Step 17: assistant

thinking

Calls add_argument

{
  "name": "The argument from the benchmark's agent success rates",
  "stance": "for",
  "claim_id": "461a798c-19b2-4b7a-9f26-aa1dfed627fb",
  "description": "Agent success with logit lens or SAEs stays near or below the default-agent baseline in all four target-model configurations."
}

Calls add_argument

{
  "name": "The argument from behaviour-level gains and tool signal",
  "stance": "against",
  "claim_id": "461a798c-19b2-4b7a-9f26-aa1dfed627fb",
  "description": "SAEs lift detection sharply on some individual behaviours and carry measurable signal in static evaluations, so the aggregate understates their benefit."
}

Calls add_related_claim

{
  "kind": "related",
  "claim_id": "461a798c-19b2-4b7a-9f26-aa1dfed627fb",
  "reasoning": "The AuditBench case study where SAEs raise Anti-AI Regulation detection from 1/16 to 8/16 is the evidence for that claim; it qualifies, without overturning, the aggregate finding here.",
  "other_claim_id": "9d6f5a8b-cb8e-41fa-a716-fca25bbf6b50"
}

Calls add_related_claim

{
  "kind": "related",
  "claim_id": "461a798c-19b2-4b7a-9f26-aa1dfed627fb",
  "reasoning": "The SAE and logit-lens tools evaluated here were delivered to the agent only after LLM-based semantic/coherence or KL-threshold filtering; whether filtering is needed bears on how to read their weak result.",
  "other_claim_id": "c9681fb1-216b-49bb-ac61-57a4e02cf329"
}

Calls propose_parent_edge

{
  "claim_id": "461a798c-19b2-4b7a-9f26-aa1dfed627fb",
  "relation": "supports",
  "parent_id": "86063716-4ef1-48dc-abe6-02ef8b75f176",
  "reasoning": "On AuditBench (Llama 3.3 70B targets, Claude Haiku 4.5 agent, 16 runs x 14 behaviours x 4 configurations), logit lens (~34% overall) and SAEs (~34.5%) sit at or slightly below the default agent (35.5%), far below scaffolded black-box tools (52-58%). This is one dataset's evidence that the white-box tools tested underperform black-box ones; it supports the parent without being required by it."
}