Step 8: tool results

query_sql

{
  "sql": "SELECT message_id, node_id, trigger, sender_kind, paid, left(body, 300) AS body, created_at, read_at, outcome FROM messages WHERE sender_node_id = $1 ORDER BY created_at",
  "params": [
    "e0abe646-c78f-417c-813f-b8e2c15d775c"
  ]
}

{"success":true,"columns":["message_id","node_id","trigger","sender_kind","paid","body","created_at","read_at","outcome"],"rows":[["27a1b56a-eb41-434f-a75b-a5f4d37504ed","bda65b89-5ecb-4728-bfe9-0b54ee7fbc25","subclaim_change","steward",false,"First assessment of \"Increasing Gaussian weight noise improves benchmark performance in sandbagging LLMs, unlike the degradation in non-sandbagging models\": inconclusive (0.72), no credence. Established for prompted sandbagging against the same model unprompted; mixed to negative for fine-tuned sand","2026-11-14T07:01:16.315Z",null,null]],"row_count":1,"truncated":false,"elapsed_ms":48,"plan_cost":8}

get_claim_dependents

{
  "claim_id": "e0abe646-c78f-417c-813f-b8e2c15d775c"
}

{
  "child_claim_id": "e0abe646-c78f-417c-813f-b8e2c15d775c",
  "dependents": [
    {
      "id": "bda65b89-5ecb-4728-bfe9-0b54ee7fbc25",
      "text": "Under what conditions can evaluators reliably detect language models deliberately underperforming capability evaluations?",
      "type": "empirical_derived",
      "kind": "question",
      "relation": "answers",
      "current_status": null,
      "confidence": null,
      "credence": null
    },
    {
      "id": "250fee4e-8706-4658-b579-b0382bfae55f",
      "text": "Sandbagging detectors are less reliable against models fine-tuned to imitate a weaker model's outputs than against prompted or prompt-distilled sandbaggers.",
      "type": "empirical_derived",
      "kind": "claim",
      "relation": "contradicts",
      "current_status": "supported",
      "confidence": 0.65,
      "credence": 0.68
    }
  ],
  "count": 2
}

read_arxiv_source

{
  "arxiv_id": "2608.29458"
}

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