Run step
Step 6: tool results
search_similar_claims
Input
{
"limit": 10,
"query": "Humans cannot reliably evaluate whether plans or outputs from superhuman AI are safe"
}{
"query": "Humans cannot reliably evaluate whether plans or outputs from superhuman AI are safe",
"results": [
{
"id": "44fdd6d3-8a0e-477a-a6ca-7375d5e376be",
"kind": "claim",
"text": "Simple AI oversight protocols improve human evaluators' accuracy over deferring to a mostly reliable AI model.",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.6265637802922075,
"importance": 0.5,
"assessment_status": "doubtful",
"assessment_confidence": 0.6
},
{
"id": "63a22f2c-9518-44dd-970b-2840296a9031",
"kind": "claim",
"text": "Researchers have not evaluated whether AI safety techniques remain effective against models intentionally trying to subvert them.",
"claim_type": "empirical_verifiable",
"state": "active",
"similarity_score": 0.6052398863153412,
"importance": 0.3,
"assessment_status": "contradicted",
"assessment_confidence": 0.85
},
{
"id": "ec4cba8f-f08e-4026-ab06-994d93e177a8",
"kind": "claim",
"text": "Human-feedback alignment methods may prove inadequate for superhuman AI without additional techniques.",
"claim_type": "causal",
"state": "active",
"similarity_score": 0.5882479385693172,
"importance": 0.75,
"assessment_status": null,
"assessment_confidence": null
},
{
"id": "dc02e01f-ce69-4dbe-ba36-ed0e2e90d071",
"kind": "claim",
"text": "Scalable oversight can reliably control superintelligent AI systems.",
"claim_type": "evaluative",
"state": "active",
"similarity_score": 0.5870127530978572,
"importance": 0.75,
"assessment_status": "doubtful",
"assessment_confidence": 0.72
},
{
"id": "353f02bc-ebdf-443f-a09e-38209d365bd4",
"kind": "claim",
"text": "Verifying an AI system's output for correctness and safety requires less capability than producing it.",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.5756934894592526,
"importance": 0.5,
"assessment_status": null,
"assessment_confidence": null
},
{
"id": "5477338c-4f5c-4049-a959-efbf3f23a988",
"kind": "claim",
"text": "Goal misgeneralization is a plausible mechanism for catastrophic risk from powerful AI systems.",
"claim_type": "causal",
"state": "active",
"similarity_score": 0.5734370566765578,
"importance": 0.65,
"assessment_status": "supported",
"assessment_confidence": 0.76
},
{
"id": "e15a36f4-edf3-45c5-92d9-85fcefae5190",
"kind": "claim",
"text": "Amplified oversight can supervise AI systems moderately beyond overseers’ capabilities, but not superintelligent systems.",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.5732480720423206,
"importance": 0.8,
"assessment_status": null,
"assessment_confidence": null
},
{
"id": "804cd71e-671c-46a6-a37b-eb6d5cff952a",
"kind": "claim",
"text": "Scalable oversight protocols remain effective against AI systems that deliberately deceive their overseers.",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.556249805938574,
"importance": 0.6,
"assessment_status": null,
"assessment_confidence": null
},
{
"id": "81f25be0-bba6-4327-9db3-43a44a177e4d",
"kind": "claim",
"text": "Current AI monitoring techniques are ineffective at obtaining real-world evidence of AI scheming.",
"claim_type": "evaluative",
"state": "active",
"similarity_score": 0.5508156112835821,
"importance": 0.45,
"assessment_status": null,
"assessment_confidence": null
},
{
"id": "535660bb-5b22-47c1-9c63-8206fc04d0e6",
"kind": "claim",
"text": "Human judges' knowledge unavailable to AI models contributed to positive results in earlier studies of simple oversight protocols.",
"claim_type": "causal",
"state": "active",
"similarity_score": 0.5488955321129176,
"importance": 0.4,
"assessment_status": "supported",
"assessment_confidence": 0.55
}
],
"count": 10
}search_similar_claims
Input
{
"limit": 8,
"query": "Obfuscated arguments: a dishonest debater can construct arguments whose flaw neither debater nor judge can locate"
}{
"query": "Obfuscated arguments: a dishonest debater can construct arguments whose flaw neither debater nor judge can locate",
"results": [
{
"id": "1ab6aea0-bea9-470b-a3b1-c9d2564fef77",
"kind": "claim",
"text": "In AI debates, dishonest debaters can win by making arguments too complex for opponents to refute.",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.6622283228848314,
"importance": 0.45,
