{
  "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": "2610.11344",
  "title": "EvoSim: Learning to Model, Modeling to Learn",
  "summary": "The method replaces manual expert selection of physical mechanisms, governing equations, couplings, and parameter identification with an LLM-driven multi-agent loop that revises model structure from experimental discrepancies and validates on held-out data. It costs a sustained multi-agent orchestration layer with iterative experiment-evaluation cycles, substantial API or compute budget per modeling run, and access to structured experimental datasets from the target physical domain. It can fail when experimental data are sparse or noisy enough that discrepancy-driven revisions converge to physically implausible mechanisms, and the reported gains are demonstrated only on two industrial battery tasks, so transfer to other domains is unvalidated.",
  "rationale": "The approach requires domain-specific experimental data, iterative multi-agent runs, and validation infrastructure that exceed a 24 GB GPU budget, and its benefits are shown only in a narrow battery-modeling setting, so it merits monitoring rather than adoption.",
  "technique": "Self-evolving multi-agent system for physics-based scientific modeling",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11344",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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    {
      "claim_id": "claim-02",
      "statement": "Self-evolution reduces model and physics errors by approximately 36% relative to baseline; lithium-plating onset prediction MAE of 1.79% in state of charge and dynamic voltage RMSE of 7.62 mV, exceeding reported human-expert model accuracy.",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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  ],
  "risks": [],
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  "open_questions": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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