{
  "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": "2610.11480",
  "title": "RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes",
  "summary": "RoboAware replaces hand-designed or success-only selection among robot policy families (modular skills vs frozen end-to-end policies) with a learned state-conditioned coordinator trained on counterfactual branch outcomes via State-Locked Counterfactual Branching and MCTS-plus-Q-learning distillation. The cost is a training pipeline requiring state-restorable simulators, repeated counterfactual rollouts per state, and MCTS search, plus a frozen coding agent in the loop at deployment, adding latency per decision. It can fail where simulators cannot reliably lock and restore physical states, where counterfactual outcomes do not transfer to real hardware, and on tasks whose state distribution differs from the evaluated single-episode benchmarks.",
  "rationale": "The method targets embodied robotics with simulator state-restoration infrastructure and robot evaluation hardware that a constrained 24 GB GPU engineering team typically lacks, and its gains are validated only in single-episode simulation benchmarks.",
  "technique": "Counterfactual skill-family coordinator for embodied agents",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11480",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 14,
  "citations": 0,
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      "dimension": "compatibility",
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