"Error calculating execution_successful: {e}")
+        metrics["execution_successful"] = 0.0
+
+    # Metric: avg_dist_to_center
+    try:
+        if centers.shape[0] == N_EXPECTED:
+            square_center = np.array([0.5, 0.5])
+            distances = np.linalg.norm(centers - square_center, axis=1)
+            metrics["avg_dist_to_center"] = float(np.mean(distances))
+        else:
+            metrics["avg_dist_to_center"] = 0.0
+    except Exception as e:
+        print(f"Error calculating avg_dist_to_center: {e}")
+        metrics["avg_dist_to_center"] = 0.0
+
+    # If no valid data or incorrect number of circles, set dependent metrics to 0
+    # Also set num_circles_generated to actual count even if it's not N_EXPECTED
+    if centers.size == 0 or radii.size == 0:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics
+
+
+        
+    # Check if the number of circles matches N_EXPECTED before calculating other metrics
+    if centers.shape[0] != N_EXPECTED or radii.shape[0] != N_EXPECTED:
+        metrics["mean_radius"] = 0.0
+        metrics["radius_std_dev"] = 0.0
+        metrics["total_packed_area"] = 0.0
+        metrics["min_clearance_to_boundary"] = 0.0
+        metrics["max_overlap_value"] = 0.0
+        metrics["min_radius"] = 0.0
+        metrics["boundary_contact_count"] = 0
+        return metrics # Exit early if counts are wrong, as other metrics might be nonsensical
+
+    # All subsequent metrics assume N_EXPECTED circles are present and no NaNs/Infs
+
+    # Metric 2: mean_radius
+    try:
+        metrics["mean_radius"] = float(np.mean(radii))
+    except Exception as e:
+        print(f"Error calculating mean_radius: {e}")
+        metrics["mean_radius"] = 0.0
+
+    # Metric 3: radius_std_dev
+    try:
+        metrics["radius_std_dev"] = float(np.std(radii)) if len(radii) > 1 else 0.0
+    except Exception as e:
+        print(f"Error calculating radius_std_dev: {e}")
+        metrics["radius_std_dev"] = 0.0
+
+    # Metric 4: total_packed_area
+    try:
+        metrics["total_packed_area"] = float(np.sum(np.pi * radii**2))
+    except Exception as e:
+        print(f"Error calculating total_packed_area: {e}")
+        metrics["total_packed_area"] = 0.0
+
+    # Metric 5: min_clearance_to_boundary
+    try:
+        min_clearance = float('inf')
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            clearances = [x - r, 1 - (x + r), y - r, 1 - (y + r)]
+            min_clearance = min(min_clearance, *clearances)
+        metrics["min_clearance_to_boundary"] = min_clearance if math.isfinite(min_clearance) else 0.0
+    except Exception as e:
+        print(f"Error calculating min_clearance_to_boundary: {e}")
+        metrics["min_clearance_to_boundary"] = 0.0
+
+    # Metric 6: max_overlap_value
+    try:
+        max_overlap = 0.0 # Changed from -inf, as overlap should be >= 0 for this metric
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                overlap = (radii[i] + radii[j]) - dist
+                if overlap > max_overlap:
+                    max_overlap = overlap
+        metrics["max_overlap_value"] = max_overlap
+    except Exception as e:
+        print(f"Error calculating max_overlap_value: {e}")
+        metrics["max_overlap_value"] = 0.0
+
+    # Metric: num_tangent_pairs
+    try:
+        tangent_count = 0
+        epsilon = 1e-4 # Tolerance for tangency
+        for i in range(N_EXPECTED):
+            for j in range(i + 1, N_EXPECTED):
+                dist = np.linalg.norm(centers[i] - centers[j])
+                sum_radii = radii[i] + radii[j]
+                # Check for tangency: distance is approximately sum of radii
+                if abs(dist - sum_radii) < epsilon:
+                    tangent_count += 1
+        metrics["num_tangent_pairs"] = float(tangent_count)
+    except Exception as e:
+        print(f"Error calculating num_tangent_pairs: {e}")
+        metrics["num_tangent_pairs"] = 0.0
+
+    # Metric 8: min_radius
+    try:
+        metrics["min_radius"] = float(np.min(radii))
+    except Exception as e:
+        print(f"Error calculating min_radius: {e}")
+        metrics["min_radius"] = 0.0
+
+    # Metric 10: boundary_contact_count
+    try:
+        contact_count = 0
+        epsilon = 1e-6 # Tolerance for floating point comparisons
+        for i in range(N_EXPECTED):
+            x, y = centers[i]
+            r = radii[i]
+            # Check if touching any of the four boundaries
+            if abs(x - r) < epsilon or abs(1 - (x + r)) < epsilon or \
+               abs(y - r) < epsilon or abs(1 - (y + r)) < epsilon:
+                contact_count += 1
+        metrics["boundary_contact_count"] = float(contact_count)
+    except Exception as e:
+        print(f"Error calculating boundary_contact_count: {e}")
+        metrics["boundary_contact_count"] = 0
+
+    return metrics

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_119/results/correct.json

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

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_119/results/metrics.json

@@ -0,0 +1,40 @@
+{
+  "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": "NameError: name 'np' is not defined"
+  },
+  "auxiliary": {
+    "execution_successful": 1.0,
+    "avg_dist_to_center": 0.0,
+    "mean_radius": 0.0,
+    "radius_std_dev": 0.0,
+    "total_packed_area": 0.0,
+    "min_clearance_to_boundary": 0.0,
+    "max_overlap_value": 0.0,
+    "min_radius": 0.0,
+    "boundary_contact_count": 0
+  },
+  "auxiliary_descriptions": {
+    "mean_radius": "Average radius of all circles.",
+    "radius_std_dev": "Standard deviation of radii.",
+    "total_packed_area": "Total area covered by all circles.",
+    "min_clearance_to_boundary": "Minimum distance of any circle to the unit square boundary.",
+    "max_overlap_value": "Maximum overlap between any two circles.",
+    "is_valid_packing": "Binary flag indicating if the packing is valid (no overlaps, in bounds, correct shapes).",
+    "min_radius": "The minimum radius among all circles in the packing.",
+    "avg_dist_to_center": "Average distance of circle centers to the center of the unit square, indicating compactness.",
+    "boundary_contact_count": "Number of circles touching any of the unit square boundaries, indicating effective utilization of space.",
+    "num_tangent_pairs": "Counts the number of pairs of circles that are tangent (touching without overlapping) within a small tolerance. Indicates efficient packing."
+  },
+  "timestamp": 1770932519.1297677,
+  "generation": 119
+}

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_12/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/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_12/results/correct.json

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

tasks/circle_packing/results/results_circle_packing_mmv1_3_gen200_periodic10_20260212_185849/gen_12/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": "NameError: name 'np' is not defined"
+  },
+  "auxiliary": {},
+  "auxiliary_descriptions": {},
+  "timestamp": 1770924589.2499666,
+  "generation": 12
+}