{
  "generated_at": "2026-04-25T18:12:09.069260+00:00",
  "base_url": "http://localhost:7860",
  "rows": [
    {
      "task_id": "clean_claim",
      "seed": 7,
      "done": true,
      "reward": 0.8725,
      "variant_id": 2,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 4
    },
    {
      "task_id": "clean_claim",
      "seed": 11,
      "done": true,
      "reward": 0.8725,
      "variant_id": 1,
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      "exploit_penalty": 0.0,
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    },
    {
      "task_id": "clean_claim",
      "seed": 13,
      "done": true,
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      "variant_id": 3,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
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    },
    {
      "task_id": "clean_claim",
      "seed": 19,
      "done": true,
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      "variant_id": 4,
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    },
    {
      "task_id": "clean_claim",
      "seed": 25,
      "done": true,
      "reward": 0.8725,
      "variant_id": 0,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 4
    },
    {
      "task_id": "contradictory_claim",
      "seed": 7,
      "done": true,
      "reward": 0.7497,
      "variant_id": 2,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 8
    },
    {
      "task_id": "contradictory_claim",
      "seed": 11,
      "done": true,
      "reward": 0.7497,
      "variant_id": 1,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 8
    },
    {
      "task_id": "contradictory_claim",
      "seed": 13,
      "done": true,
      "reward": 0.7497,
      "variant_id": 3,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 8
    },
    {
      "task_id": "contradictory_claim",
      "seed": 19,
      "done": true,
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      "variant_id": 4,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 8
    },
    {
      "task_id": "contradictory_claim",
      "seed": 25,
      "done": true,
      "reward": 0.7497,
      "variant_id": 0,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 8
    },
    {
      "task_id": "distribution_shift_claim",
      "seed": 7,
      "done": true,
      "reward": 0.7827,
      "variant_id": 2,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 12
    },
    {
      "task_id": "distribution_shift_claim",
      "seed": 11,
      "done": true,
      "reward": 0.7827,
      "variant_id": 1,
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    },
    {
      "task_id": "distribution_shift_claim",
      "seed": 13,
      "done": true,
      "reward": 0.7827,
      "variant_id": 3,
      "evidence_quality": 1.0,
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      "steps": 12
    },
    {
      "task_id": "distribution_shift_claim",
      "seed": 19,
      "done": true,
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      "variant_id": 4,
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      "exploit_penalty": 0.0,
      "steps": 12
    },
    {
      "task_id": "distribution_shift_claim",
      "seed": 25,
      "done": true,
      "reward": 0.7827,
      "variant_id": 0,
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      "exploit_penalty": 0.0,
      "steps": 12
    },
    {
      "task_id": "coordinated_fraud",
      "seed": 7,
      "done": true,
      "reward": 0.823,
      "variant_id": 2,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 12
    },
    {
      "task_id": "coordinated_fraud",
      "seed": 11,
      "done": true,
      "reward": 0.823,
      "variant_id": 1,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 12
    },
    {
      "task_id": "coordinated_fraud",
      "seed": 13,
      "done": true,
      "reward": 0.823,
      "variant_id": 3,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 12
    },
    {
      "task_id": "coordinated_fraud",
      "seed": 19,
      "done": true,
      "reward": 0.823,
      "variant_id": 4,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 12
    },
    {
      "task_id": "coordinated_fraud",
      "seed": 25,
      "done": true,
      "reward": 0.823,
      "variant_id": 0,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 12
    },
    {
      "task_id": "identity_fraud",
      "seed": 7,
      "done": true,
      "reward": 0.818,
      "variant_id": 2,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 10
    },
    {
      "task_id": "identity_fraud",
      "seed": 11,
      "done": true,
      "reward": 0.818,
      "variant_id": 1,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 10
    },
    {
      "task_id": "identity_fraud",
      "seed": 13,
      "done": true,
      "reward": 0.818,
      "variant_id": 3,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 10
    },
    {
      "task_id": "identity_fraud",
      "seed": 19,
      "done": true,
      "reward": 0.818,
      "variant_id": 4,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 10
    },
    {
      "task_id": "identity_fraud",
      "seed": 25,
      "done": true,
      "reward": 0.818,
      "variant_id": 0,
      "evidence_quality": 1.0,
      "exploit_penalty": 0.0,
      "steps": 10
    }
  ],
  "average_reward": 0.8092,
  "completion_rate": 1.0,
  "cf4_notes": {
    "variant_awareness": "All strategies read observation.documents to extract variant-specific values (declared_cost_inr, incident_date, admission_date, claimed_cost_inr, standard_rate_inr, distance_km, template_similarity, days_since_purchase, days_to_claim). Evidence text cites per-variant numbers.",
    "reward_variance_explanation": "Rewards differ across tasks (5 unique values: 0.8725, 0.7497, 0.7827, 0.823, 0.818) but remain identical within each task across seeds. This is an env design property: variants change document values (costs, dates, distances) but the reward function scores signal flag_ids not values. The only variant-sensitive reward component is payout_accuracy (clean_claim only), which the agent now satisfies by reading estimate_inr from docs.",
    "previous_clean_claim_reward": 0.7625,
    "current_clean_claim_reward": 0.8725,
    "improvement_source": "Reading variant payout_band values instead of hardcoded amount_inr=150000"
  },
  "known_limitations": {
    "model_capacity": "0.5B parameter model (Qwen2.5-0.5B-Instruct) — limited reasoning and instruction-following capacity compared to larger models",
    "scripted_baseline": "Eval uses scripted strategies (not LLM inference) to isolate env reward mechanics from model quality"
  }
}