Step 15: assistant

Calls evaluate_argument

{
  "verdict": "likely",
  "decisive": false,
  "evaluation": "Granting its premises, the argument makes it much more likely that SAEs can surface knowledge across a semantic gap. The SAE route passes through the audited model's activations, and a model-free retriever failed on the same inputs. It is not decisive. The evidence is one synthetic model whose objective was stated outright in training documents that the SAE was also trained on. The baseline comparison was qualitative and post hoc, using a single small embedding model. And the Assistant-token hypothesis was formed after the relevant feature had been seen. Stronger retrieval baselines or less salient knowledge could narrow the result.",
  "argument_id": "83ea837b-7db6-4ea9-a189-0d6ef275308f"
}

Calls evaluate_argument

{
  "verdict": "slight",
  "decisive": false,
  "evaluation": "Granting its premises, the argument defeats the audit's headline SAE success as evidence of revealed knowledge, since semantic search matched it. It bears only slightly on the claim as a whole, which is an existence claim. The argument does not address the cases where semantic search failed and the SAE succeeded: behaviour demonstrations and the Assistant token. Its point that the SAE's training corpus contained the teaching documents is a real limit on how far the result generalises. It does not show that those features only track surface similarity.",
  "argument_id": "0df54db7-5733-4325-b50b-5f95ec412da9"
}

Calls set_claim_importance

{
  "claim_id": "fc567967-00d4-456b-b196-d188db1187c4",
  "reasoning": "A live point in the debate over whether SAEs beat simple baselines in downstream use, with bearing on alignment auditing. The evidence base is narrow and the claim sits within one subfield, so this is between notable and major.",
  "importance": 0.5,
  "contestation": 0.55
}