{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An assistant chooses the wrong API or sends arguments your product cannot use.",
    "change": "Apply the proposed tool-selection or argument-generation change in a sandbox.",
    "baseline": "Your current agent with the same tool schemas and task 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 completed actions, invalid calls, retries, latency, and cost per completed 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": "2610.02038",
  "title": "Mimir: Physics-Grounded LLM Agents for Long-Horizon Irrigation Control",
  "summary": "The method replaces direct LLM-to-actuator control with an architecture where LLM proposals are checked and revised against a deterministic physics simulator, executed via bounded deterministic action selection, and improved slowly through consolidated contextual principles while the physical model stays immutable. The cost is substantial engineering: a domain-specific simulator, a structured physical interface, an evaluator, and per-site validation, plus ongoing LLM inference at every decision step. It can fail when the simulator misrepresents real dynamics, when persistent principles consolidate spurious patterns, and its evidence comes from retrospective evaluation on irrigation rather than live deployments or other domains.",
  "rationale": "The verified-proposal and bounded-action pattern is transferable to physical or high-stakes control, but the reader lacks evidence it applies outside irrigation, so adoption is premature while the design principle is worth tracking.",
  "technique": "Physics-grounded LLM agent with verified revision and persistent context",
  "family": "uso_de_ferramenta",
  "recommendation": "observar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.02038",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "source_url": "https://arxiv.org/abs/2610.02038",
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    },
    {
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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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  "risks": [],
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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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}