Minerval

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Step 8: tool results

2026-11-14 03:03:55 UTC · previous step · next step

get_claim_instances

Input
{
  "claim_id": "5e4c99e3-f697-45fd-b79e-d75cb33a5732"
}
{
  "claim_id": "5e4c99e3-f697-45fd-b79e-d75cb33a5732",
  "total": 1,
  "limit": 20,
  "offset": 0,
  "has_more": false,
  "instances": [
    {
      "id": "5a857811-24a7-4796-a966-bb7a2a8aab09",
      "source_id": "87406e11-4df5-49b3-8558-11e864316894",
      "verbatim_text": "Given all of this, I think there’s a ~60% chance we see automated AI R&D (where a frontier model is able to autonomously train a successor version of itself) by the end of 2028.",
      "context": "Concluding forecast of the essay, published May 2026. Opening restates it as 'a likely chance (60%+) that no-human-involved AI R&D - an AI system powerful enough that it could plausibly autonomously build its own successor - happens by the end of 2028.'",
      "stance": "affirms",
      "confidence": 0.9,
      "speaker": null,
      "publication": null,
      "source_date": null,
      "link": null,
      "source_title": "Import AI 455: AI systems are about to start building themselves.",
      "source_type": "blog_post",
      "source_url": "https://importai.substack.com/p/import-ai-455-automating-ai-research",
      "source_published_date": null,
      "source_authors": [
        "Jack Clark"
      ],
      "source_publisher": null,
      "source_doi": null,
      "source_interest": null,
      "recorded_by": "extractor",
      "display_rank": null,
      "reading": null,
      "draws_on": [],
      "story": {
        "standing": "untraced",
        "basis": "none",
        "depth": 0,
        "diverges": false,
        "downstream_total": 0
      }
    }
  ]
}

search_similar_claims

Input
{
  "limit": 8,
  "query": "independent evaluation of AI agents end-to-end ML research ability benchmark results"
}
{
  "query": "independent evaluation of AI agents end-to-end ML research ability benchmark results",
  "results": [
    {
      "id": "6d318e34-dfe2-4f86-a5d8-d7f71179125d",
      "kind": "claim",
      "text": "Current evaluations poorly measure AI agents' ability to conduct open-ended research.",
      "claim_type": "evaluative",
      "state": "active",
      "similarity_score": 0.6491166168609296,
      "importance": 0.4,
      "assessment_status": "supported",
      "assessment_confidence": 0.7
    },
    {
      "id": "6a916280-b09d-4f26-b079-e7d392e6eeeb",
      "kind": "claim",
      "text": "AI agents' success on real-world software tasks judged by maintainer review improves at a similar rate to their benchmark success.",
      "claim_type": "empirical_derived",
      "state": "active",
      "similarity_score": 0.6188108261015796,
      "importance": 0.4,
      "assessment_status": "doubtful",
      "assessment_confidence": 0.5
    },
    {
      "id": "d48672dc-3d4b-4b00-ace2-141400539476",
      "kind": "claim",
      "text": "In InnovationEval, AI agents' research reports sometimes overstated scores by omitting selection of the best result from similar runs.",
      "claim_type": "empirical_derived",
      "state": "active",
      "similarity_score": 0.6149032716851085,
      "importance": 0.5,
      "assessment_status": null,
      "assessment_confidence": null
    },
    {
      "id": "da087b63-9f41-43a1-b6b3-d6d804623bb2",
      "kind": "claim",
      "text": "Reliable AI agents will substantially advance AI research by the end of 2025.",
      "claim_type": "empirical_derived",
      "state": "active",
      "similarity_score": 0.5957998990457976,
      "importance": 0.35,
      "assessment_status": "doubtful",
      "assessment_confidence": 0.65
    },
    {
      "id": "9493359e-c047-4b55-a8bb-be6049818df6",
      "kind": "claim",
      "text": "Frontier AI agents' performance on long-horizon AI R&D tasks varies substantially across repeated runs.",
      "claim_type": "empirical_verifiable",
      "state": "active",
      "similarity_score": 0.5948749173101819,
      "importance": 0.55,
      "assessment_status": null,
      "assessment_confidence": null
    },
    {
      "id": "f2bff4f2-c6ee-4950-bf76-eb83969f2707",
      "kind": "question",
      "text": "How can measured AI productivity effects on developers be reconciled with benchmark scores and anecdotal reports of AI helpfulness and adoption?",
      "claim_type": "empirical_derived",
      "state": "active",
      "similarity_score": 0.5872728173988363,
      "importance": 0.55,
      "assessment_status": null,
      "assessment_confidence": null
    },
    {
      "id": "78d6c0a0-a6d8-4b57-89eb-f193be0c0e0e",
      "kind": "claim",
      "text": "AI agent evaluations built around narrow, verifiable tasks do not assess open-ended research ability.",
      "claim_type": "evaluative",
      "state": "active",
      "similarity_score": 0.5866627130570019,
      "importance": 0.4,
      "assessment_status": "supported",
      "assessment_confidence": 0.7
    },
    {
      "id": "3675680d-92c0-4815-bf07-33f659720bea",
      "kind": "claim",
      "text": "Reliance on AI capability benchmarks leads to systematic overestimation of AI's real-world impact.",
      "claim_type": "causal",
      "state": "active",
      "similarity_score": 0.5822976902986341,
      "importance": 0.6,
      "assessment_status": "supported",
      "assessment_confidence": 0.55
    }
  ],
  "count": 8
}