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tasks/task_hard.py -- Hard task: 4 variables, high noise, random domain.
The agent must discover a complex causal graph with high noise and a
tight budget. This is designed to challenge frontier models.
Grader returns 0.0-1.0 based on:
- Hypothesis accuracy (40%)
- Precision bonus (15%)
- Efficiency bonus (15%)
- Calibration (15%)
- Avoiding contradiction penalty (15%)
"""
from __future__ import annotations
from typing import Any
TASK_HARD = {
"id": "hard",
"name": "Hard -- Complex Graph Under Noise",
"description": (
"Discover causal relationships among four variables under "
"high noise (sigma=0.50) with a tight budget (8 steps). "
"Requires strategic experiment design and careful reasoning."
),
"difficulty": "hard",
"reset_kwargs": {
"noise_level": "high",
"seed": 999,
},
}
def grade_hard(episode_result: dict[str, Any]) -> float:
"""
Grade a hard-task episode. Returns a score in [0.0, 1.0].
"""
accuracy = episode_result.get("accuracy_score", 0.0)
precision = episode_result.get("precision_bonus", 0.0)
efficiency = episode_result.get("efficiency_bonus", 0.0)
calibration = episode_result.get("calibration_score", 0.0)
contradiction = episode_result.get("contradiction_penalty", 0.0)
no_contradiction = 1.0 if contradiction >= 0.0 else 0.0
raw = (
0.40 * min(accuracy, 1.0)
+ 0.15 * min(precision / 0.10, 1.0)
+ 0.15 * min(efficiency / 0.15, 1.0)
+ 0.15 * min(calibration / 0.20, 1.0)
+ 0.15 * no_contradiction
)
return round(max(0.0, min(1.0, raw)), 4)
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