{
  "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": "2609.18520",
  "title": "AeroWeaver: An Embodied-Agent Harness for Weaving Aerial Skills into Distributed, Adaptive Swarm Execution",
  "summary": "AeroWeaver replaces a centralized agent that generates joint actions from global context with role-conditioned body-local agents, governed skill grounding, and reward-guided training-free online skill-selection updates for UAV swarms. The cost is the integration and maintenance of a skill library, role conditioning, and per-agent state-action-reward experience indexing, plus LLM inference latency or API dependency at runtime. It can fail if mission conditions deviate from the tested ones (valid execution is only claimed 'under tested conditions'), if the governed skill set lacks coverage for emergent mission needs, or if reward signals misguide online adaptation in distributed settings without global context.",
  "rationale": "The method targets embodied UAV swarm operations, which are outside the stated production scope of a constrained-infrastructure ML engineering practice, and the abstract reports no quantified performance improvement to justify adoption.",
  "technique": "LLM-agent harness for distributed UAV swarm coordination",
  "family": "orquestracao_multiagente",
  "recommendation": "nao_aplica",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.18520",
  "independent_implementations": 0,
  "total_implementations": 2,
  "stars": 218,
  "citations": 0,
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  "equations_status": "not_fetched",
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}