{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your coding assistant makes changes that still need substantial repair before merging.",
    "change": "Try the technique on held-out issues from a repository with reliable tests.",
    "baseline": "The same coding agent, repository snapshot, tools, and token 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": "Correct patches, regression failures, human repair time, and total cost per accepted change.",
    "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": "2610.30292",
  "title": "GAV: A scientific experimentation environment for PDE agents",
  "summary": "Replaces fixed benchmark datasets and expensive numerical reference computations with a generative model of reference solutions that constructs verifiable PDE problems via the method of manufactured solutions. Costs include training a generator on simulation or experimental data and maintaining a closed-loop agent-verification pipeline, which increases system complexity and requires domain-specific validation data. Can fail if the generator learns a distribution that does not cover target physical regimes, produces solutions that permit trivial or non-representative problems, or if the verification metric poorly correlates with actual solver performance on downstream tasks.",
  "rationale": "GAV requires training a generative model on PDE solution data and running an iterative agent-verifier loop, which is feasible on a single 24GB GPU but demands workload-specific validation and data infrastructure that may exceed immediate small-team capacity.",
  "technique": "Generator-Agent-Verifier (GAV) for PDE solvers",
  "family": "agentes_de_codigo",
  "recommendation": "testar",
  "published": "2026-10-21",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30292",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 14,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Replaces fixed benchmark datasets and expensive numerical reference computations with a generative model of reference solutions that constructs verifiable PDE problems via the method of manufactured solutions. Costs include training a generator on simulation or experimental data and maintaining a closed-loop agent-verification pipeline, which increases system complexity and requires domain-specific validation data. Can fail if the generator learns a distribution that does not cover target physical regimes, produces solutions that permit trivial or non-representative problems, or if the verification metric poorly correlates with actual solver performance on downstream tasks.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.30292",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Reduces the mean test error of three existing PDE agents by more than an order of magnitude on the numerical Navier-Stokes task.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.30292",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}