reasoning-degeneration-dev/t1-musr-prompt-enhancement-together_ai-meta-llama-meta-llama-3-1-8b-s4-enhanced-strategy

{
  "script_name": "musr_prompt_enhancement/run_experiment.py",
  "model": "gpt-5-mini",
  "description": "Phase 4: enhanced strategy derived from 8 elements",
  "custom_metadata": {
    "stage": "s4_enhanced_strategy",
    "strategy": {
      "strategy_name": "Means-Motive-Opportunity Matrix",
      "strategy_description": "Follow a structured, stepwise workflow that treats each suspect as an entry in a comparable matrix. First, list every suspect and for each explicitly record Means, Motive, and Opportunity, labeling each as Strong / Weak / Missing. For Means, note tools, skills, or unique capabilities; for Motive, separate emotional motives (anger, jealousy) from material motives (financial gain, career survival) and rate verifiability; for Opportunity, verify physical feasibility by cross-checking timestamps, CCTV, receipts, phone/GPS logs and witness times. Distinguish possession of the exact weapon (unique markings, purchase/possession records, serial numbers) from mere access to that weapon type or to the location; treat forensic or documentary links as higher weight than vague associations.\n\nNext, corroborate or refute statements and alibis with independent third-party records and prioritize physical timestamps (camera, transaction, system logs) over recollection. Flag evasive or defensive behavior as a prompt for follow-up investigation rather than proof. Compare suspects pairwise across each evidential axis (Means, Motive, Opportunity, forensic/documentary, alibi consistency, cover-up indicators) and use negative evidence (airtight alibis, lack of access) to exclude candidates. Actively seek disconfirming evidence to avoid anchoring; prefer the suspect supported by the most corroborated, independent evidence across different categories, report conclusions as degrees of confidence, and explicitly list which elements are speculative. When evidence is internally inconsistent or multiple suspects have similar but weak motives, deliberate the possibility of accomplices, staged scenes, or third-party manipulation and escalate to targeted checks for deliberate cover-up (altered records, cleaned areas, missing items). Apply Occam\u2019s razor only after ensuring the simplest explanation still fits independent corroborated facts.",
      "key_elements": [
        "Create a table listing each suspect and mark Means, Motive, and Opportunity as Strong / Weak / Missing for each entry.",
        "Cross-check every claimed time or presence against at least one independent timestamp (CCTV, receipt, transaction/GPS log) and record temporal contradictions.",
        "Classify each piece of evidence as direct/documentary/forensic versus circumstantial and weight direct links higher in the matrix.",
        "For weapons, explicitly note whether a suspect had access to the weapon type versus documented possession of the exact weapon (purchase record, serial/unique marks, possession).",
        "Corroborate alibis with independent records; flag evasions or contradictions but do not treat them as decisive until externally confirmed.",
        "When a suspect is evasive or defensive, log that behavior as 'investigate further' and list concrete follow-up checks (search for altered records, missing items, surveillance gaps).",
        "Perform pairwise comparisons between suspects on each evidential axis (MMO, forensic, alibi, cover-up) to identify the relatively stronger candidate rather than evaluating in isolation.",
        "Produce a conclusion with graded confidence, explicitly stating which links are well-supported, which are speculative, and whether accomplice/staged-scene hypotheses remain plausible."
      ]
    }
  },
  "hyperparameters": {},
  "input_datasets": []
}

from datasets import load_dataset

dataset = load_dataset("reasoning-degeneration-dev/t1-musr-prompt-enhancement-together_ai-meta-llama-meta-llama-3-1-8b-s4-enhanced-strategy", split="train")
print(f"Loaded {len(dataset)} rows")