Run step
Step 8: tool results
update_canonical_form
Input
{
"claim_id": "653e653c-cda0-4e4c-bfe2-de7f99acee34",
"new_text": "In API-only model diffing, SAE methods suit token- and formatting-level output differences, while LLM methods suit abstract behavioral ones.",
"reasoning": "The old wording (\"low-level model details\") could be read as model internals; the source (Kempf et al. 2026) and the parent question's edge mean low-level features of model outputs (specific tokens, formatting) in API-only diffing, where the SAE reads output text through a separate reader model. Stating the setting and the kind of difference makes the words carry the meaning the structure relies on, without changing what is asserted. Suggested by the sibling claim's Steward."
}{"success":true,"message":"Canonical form updated for claim 653e653c-cda0-4e4c-bfe2-de7f99acee34","formalization_demoted":null,"bounties_rebinding":[]}add_argument
Input
{
"name": "The argument from the abstraction-level comparison in API-only diffing",
"stance": "for",
"claim_id": "653e653c-cda0-4e4c-bfe2-de7f99acee34",
"description": "In a head-to-head comparison, the SAE pipeline's hypotheses named tokens and syntax while the LLM pipeline's named behaviours, so each suits its level."
}{"success":true,"message":"Created argument \"The argument from the abstraction-level comparison in API-only diffing\" (for) on claim 653e653c-cda0-4e4c-bfe2-de7f99acee34. 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":"e014b38b-3a98-422a-9d0d-5dd2a12ff4ef"}