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Step 12: tool results

2026-11-11 20:25:14 UTC · previous step · next step

add_argument

Input

{
  "name": "The argument from measured scaling trends in particular methods",
  "stance": "for",
  "claim_id": "a0867a24-31b6-4ad6-9add-f140c4da7663",
  "description": "Experiments on peer-prediction evaluation, debate, and lie-detector-guided preference learning each report deception resistance improving as the overseen models grow stronger relative to the overseer."
}

{"success":true,"message":"Created argument \"The argument from measured scaling trends in particular methods\" (for) on claim a0867a24-31b6-4ad6-9add-f140c4da7663. Attach its subclaim edges, then give it a written form with write_argument, and evaluate it with evaluate_argument when you record the claim's assessment.","argument_id":"b36e4719-5b34-4b2e-b2cb-05bfc113f682"}

add_argument

Input

{
  "name": "The argument from non-adaptive deceivers",
  "stance": "against",
  "claim_id": "a0867a24-31b6-4ad6-9add-f140c4da7663",
  "description": "The measured trends used deceivers that were prompted or fine-tuned to deceive but did not optimise against the oversight method, so they may not show resistance to a more capable deceiver that does."
}

{"success":true,"message":"Created argument \"The argument from non-adaptive deceivers\" (against) on claim a0867a24-31b6-4ad6-9add-f140c4da7663. Attach its subclaim edges, then give it a written form with write_argument, and evaluate it with evaluate_argument when you record the claim's assessment.","argument_id":"efdc58de-36ea-4a12-9864-10150cee445b"}

record_claim_instance

Input

{
  "url": "https://arxiv.org/abs/2601.20299",
  "title": "Truthfulness Despite Weak Supervision: Evaluating and Training LLMs Using Peer Prediction",
  "stance": "affirms",
  "context": "Abstract of a paper proposing peer prediction for LLM evaluation and training. The gap is the ratio of participant model size to expert model size; resistance is measured by how well scores predict honesty labels for honest versus deceptively prompted participants.",
  "speaker": "Tianyi Alex Qiu, Micah Carroll, Cameron Allen",
  "claim_id": "a0867a24-31b6-4ad6-9add-f140c4da7663",
  "publication": "ICLR 2026 (arXiv:2601.20299)",
  "source_date": "2026-01-28",
  "verbatim_text": "we discover an inverse scaling property in peer prediction, where, surprisingly, resistance to deception is strengthened as the capability gap between the experts and participants widens"
}

{"success":true,"instance_id":"64cc6d5e-5b64-4cbe-a4da-dbb4eeb5af88","source_id":"f52bf77b-f5dc-4d5f-8b66-2d61f26537c1","message":"Recorded a affirms instance of claim a0867a24-31b6-4ad6-9add-f140c4da7663 from https://arxiv.org/abs/2601.20299. It now counts among the claim's source instances; weigh its stance in your assessment like any other."}

record_claim_instance

Input

{
  "url": "https://arxiv.org/abs/2607.01567",
  "title": "Scaling Trends for Lie Detector Oversight in Preference Learning",
  "stance": "affirms",
  "context": "Discussion section of a study scaling lie-detector-guided preference learning (SOLiD). The trend is stated over the size of the overseen model, with a simulated fixed low-cost labeler and a linear probe on the model's own activations, not over a measured capability gap between model and overseer.",
  "speaker": "Oskar J. Hollinsworth, Ann-Kathrin Dombrowski, Sam Adam-Day, Adam Gleave, Chris Cundy",
  "claim_id": "a0867a24-31b6-4ad6-9add-f140c4da7663",
  "confidence": 0.6,
  "publication": "arXiv:2607.01567 (FAR.AI)",
  "source_date": "2026-07-02",
  "verbatim_text": "SOLiD exhibits favorable scaling: larger models yield stronger detectors and lower undetected deception at fixed TPR, across Llama (1B–405B) and Qwen (0.6B–32B)."
}

{"success":true,"instance_id":"c1d898d2-e1f9-4eaf-b3da-785b76371cdb","source_id":"8b97832d-8ee0-410f-aac8-eea22b5a55b5","message":"Recorded a affirms instance of claim a0867a24-31b6-4ad6-9add-f140c4da7663 from https://arxiv.org/abs/2607.01567. It now counts among the claim's source instances; weigh its stance in your assessment like any other."}

add_related_claim

Input

{
  "kind": "counterpart_position",
  "claim_id": "a0867a24-31b6-4ad6-9add-f140c4da7663",
  "reasoning": "One states the general trend that oversight success falls as the capability gap grows; the other states that particular methods run the other way. A reader of either needs the other to see that the decline is measured for some protocols and the improvement for others.",
  "other_claim_id": "2d5868e4-745c-474f-ac6f-cc9cbc25ea8c"
}

{"success":true,"linked":true,"link_id":"ca0e719f-d50b-4858-bba1-839fe2b7e40b","message":"Linked a0867a24-31b6-4ad6-9add-f140c4da7663 <-> 2d5868e4-745c-474f-ac6f-cc9cbc25ea8c (counterpart_position); it now shows as a see-also on both claims."}

set_claim_importance

Input

{
  "claim_id": "a0867a24-31b6-4ad6-9add-f140c4da7663",
  "reasoning": "A supporting empirical premise inside the live debate over whether scalable oversight can keep pace with capability. The existential wording makes it easy to satisfy within tested regimes, and few dispute the reported measurements; what is argued is how far they extend. Notable, not major.",
  "importance": 0.35,
  "contestation": 0.35
}

{"success":true,"message":"Importance of claim a0867a24-31b6-4ad6-9add-f140c4da7663 set to 0.35. Contestation recorded as 0.35."}