{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Agreement with human review, missed failures, false alarms, and evaluation cost per task.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.21082",
  "title": "Sci$Π$vot: Benchmarking Principal-Investigator Judgment at Pivotal Moments of Scientific AutoResearch Agents",
  "summary": "Sci-Pi-vot replaces ad hoc or anecdotal assessment of scientific judgment in long-running LLM research agents with a 248-case benchmark built from real human corrections across 26 projects, where models must choose between the original LLM plan and a human-corrected alternative. It costs nothing to run at small scale beyond API calls and evaluation harness effort, but it is an evaluation artifact, not a deployable method, so it consumes budget without improving any production system directly. The findings imply a concrete failure mode for adoption: frontier models prefer their own flawed plans even when given full project context, added reasoning effort and reconsideration do not reliably fix this, and context access can actively degrade judgment, so autonomous research agents cannot yet be trusted to self-correct at pivotal decisions.",
  "rationale": "This is a benchmark demonstrating that current models lack reliable scientific judgment, which is decision-relevant context for anyone deploying autonomous research agents but is not itself a technique the reader can adopt for a measured gain.",
  "technique": "Sci-Pi-vot: benchmark of PI-level research judgment",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21082",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 136,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Sci-Pi-vot replaces ad hoc or anecdotal assessment of scientific judgment in long-running LLM research agents with a 248-case benchmark built from real human corrections across 26 projects, where models must choose between the original LLM plan and a human-corrected alternative. It costs nothing to run at small scale beyond API calls and evaluation harness effort, but it is an evaluation artifact, not a deployable method, so it consumes budget without improving any production system directly. The findings imply a concrete failure mode for adoption: frontier models prefer their own flawed plans even when given full project context, added reasoning effort and reconsideration do not reliably fix this, and context access can actively degrade judgment, so autonomous research agents cannot yet be trusted to self-correct at pivotal decisions.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.21082",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Highest model accuracy is 63.5% versus 73.4% for fresh researchers; four of five frontier models score below chance at medium reasoning effort, and project-record access lowers accuracy for every model tested.",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2610.21082",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "next_check": "",
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      "dimension": "compute",
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    },
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}