{
  "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": "2609.29044",
  "title": "Multi-Agent Orchestration of 3GPP Channel Estimators",
  "summary": "Replaces a single fixed channel estimator in 5G-NR/LTE OFDM receivers with a dispatcher that selects, per operating condition, the best of eight literature estimators based on a validation split. Costs include running all eight estimators concurrently (or paying selection latency), maintaining per-condition validation tables, and added system complexity, though data-parallel execution keeps latency near single-estimator levels. Can fail when deployment conditions drift from the validation split's coverage, when the dispatcher misclassifies the operating condition, or in regimes where the 1.07 dB gap to oracle represents systematic selection error.",
  "rationale": "The paper addresses wireless PHY-layer estimation rather than LLM or generative-AI workloads, so the reader should only adopt it if working on telecom receivers, though its condition-adaptive routing pattern is worth noting as a transferable design.",
  "technique": "Condition-adaptive multi-estimator orchestration for OFDM channel estimation",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.29044",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
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      "statement": "Tracks the per-realization oracle to within 1.07 dB and improves NMSE over the best fixed estimator by up to 3.6 dB at high SNR; data-parallel execution scales wall-clock nearly as 1/K, up to 6.9x.",
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  "report_available": false,
  "validation_tier": "unknown",
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