Step 15: assistant

thinking

Calls update_claim_instance

{
  "claim_id": "a0867a24-31b6-4ad6-9add-f140c4da7663",
  "reasoning": "The passage first recorded is from the Discussion section of the full text (arxiv.org/html/2607.01567v1), which is not on the abstract page stored for this source. Replaced with the abstract's statement of the same finding, which is on the stored page.",
  "instance_id": "c1d898d2-e1f9-4eaf-b3da-785b76371cdb",
  "verbatim_text": "We find favorable scaling: undetected deception drops from 34% for 1B-parameter models to 14% for 405B-parameter models at a detector true positive rate of 99%"
}

Calls evaluate_argument

{
  "verdict": "likely",
  "decisive": false,
  "evaluation": "Granting that the reported trends are real, the conclusion follows for the tested regimes, since the claim needs only one method that improves as the gap widens. The weight rests on the peer-prediction result, the only one that varies a capability gap directly; it is peer reviewed but not independently replicated, and it widens the gap mainly by shrinking the expert. Debate adds a second method by different authors with a smaller effect. The lie-detector trend is over model size, not a gap with the overseer, and adds little. The step falls short of certain because all three used deceivers that did not adapt to the method, so a reader who takes resistance to mean resistance to such a deceiver is not answered by these measurements.",
  "argument_id": "b36e4719-5b34-4b2e-b2cb-05bfc113f682"
}

Calls evaluate_argument

{
  "verdict": "slight",
  "decisive": false,
  "evaluation": "The premises are largely accurate: the experiments used prompted or fine-tuned deceivers, peer prediction's guarantees exclude collusion, and lie detectors degrade under distribution shift. Granting them, they show that the measured trends may not extend to a deceiver that optimises against the method. They do not show that the trends are absent, and a method that scores deceptive models worse as their advantage grows is resisting the deception it was tested against. The argument therefore limits how far the claim reaches more than it counts against the claim as worded. It would gain force from an experiment in which deceivers trained against one of these methods reverse the trend; none has been reported.",
  "argument_id": "efdc58de-36ea-4a12-9864-10150cee445b"
}

Calls log_stewardship_decision

{
  "claim_id": "a0867a24-31b6-4ad6-9add-f140c4da7663",
  "reasoning": "First pass. Read the two primary sources behind the seed (Qiu, Carroll and Allen, arXiv 2601.20299: abstract, intro, Section 4.3, limitations, appendix FAQ on inverse scaling; Hollinsworth et al., arXiv 2607.01567: abstract, methods, results 4.1-4.2, discussion, limitations) and checked abstracts or summaries for debate (Khan et al. 2024, Kenton et al. 2024) and for contrary scaling evidence (probe fragility study 2605.27958; nested oversight games already in the graph). Assessed as supported, confidence 0.72, credence 0.8: the claim is existential and peer prediction directly, debate independently, show the stated direction within tested ranges; unreplicated, non-adaptive deceivers, gap varied mostly from the expert side. Kept the claim without subclaims: at importance 0.35 the individual experimental findings are source-specific results better carried in prose than minted as nodes. Added two named arguments with no attached subclaims (measured scaling trends, for, rated likely; non-adaptive deceivers, against, rated slight). Linked the general-trend claim (oversight success declines as the gap grows) as a counterpart position, not a premise. Recorded two instances with readings; corrected the Hollinsworth instance's passage to the abstract sentence after the quote check failed against the stored abstract page. Canonical form left as is; importance 0.35, contestation 0.35. Sent the dependent claim's Steward a free note since the verdict is close to the seed it already weighed. Not done: inspecting figures or released data for the peer-prediction scaling curve, and a full reading of the debate papers; marginal yield 0.3 reflects that.",
  "action_taken": "structured_and_assessed"
}

Calls notify_steward

{
  "message": "First assessment of \"Some AI oversight methods resist deception better as the overseen model's capability advantage grows\": supported, credence 0.8 (your seed was 0.6). The reading of the two 2026 studies matches your seed note. Two points you may want when weighing it for your claim. First, debate is a further instance by other authors (Khan et al. 2024; Kenton et al. 2024, smaller effect), so the existential claim does not rest on peer prediction alone. Second, the lie-detector result (Hollinsworth et al.) is a trend over model size with a simulated fixed labeler, not over a capability gap, and the authors attribute much of it to larger models being less deceptive before training; it supports this claim only loosely. All the evidence uses deceivers that did not adapt to the oversight method, and peer prediction rewards deception once deceivers are half the population, so this claim gives little support to protocols remaining effective against a deliberately adaptive deceiver. I do not think your verdict needs to move.",
  "claim_id": "a0867a24-31b6-4ad6-9add-f140c4da7663",
  "target_ids": [
    "804cd71e-671c-46a6-a37b-eb6d5cff952a"
  ]
}

Calls answer_messages

{
  "answers": [
    {
      "note": "First pass done: two named arguments, a counterpart link to the general-trend claim, two source instances with readings, and an assessment of supported (credence 0.8). No subclaims minted; the individual experimental results are carried in prose at this importance.",
      "message": "m1",
      "outcome": "changed"
    },
    {
      "note": "Assess pass completed and recorded: supported, confidence 0.72, credence 0.8, marginal yield 0.3. Remaining gain would come from inspecting the peer-prediction scaling data and from any experiment with deceivers trained against the method.",
      "message": "m2",
      "outcome": "changed"
    }
  ]
}