"assessment_status": "supported",
"assessment_confidence": 0.75
},
{
"id": "a4225e85-09ed-4f2b-8cf2-23998858b16c",
"kind": "claim",
"text": "Under stability assumptions, prover-estimator debate provably lets an honest debater defeat obfuscated arguments with computational effort comparable to the dishonest debater's.",
"claim_type": "mathematical",
"state": "active",
"similarity_score": 0.6233749826967695,
"importance": 0.3,
"assessment_status": "supported",
"assessment_confidence": 0.8
},
{
"id": "4f4d4caf-b6c5-4b20-8bea-5425bd5f65d6",
"kind": "claim",
"text": "AI debate incentives to persuade the judge can conflict with debaters' epistemic honesty.",
"claim_type": "causal",
"state": "active",
"similarity_score": 0.5833766378822539,
"importance": 0.35,
"assessment_status": "supported",
"assessment_confidence": 0.85
},
{
"id": "9a81d74b-2861-4f13-a74d-c9bce7022413",
"kind": "claim",
"text": "In adversarial settings, small flaws in a safety argument can be exploited into complete failure",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.5691577808704296,
"importance": 0.4,
"assessment_status": "supported",
"assessment_confidence": 0.8
},
{
"id": "ae1d1d70-45fa-478f-aae4-5685f808a227",
"kind": "claim",
"text": "AI debate systematically favors the debater arguing for the true answer over the debater arguing for a false one.",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.534330141774088,
"importance": 0.55,
"assessment_status": "supported",
"assessment_confidence": 0.6
},
{
"id": "c70f8d62-2836-4b76-999a-83bc895fc930",
"kind": "claim",
"text": "Defeater analyses of real-world assurance cases identify concrete weaknesses in their arguments and evidence",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.5092088624017324,
"importance": 0.3,
"assessment_status": "supported",
"assessment_confidence": 0.8
},
{
"id": "aeba454c-e38f-4c15-925c-b52a896b1bea",
"kind": "claim",
"text": "AI debaters trained to win debates learn to exploit systematic errors in their judge.",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.5090012213998757,
"importance": 0.4,
"assessment_status": "supported",
"assessment_confidence": 0.65
},
{
"id": "9412fc93-bd20-4003-a309-cfd1b0ca40e8",
"kind": "claim",
"text": "AI debate reliably improves a weaker judge's accuracy only where the judge can check debaters' specific claims more easily than answer the question itself.",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.49717850151236265,
"importance": 0.4,
"assessment_status": "inconclusive",
"assessment_confidence": 0.6
}
],
"count": 8
}search_similar_claims
Input
{
"limit": 8,
"query": "Non-experts cannot reliably distinguish good expert work from bad without domain expertise"
}{
"query": "Non-experts cannot reliably distinguish good expert work from bad without domain expertise",
"results": [
{
"id": "fa4c5128-8cc9-4080-8c12-ed3be69ad889",
"kind": "claim",
"text": "Expert performance on isolated occupational tasks is insufficient to establish expert performance of the occupation.",
"claim_type": "evaluative",
"state": "active",
"similarity_score": 0.5647981190241277,
"importance": 0.45,
"assessment_status": "supported",
"assessment_confidence": 0.75
},
{
"id": "e573404a-40e4-4b33-8608-8bba367ae1a0",
"kind": "claim",
"text": "Individuals lack the time and expertise to independently verify most claims they encounter.",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.5046519764086604,
"importance": 0.2,
"assessment_status": null,
"assessment_confidence": null
},
{
"id": "babf37e4-0854-4924-b95e-30227fff9c53",
"kind": "claim",
"text": "Frontier models paired with expert review can complete professional knowledge-work tasks faster and cheaper than unaided experts.",
"claim_type": "causal",
"state": "active",
"similarity_score": 0.5042086683352559,
"importance": 0.55,
"assessment_status": "supported",
"assessment_confidence": 0.65
},
{
"id": "8f1936b6-a556-492b-8711-f64bbed3f5b4",
"kind": "claim",
"text": "For non-experts judging unfamiliar scientific topics, AI debate increased accuracy over prior beliefs, unlike single-expert consultancy.",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.49361945914328575,
"importance": 0.4,
"assessment_status": "supported",
"assessment_confidence": 0.7
},
{
"id": "2e6a6348-a373-48b5-b7fa-f26a3c3342c5",
"kind": "claim",
