Step 4: tool results

search_similar_claims

{
  "limit": 6,
  "query": "Cheap monitors or sampled auditing can approximate expensive amplified oversight without overseeing every action"
}

{
  "query": "Cheap monitors or sampled auditing can approximate expensive amplified oversight without overseeing every action",
  "results": [
    {
      "id": "0a64000e-ce65-4078-b4f6-fc0ebb861ccf",
      "kind": "claim",
      "text": "Obtaining amplified-oversight signals for every action of very powerful AI is too costly during training and deployment.",
      "claim_type": "empirical_derived",
      "state": "active",
      "similarity_score": 0.5595046753597589,
      "importance": 0.35,
      "assessment_status": null,
      "assessment_confidence": null
    },
    {
      "id": "b9f81bbe-39c6-411b-b29c-18309804ea9c",
      "kind": "claim",
      "text": "For obvious sabotage, action-only monitoring can outperform chain-of-thought monitoring because models may rationalize sabotage deceptively.",
      "claim_type": "empirical_derived",
      "state": "active",
      "similarity_score": 0.5556413090003484,
      "importance": 0.35,
      "assessment_status": "supported",
      "assessment_confidence": 0.75
    },
    {
      "id": "e04aa07d-8063-4f27-b35f-9b8d34fac642",
      "kind": "claim",
      "text": "Amplified oversight aims to mitigate specification gaming, while robust training and monitoring address goal misgeneralization.",
      "claim_type": "causal",
      "state": "active",
      "similarity_score": 0.5438033647009802,
      "importance": 0.4,
      "assessment_status": null,
      "assessment_confidence": null
    },
    {
      "id": "ea57432c-c473-4452-94e1-16102c805446",
      "kind": "claim",
      "text": "In AI control evaluations, weak trusted monitors with resampling, replay, and auditing catch most sabotage attempts by stronger untrusted models.",
      "claim_type": "empirical_derived",
      "state": "active",
      "similarity_score": 0.540151022066194,
      "importance": 0.4,
      "assessment_status": null,
      "assessment_confidence": null
    },
    {
      "id": "a9c96aaf-478f-46e2-bd00-534e7086fcdc",
      "kind": "claim",
      "text": "Human cognitive biases can reduce the reliability of oversight judgments, including in amplified oversight.",
      "claim_type": "causal",
      "state": "active",
      "similarity_score": 0.5302493087381,
      "importance": 0.35,
      "assessment_status": null,
      "assessment_confidence": null
    },
    {
      "id": "76f018a8-074a-4315-bb87-7cff91bbdf4c",
      "kind": "claim",
      "text": "In Guard-favored oversight games, multi-step nested scalable oversight outperforms single-step oversight.",
      "claim_type": "empirical_derived",
      "state": "active",
      "similarity_score": 0.5192239754337021,
      "importance": 0.35,
      "assessment_status": "supported",
      "assessment_confidence": 0.6
    }
  ],
  "count": 6
}

search_similar_claims

{
  "limit": 6,
  "query": "Computational overhead of running a monitor or debate protocol on every model action"
}

{
  "query": "Computational overhead of running a monitor or debate protocol on every model action",
  "results": [
    {
      "id": "4c3b7bcf-c9a9-426e-af04-0e88469cf505",
      "kind": "claim",
      "text": "In OpenAI's monitorability evaluation suite, chain-of-thought-only monitors outperform monitors seeing only actions and final outputs.",
      "claim_type": "empirical_verifiable",
      "state": "active",
      "similarity_score": 0.541205921967179,
      "importance": 0.3,
      "assessment_status": "supported",
      "assessment_confidence": 0.75
    },
    {
      "id": "aac4e677-69e5-4219-947c-727a4acbd1ae",
      "kind": "claim",
      "text": "CoT monitorability evaluations should test whether reasoning traces inform monitors about models' subsequent actions.",
      "claim_type": "normative",
      "state": "active",
      "similarity_score": 0.5287654741819131,
      "importance": 0.5,
      "assessment_status": null,
      "assessment_confidence": null
    },
    {
      "id": "0a64000e-ce65-4078-b4f6-fc0ebb861ccf",
      "kind": "claim",
      "text": "Obtaining amplified-oversight signals for every action of very powerful AI is too costly during training and deployment.",
      "claim_type": "empirical_derived",
      "state": "active",
      "similarity_score": 0.5275236476095294,
      "importance": 0.35,
      "assessment_status": null,
      "assessment_confidence": null
    },
    {
      "id": "f64fab11-5acc-4afe-9f87-512b666473d6",
      "kind": "claim",
      "text": "A chain-of-thought monitor can detect behavior without the reasoning trace faithfully reflecting the model's computation.",
      "claim_type": "empirical_derived",
      "state": "active",
      "similarity_score": 0.5252904251277445,
      "importance": 0.35,
      "assessment_status": "supported",
      "assessment_confidence": 0.85
    },
    {
      "id": "970e42a2-38d5-4349-8bc6-8c9925015ee3",
      "kind": "claim",
      "text": "Optimizing reasoning models' chains of thought against a monitor leads to reward hacking that the monitor cannot detect.",
      "claim_type": "causal",
      "state": "active",
      "similarity_score": 0.5198457136145486,
      "importance": 0.55,
      "assessment_status": "supported",
      "assessment_confidence": 0.72
    },
    {
      "id": "3448b7af-6fa0-4c48-a097-64882df6b2d4",
      "kind": "claim",
      "text": "Chain-of-thought monitoring depends on models externalizing legible reasoning, which future models may stop doing.",
      "claim_type": "empirical_derived",
      "state": "active",
      "similarity_score": 0.5138268176848759,
      "importance": 0.5,
      "assessment_status": "supported",
      "assessment_confidence": 0.8
    }
  ],
  "count": 6
}