{
  "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": "2610.09034",
  "title": "Shared-Roadmap Generation and Evaluator for Multi-Agent Path Planning Using Heterogeneous Graph Neural Network",
  "summary": "The method replaces hand-designed roadmap construction (lattice grids or standard sampling-based roadmaps) with a heterogeneous GNN that prunes redundant nodes and edges and ranks critical waypoints for multi-agent path planning. It costs a training pipeline dependent on occupation density maps aggregated from expert solver trajectories, plus graph construction and inference overhead at deployment. It can fail on environments or task distributions unlike the training data, since the learned pruning decisions inherit the expert solver's biases and the reported gains apply to dense-roadmap settings only.",
  "rationale": "The technique targets robotics multi-agent path planning and requires expert-solver training data and domain-specific evaluation, making it relevant only to readers deploying MAPP systems rather than general LLM production work.",
  "technique": "GNN-based shared roadmap generation and pruning",
  "family": "planejamento_e_decomposicao",
  "recommendation": "observar",
  "published": "2026-10-06",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.09034",
  "independent_implementations": 2,
  "total_implementations": 2,
  "stars": 57,
  "citations": 0,
  "claims": [
    {
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      "source_url": "https://arxiv.org/abs/2610.09034",
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      "source_url": "https://arxiv.org/abs/2610.09034",
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  ],
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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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  ],
  "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",
  "core_kind": "",
  "equations_fetched_at": "",
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  "equations_source_sha256": "",
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}