{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A workflow needs specialist review, but extra agents may add more cost than useful work.",
    "change": "Test the proposed division of work against a single-agent implementation.",
    "baseline": "One agent with the same tools and an equal total token and time budget.",
    "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": "Accepted outputs, coordination failures, wall-clock time, and total cost across all agents.",
    "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": "2610.03056",
  "title": "MOF-VERIFY: A Failure-Aware Agentic Harness for MOF Hypothesis Verification",
  "summary": "The harness replaces direct LLM inference or simple retrieval-augmented verification with a staged agentic pipeline that resolves structural identity, gathers literature evidence, checks evidence sufficiency, and runs MLIP-based computation before issuing a verdict. It costs additional orchestration complexity, multiple LLM calls per hypothesis, and dependencies on external structure databases and machine-learned interatomic potential computations, all feasible on a single 24 GB GPU or via APIs but nontrivial to wire up. After adoption it can still fail when evidence is genuinely absent from sources, when structural identifiers are ambiguous, or when the MLIP prediction is inaccurate for the target chemistry, and the reported gains are specific to metal-organic framework verification.",
  "rationale": "The technique is a domain-specific agentic verification pattern validated only on MOF benchmarks with no quantified gain in the abstract, so the reader should track it rather than adopt it unless they build scientific-verification agents.",
  "technique": "Failure-aware agentic harness for scientific hypothesis verification",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-10-02",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.03056",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 12,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The harness replaces direct LLM inference or simple retrieval-augmented verification with a staged agentic pipeline that resolves structural identity, gathers literature evidence, checks evidence sufficiency, and runs MLIP-based computation before issuing a verdict. It costs additional orchestration complexity, multiple LLM calls per hypothesis, and dependencies on external structure databases and machine-learned interatomic potential computations, all feasible on a single 24 GB GPU or via APIs but nontrivial to wire up. After adoption it can still fail when evidence is genuinely absent from sources, when structural identifiers are ambiguous, or when the MLIP prediction is inaccurate for the target chemistry, and the reported gains are specific to metal-organic framework verification.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.03056",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "MOF-Verify substantially improves hypothesis-verification performance over direct inference and retrieval-based baselines across multiple backbone LLMs; no numeric magnitude is reported in the abstract.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.03056",
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      "source_excerpt": ""
    }
  ],
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      "finding": "",
      "interpretation": "",
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      "next_check": "",
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "finding": "",
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      "next_check": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
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      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}