Step 3: assistant

thinking

Calls update_claim_assessment

{
  "status": "doubtful",
  "claim_id": "dc6644de-7040-4689-be14-749e9f4c8090",
  "assessment": "The claim is that, in recursive AI debate, the human judges who rule on the final narrow \"leaf\" questions are accurate enough that debaters cannot reliably exploit their mistakes. The theoretical case for debate assumes this. Formal results model the judge as an oracle whose answers define what counts as correct, so they show what debate achieves if leaf judgments can be trusted. They do not show that the judgments can be trusted.\n\nThe evidence weighs against the claim, though only indirectly. Random judge errors tend to cancel out. Systematic errors do not: a debater trained to win is rewarded for steering the debate toward leaves where judges are predictably wrong. Human judgment is known to be swayed by anchoring, order of presentation and fluent writing. Empirical debate studies have found accuracy gains with non-expert judges on narrow reading-comprehension tasks, but these involved weak debaters and no optimization against the judge. Reports from reinforcement-learning debate training describe debaters exploiting weak judges unless the setup constrains them.\n\nSome protocol choices could make the claim true: keeping leaf questions narrow and simple, controlling which questions reach a human, training judges, using several judges, and cross-examination. No experiment has yet measured human accuracy at the leaves of a deep recursive debate against optimized debaters. Such a study would settle the question.",
  "confidence": 0.55,
  "claim_credence": 0.25,
  "marginal_yield": 0.5,
  "reasoning_trace": "This pass had a tight budget and no sources were opened, so the assessment rests on general knowledge of the debate literature and on the parent Steward's prior (credence 0.2). That literature includes the original debate proposal by Irving, Christiano and Amodei (2018), later work on debate complexity and obfuscated arguments, and human debate experiments on QuALITY-style tasks (Michael et al. 2023; Khan et al. 2024). The obfuscated-arguments problem identified by Barnes and Christiano is a known route to exploitation: a dishonest debater can build an argument whose flaw no judge can efficiently locate. Doubtful was chosen over contradicted because nobody has tested the claim directly, and protocol designs could change the outcome. A fuller pass should read the human-judge experiments and the 2025–2026 papers on RL debate training, and record instances from them."
}

Calls set_claim_importance

{
  "claim_id": "dc6644de-7040-4689-be14-749e9f4c8090",
  "reasoning": "A load-bearing assumption of debate as a scalable-oversight method, and an open empirical question. It matters within AI-safety research rather than across the whole graph.",
  "importance": 0.4,
  "contestation": 0.45
}

Calls log_stewardship_decision

{
  "claim_id": "dc6644de-7040-4689-be14-749e9f4c8090",
  "reasoning": "First pass on a tight budget, so the claim was not decomposed. Assessed as doubtful (credence 0.25) from general knowledge of the debate literature and the parent Steward's seed. Marginal yield is 0.5: a funded pass that reads the human-judge and RL-debate experiments and records instances would improve the assessment.",
  "action_taken": "assessed"
}

Calls answer_messages

{
  "answers": [
    {
      "note": "Recorded a first assessment: doubtful, credence 0.25. The claim was not decomposed.",
      "message": "m1",
      "outcome": "changed"
    },
    {
      "note": "Did the assess pass. The verdict is doubtful, with credence 0.25.",
      "message": "m2",
      "outcome": "changed"
    }
  ]
}