Step 5: assistant

Calls record_claim_instance

{
  "url": "https://arxiv.org/abs/2402.06782",
  "title": "Debating with More Persuasive LLMs Leads to More Truthful Answers",
  "stance": "affirms",
  "context": "QuALITY hidden-text debates; debater strength varied by base model and best-of-N; judges lack the story.",
  "speaker": "Khan et al.",
  "claim_id": "f02489a9-374a-449f-a645-a9d454a68d58",
  "confidence": 0.8,
  "source_date": "2024",
  "verbatim_text": "As debaters are optimised against the unsupervised metric of win rate (judge preference), judge accuracy improves"
}

Calls record_claim_instance

{
  "url": "https://arxiv.org/abs/2504.18530",
  "title": "Scaling Laws For Scalable Oversight",
  "stance": "affirms",
  "context": "Debate game in which the debaters' win rate is one minus judge accuracy; flat debater rating implies flat accuracy for a given judge as debaters strengthen.",
  "speaker": "Engels, Baek, Kantamneni, Tegmark",
  "claim_id": "f02489a9-374a-449f-a645-a9d454a68d58",
  "confidence": 0.75,
  "source_date": "2025",
  "verbatim_text": "Notably, Houdini performance stays constant (an expected result of the adversarial nature of debate), while the Guard's performance scales with general intelligence rating"
}

Calls record_claim_instance

{
  "url": "https://arxiv.org/abs/2407.04622",
  "title": "On scalable oversight with weak LLMs judging strong LLMs",
  "stance": "affirms",
  "context": "Section 4.3, cross-play of five debaters judged by Gemini Pro 1.5; clearer on extractive tasks.",
  "speaker": "Kenton et al.",
  "claim_id": "f02489a9-374a-449f-a645-a9d454a68d58",
  "confidence": 0.7,
  "source_date": "2024",
  "verbatim_text": "stronger debate models (as measured by Elo) tend to lead to marginally higher judge accuracy, though the effect is relatively small"
}

Calls record_claim_instance

{
  "url": "https://arxiv.org/abs/2608.17776",
  "title": "Debate Training Reduces Reward Hacking in RLAIF",
  "stance": "denies",
  "context": "RL training of debaters against a frozen weaker judge; without critique word limits the critic exploited the judge's verbosity bias and won almost every debate. Counts against the claim on the reading where capability gained by training to win counts.",
  "speaker": "Kenton et al.",
  "claim_id": "f02489a9-374a-449f-a645-a9d454a68d58",
  "confidence": 0.55,
  "source_date": "2026",
  "verbatim_text": "without player constraints, adversarial training risks defaulting to critic judge-hacking"
}

Calls log_stewardship_decision

{
  "claim_id": "f02489a9-374a-449f-a645-a9d454a68d58",
  "reasoning": "First pass. Four arguments: two for (measured accuracy as debaters strengthen; both sides strengthening together), two against (optimisation against the judge; obfuscation at wide gaps). Each linked to an existing assessed claim. Related links to the debate-supervision and intermediate-asymmetry claims. Commissioned a researcher reading of primary texts (1.37 owls). Assessed supported, confidence 0.55, credence 0.55: inference-time measurements are all flat or positive but narrow-range; RL training studies show declines. Importance 0.45, contestation 0.5. Recorded four instances. The parent's contradicts edge remains apt; the assessment is close to the parent Steward's seed of 0.58, so no notification needed.",
  "action_taken": "structured_and_assessed"
}

Calls answer_messages

{
  "answers": [
    {
      "note": "Resumed with the research report; assessment finalised as supported (0.55), arguments evaluated.",
      "message": "m1",
      "outcome": "changed"
    }
  ]
}