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Step 20: tool results

2026-11-14 20:42:21 UTC · previous step · next step

record_argument_instance

Input

{
  "url": "https://alignment.anthropic.com/2025/automated-auditing/",
  "title": "Building and evaluating alignment auditing agents",
  "stance": "advances",
  "reading": "full",
  "speaker": "Trenton Bricken, Rowan Wang, Sam Bowman et al. (Anthropic)",
  "argument_id": "851336c7-7768-4848-9448-3179bfa582a6",
  "source_date": "2025-07-24",
  "source_kind": "blog_post",
  "verbatim_text": "The agent's success critically depends on having access to interpretability tools or semantic search capabilities."
}

{"source_kind":"report","source_kind_note":"This source is already recorded as \"report\", so \"blog_post\" was not taken. If \"report\" is wrong, open the document (provenance_read_source) and state its kind on a reading of it (provenance_record_reading with source_read true), which corrects it.","success":true,"instance_id":"a6059642-9724-4416-93f8-da90a7b94126","source_id":"520313d1-54c5-40b1-8583-fb8f914e4a97","message":"Recorded a advances instance of argument \"The argument from the Marks et al. auditing game, where interpretability tools were key\" from https://alignment.anthropic.com/2025/automated-auditing/."}

record_claim_instance

Input

{
  "url": "https://arxiv.org/abs/2510.01070",
  "title": "Eliciting Secret Knowledge from Language Models",
  "stance": "affirms",
  "context": "Secret-keeping model organisms audited by an LLM auditor; the auditor is not a full open-ended agent, so this bears on the claim indirectly.",
  "speaker": "Bartosz Cywiński et al.",
  "claim_id": "86063716-4ef1-48dc-abe6-02ef8b75f176",
  "confidence": 0.7,
  "source_date": "2025-10",
  "source_kind": "preprint",
  "verbatim_text": "Our most effective techniques (performing best in all settings) are based on prefill attacks, a black-box technique where the LLM reveals secret knowledge when generating a completion from a predefined prefix. Our white-box techniques based on logit lens and sparse autoencoders (SAEs) also consistently increase the success rate of the LLM auditor, but are less effective."
}

{"source_kind":"preprint","success":true,"instance_id":"eb98bf50-1f3d-4614-a887-390bedb6ea57","source_id":"bcebb0df-f917-4d1a-b9a9-e18b92d2f7de","message":"Recorded a affirms instance of claim 86063716-4ef1-48dc-abe6-02ef8b75f176 from https://arxiv.org/abs/2510.01070. It now counts among the claim's source instances; weigh its stance in your assessment like any other."}