Step 20: tool results

{
  "verdict": "slight",
  "decisive": false,
  "evaluation": "Granting its premises, the argument shows that sparse dictionaries can surface concepts researchers had not specified, but \"powerful\" needs more: that they find such concepts more often or more cheaply than alternatives. Case studies without comparators cannot show that, and the premise that only dictionaries can enumerate candidate concepts is not established, since behavioural elicitation, embedding search and language-model summarisers also generate unprompted hypotheses. The argument rests on [[claim:5c397b68-66c7-4e8e-b2b4-22d77ac1ec06]], a well-evidenced finding with no matched alternative, and on [[claim:391dac24-440f-438b-8cff-8933dfac302c]], a positive result conditional on the dictionary's training data. Not decisive.",
  "argument_id": "09caf9a4-6968-4c4c-9fd3-4eaa6b5adda6"
}

{
  "verdict": "likely",
  "decisive": false,
  "evaluation": "Granting its premises, the argument shows that in hidden-behaviour auditing and output-based model diffing, SAEs have not outperformed simpler methods, which counts substantially against a general claim of power. It does not reach circuit-level discovery of mechanisms, where no matched alternative has been tested, and the AuditBench null in [[claim:461a798c-19b2-4b7a-9f26-aa1dfed627fb]] used a weak SAE tool. It lives on that null, on [[claim:846a4d41-3752-45ed-9210-b4177d75b5e9]] and on the authors' own concession in [[claim:5393957c-bef5-4262-a152-84c5fe63ece2]]. Not decisive.",
  "argument_id": "237dd1f1-d6f3-41b7-a81d-f132bb376640"
}

{
  "claim_id": "4dbe502e-402c-4ae9-8c6f-57a252a0d324",
  "reasoning": "A prominent, actively argued position in interpretability that shapes research investment in SAEs; consequential within the field but not beyond it.",
  "importance": 0.55,
  "contestation": 0.65
}