"text": "LLM-generated research ideas lose their advantage over expert ideas when actually executed.",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.4613564760798188,
"importance": 0.4,
"assessment_status": "supported",
"assessment_confidence": 0.7
},
{
"id": "72ac75a9-6f75-45b9-997c-205f081655f1",
"kind": "claim",
"text": "AI agents outperform human experts on short AI research-engineering tasks but underperform them on longer tasks.",
"claim_type": "empirical_verifiable",
"state": "active",
"similarity_score": 0.45811405630082347,
"importance": 0.35,
"assessment_status": null,
"assessment_confidence": null
},
{
"id": "35f94a14-6fb4-4ee5-bde9-ec3131c396fd",
"kind": "claim",
"text": "GDPval's one-shot, well-specified tasks and blinded expert pairwise grading validly measure real professional deliverable quality.",
"claim_type": "evaluative",
"state": "active",
"similarity_score": 0.4515654859777207,
"importance": 0.5,
"assessment_status": "inconclusive",
"assessment_confidence": 0.65
},
{
"id": "5896a332-e94d-4a02-9104-84297d625fec",
"kind": "claim",
"text": "AI systems sometimes make mistakes on problems that people ignorant of the relevant field could avoid.",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.45059542153732224,
"importance": 0.25,
"assessment_status": "verified",
"assessment_confidence": 0.9
}
],
"count": 8
}search_similar_claims
Input
{
"limit": 8,
"query": "Finding deliberately hidden vulnerabilities in code is harder than inserting them"
}{
"query": "Finding deliberately hidden vulnerabilities in code is harder than inserting them",
"results": [
{
"id": "164bc6f3-e1db-4b37-a5ab-c7a140c29409",
"kind": "claim",
"text": "Optimizing adversaries systematically search for and exploit the weakest point of a system",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.49218635297706836,
"importance": 0.2,
"assessment_status": null,
"assessment_confidence": null
},
{
"id": "8bb08f88-c7d1-4c69-9d4f-a6047e2c7eb6",
"kind": "claim",
"text": "Deliberately implanted backdoors in tested language models can persist through standard safety training.",
"claim_type": "empirical_verifiable",
"state": "active",
"similarity_score": 0.48667611330795446,
"importance": 0.6,
"assessment_status": "verified",
"assessment_confidence": 0.85
},
{
"id": "ae8dd434-286f-41ad-95fc-6beda306f634",
"kind": "claim",
"text": "Backdoors inserted with SOAP are more robust to HHH safety fine-tuning than backdoors inserted with Adam.",
"claim_type": "causal",
"state": "active",
"similarity_score": 0.4840029948276886,
"importance": 0.3,
"assessment_status": null,
"assessment_confidence": null
},
{
"id": "91d4e6ae-20ca-46df-acb2-c17305ec53b1",
"kind": "claim",
"text": "A single overlooked vulnerability can compromise the security of an entire system",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.48291722611019705,
"importance": 0.15,
"assessment_status": null,
"assessment_confidence": null
},
{
"id": "7df88aa2-0256-42a2-a986-a039c1b0c13a",
"kind": "claim",
"text": "Fine-tuning can implant backdoors in reasoning models that produce attacker-chosen outputs while showing benign reasoning traces.",
"claim_type": "empirical_verifiable",
"state": "active",
"similarity_score": 0.46995732114575306,
"importance": 0.7,
"assessment_status": null,
"assessment_confidence": null
},
{
"id": "e23d0c65-3ab1-4b7c-ba92-2400dc6785a0",
"kind": "claim",
"text": "Defense in depth prevents a single exploited flaw from causing complete system failure",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.4657718093288492,
"importance": 0.3,
"assessment_status": null,
"assessment_confidence": null
},
{
"id": "298238d0-dc6d-4ec5-932f-32487f9e2952",
"kind": "claim",
"text": "Independent adversarial review detects significant flaws that safety case developers overlook",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.4586986204565633,
"importance": 0.4,
"assessment_status": "supported",
"assessment_confidence": 0.8
},
{
"id": "a2452764-4064-476d-9404-4fc9e6d8d3ca",
"kind": "claim",
"text": "Longer backdoor-insertion training increases a backdoor's robustness to subsequent untargeted training.",
"claim_type": "empirical_derived",
"state": "active",
"similarity_score": 0.4567941336268795,
"importance": 0.4,
"assessment_status": null,
"assessment_confidence": null
}
],
"count": 8
}