Step 15: assistant

Calls place_argument_passage

{
  "stance": "advances",
  "reading": "full",
  "argument_id": "62410a2b-7823-4525-b7fb-92492d436f4f",
  "instance_id": "0bffbda7-9cdc-424b-ae78-a76d5a0296e2"
}

Calls place_argument_passage

{
  "stance": "advances",
  "reading": "premise_unstated",
  "argument_id": "62410a2b-7823-4525-b7fb-92492d436f4f",
  "instance_id": "c0f0f8e8-0dc5-48fa-90fa-399cd2787b7a"
}

Calls record_argument_instance

{
  "url": "https://arxiv.org/pdf/2602.10371",
  "title": "Simple LLM Baselines are Competitive for Model Diffing",
  "stance": "mentions",
  "context": "Limitations section of the paper reporting the comparison.",
  "speaker": "Kempf, Schrodi, Cywiński, Brox, Nanda, Conmy",
  "argument_id": "cf14b884-312e-48ee-bc52-d00859298e17",
  "source_date": "2026-02-10",
  "source_kind": "preprint",
  "verbatim_text": "Automatic evaluation has inherent limitations: LLM judges can be unreliable, and while our metrics provide valuable signal, they cannot guarantee hypothesis quality. Manual verification of high-impact hypotheses remains essential."
}

Calls set_claim_importance

{
  "claim_id": "846a4d41-3752-45ed-9210-b4177d75b5e9",
  "reasoning": "A specific methodological result inside the live debate over whether SAEs earn their cost relative to simple baselines in interpretability; consequential for practitioners choosing diffing tools but resting on one preprint and not widely disputed in print.",
  "importance": 0.35,
  "contestation": 0.3
}