{
  "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": "2609.29964",
  "title": "World Action Agent: Harnessing VLMs for Robot Manipulation via World Action Rehearsal",
  "summary": "WAA replaces end-to-end VLA policies and code-as-policy pipelines with a multi-agent harness in which general-purpose VLMs rehearse, preview, and correct actions inside a visual workspace before execution. It costs repeated VLM inference calls per decision (high API spend and per-step latency), automatic contact-view selection, skill evolution from expert videos, and optionally fine-tuning a smaller VLM on interaction traces. It can fail through compounding VLM perception or planning errors, rehearsal previews that diverge from real dynamics, and results that are validated only in simulation (LIBERO-Pro, robosuite) rather than on physical robots.",
  "rationale": "The gains depend on robot hardware, simulation benchmarks, and heavy per-step VLM inference that exceed a constrained production budget, so the reader should track the harness-and-rehearsal pattern rather than deploy it.",
  "technique": "VLM robot harness with visual action rehearsal",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.29964",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 217,
  "citations": 0,
  "claims": [
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      "claim_id": "claim-02",
      "statement": "75.6% average success on LIBERO-Pro, state of the art versus end-to-end VLAs and code-as-policy agents; fine-tuning Qwen3.5-9B on harness traces raises out-of-domain success from 1.7% to 43.3% (about 25x relative).",
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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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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
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  "equations_status": "not_fetched",
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}