Run step

Step 18: tool results

2026-11-14 20:43:24 UTC · previous step · next step

add_argument

Input

{
  "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."
}

{"success":true,"message":"Created argument \"The argument from the benchmark's agent success rates\" (for) on claim 461a798c-19b2-4b7a-9f26-aa1dfed627fb. 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":"922d4c89-22e3-4324-af3b-95111a847513"}

add_argument

Input

{
  "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."
}

{"success":true,"message":"Created argument \"The argument from behaviour-level gains and tool signal\" (against) on claim 461a798c-19b2-4b7a-9f26-aa1dfed627fb. 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":"d5c3334f-7115-4fe0-b8a5-697b47b2eda4"}

add_related_claim

Input

{
  "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"
}

{"success":true,"linked":true,"link_id":"cae8722c-d60e-421a-ab0d-cec6115177e5","message":"Linked 461a798c-19b2-4b7a-9f26-aa1dfed627fb <-> 9d6f5a8b-cb8e-41fa-a716-fca25bbf6b50 (related); it now shows as a see-also on both claims."}

add_related_claim

Input

{
  "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"
}

{"success":true,"linked":true,"link_id":"e514bdfe-6a07-4cb9-83f9-0e1781bb8eb6","message":"Linked 461a798c-19b2-4b7a-9f26-aa1dfed627fb <-> c9681fb1-216b-49bb-ac61-57a4e02cf329 (related); it now shows as a see-also on both claims."}

propose_parent_edge

Input

{
  "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."
}

{"success":true,"message":"Proposed the edge 86063716-4ef1-48dc-abe6-02ef8b75f176 -> 461a798c-19b2-4b7a-9f26-aa1dfed627fb (supports) to the Steward of 86063716-4ef1-48dc-abe6-02ef8b75f176; it decides whether to adopt it."}