Step 22: tool results

add_argument

{
  "name": "The argument from the inductive bias of interpretable sparse bases",
  "stance": "for",
  "claim_id": "20fee30f-0192-4b8c-b479-83c3f230014d",
  "description": "If the target concept occupies a few SAE latents while spurious cues are diffuse or minor, a k-sparse probe will pick the concept and ignore the cues a dense probe can exploit."
}

{"success":true,"message":"Created argument \"The argument from the inductive bias of interpretable sparse bases\" (for) on claim 20fee30f-0192-4b8c-b479-83c3f230014d. 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":"9903c59d-51d9-4797-b696-379a92f9128e"}

add_argument

{
  "name": "The argument from head-to-head out-of-distribution comparisons",
  "stance": "against",
  "claim_id": "20fee30f-0192-4b8c-b479-83c3f230014d",
  "description": "Controlled comparisons on Gemma 2 SAEs find k-sparse SAE probes transfer worse under distribution shift than tuned dense logistic-regression probes."
}

{"success":true,"message":"Created argument \"The argument from head-to-head out-of-distribution comparisons\" (against) on claim 20fee30f-0192-4b8c-b479-83c3f230014d. 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":"5ee763da-20ce-4dca-b9da-7bb5524709d1"}