Run step

Step 12: tool results

2026-11-14 20:42:32 UTC · previous step · next step

add_argument

Input

{
  "name": "The argument from discovery case studies",
  "stance": "for",
  "claim_id": "4dbe502e-402c-4ae9-8c6f-57a252a0d324",
  "description": "SAEs and related sparse dictionaries have surfaced concepts researchers had not specified in advance (rhyme planning, arithmetic heuristics, hidden objectives, persona features), and enumeration-then-selection is a task baselines are not built for."
}

{"success":true,"message":"Created argument \"The argument from discovery case studies\" (for) on claim 4dbe502e-402c-4ae9-8c6f-57a252a0d324. 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":"09caf9a4-6968-4c4c-9fd3-4eaa6b5adda6"}

add_argument

Input

{
  "name": "The argument from matched comparisons with simpler methods",
  "stance": "against",
  "claim_id": "4dbe502e-402c-4ae9-8c6f-57a252a0d324",
  "description": "Where SAE-based discovery has been run against simpler alternatives on the same discovery task, the alternatives match or beat it, so the case studies show SAEs can find things, not that they are powerful at it."
}

{"success":true,"message":"Created argument \"The argument from matched comparisons with simpler methods\" (against) on claim 4dbe502e-402c-4ae9-8c6f-57a252a0d324. 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":"237dd1f1-d6f3-41b7-a81d-f132bb376640"}