{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A multi-step product task fails because the agent loses track of dependencies.",
    "change": "Try the proposed planning step on tasks with checkable intermediate outcomes.",
    "baseline": "The same agent solving the same tasks without the new planning step.",
    "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": "Task completion, unnecessary steps, planning tokens, response time, and total cost.",
    "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": "2610.30379",
  "title": "MemPlan: Memory-Conditioned PDDL Planning for Partially Observable Text Environments",
  "summary": "The method replaces direct LLM-based action generation (e.g., ReAct) with a constrained LLM interface that parses observations into PDDL facts for a classical planner, reducing online inference costs. It requires offline supervised fine-tuning to train the language-to-symbol interface, adding training complexity and engineering effort for schema design. Performance may degrade if the parser hallucinates facts, if the PDDL domain becomes outdated, or if the environment dynamics exceed the representational capacity of the fixed symbolic model.",
  "rationale": "While the approach significantly reduces online token costs and inference latency by offloading planning to a symbolic solver, it requires offline supervised fine-tuning infrastructure and careful PDDL domain engineering that may not transfer robustly to arbitrary production environments without validation.",
  "technique": "Memory-Conditioned PDDL Planning for LLM Agents",
  "family": "planejamento_e_decomposicao",
  "recommendation": "testar",
  "published": "2026-10-21",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30379",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 126,
  "citations": 0,
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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      "dimension": "reproducibility",
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  "risks": [],
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
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}