Step 19: assistant

Calls record_argument_instance

{
  "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."
}

Calls record_claim_instance

{
  "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."
}