Step 14: tool results

{
  "relation": "supports",
  "parent_id": "4dbe502e-402c-4ae9-8c6f-57a252a0d324",
  "reasoning": "The flagship 'unknown concept' discovery the position paper cites (Lindsey et al. 2025, Biology of a Large Language Model): transcoder features revealed forward planning of a rhyme word, then confirmed by intervention. If it holds, it is a concrete case of a sparse dictionary surfacing a mechanism researchers did not specify in advance.",
  "seed_note": "Anthropic's 2025 attribution-graph study reports features for candidate rhyme words active at the end of the first line, and that suppressing or injecting them changes the line the model writes. The intervention evidence is observable in outputs, which makes the finding fairly robust, though it comes from one laboratory and one model, and the more general idea that language models carry information about future tokens had been shown by earlier probing work.",
  "child_text": "Claude 3.5 Haiku represents a couplet's intended rhyme word before generating its second line.",
  "claim_type": "empirical_verifiable",
  "importance": 0.3,
  "argument_id": "09caf9a4-6968-4c4c-9fd3-4eaa6b5adda6",
  "contestation": 0.2,
  "seed_credence": 0.85
}

{
  "child_id": "391dac24-440f-438b-8cff-8933dfac302c",
  "relation": "supports",
  "parent_id": "4dbe502e-402c-4ae9-8c6f-57a252a0d324",
  "reasoning": "The one tool ablation isolating an SAE's marginal contribution to discovering a hidden objective, positive in its setting (win rate near 0% to 10-20%), though conditional on the dictionary's training data.",
  "argument_id": "09caf9a4-6968-4c4c-9fd3-4eaa6b5adda6"
}

{
  "child_id": "461a798c-19b2-4b7a-9f26-aa1dfed627fb",
  "relation": "contradicts",
  "parent_id": "4dbe502e-402c-4ae9-8c6f-57a252a0d324",
  "reasoning": "AuditBench: across 56 models with unspecified hidden behaviours, the SAE tool added nothing over a default agent while black-box elicitation tools added much; a matched discovery test with a null for SAEs.",
  "argument_id": "237dd1f1-d6f3-41b7-a81d-f132bb376640"
}

{
  "child_id": "846a4d41-3752-45ed-9210-b4177d75b5e9",
  "relation": "contradicts",
  "parent_id": "4dbe502e-402c-4ae9-8c6f-57a252a0d324",
  "reasoning": "Model diffing is one of the discovery uses the position paper cites (Jiang et al., Tjuatja and Neubig); a strengthened LLM baseline matched the SAE pipeline on that task.",
  "argument_id": "237dd1f1-d6f3-41b7-a81d-f132bb376640"
}

{
  "child_id": "5393957c-bef5-4262-a152-84c5fe63ece2",
  "relation": "contradicts",
  "parent_id": "4dbe502e-402c-4ae9-8c6f-57a252a0d324",
  "reasoning": "The headline SAE audit success could, by its authors' own account, have been replicated by embedding search over training data, so it does not show an SAE-specific advantage.",
  "argument_id": "237dd1f1-d6f3-41b7-a81d-f132bb376640"
}