{
  "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": "2610.28286",
  "title": "Local Work, Global Progress: A Convergence Theory of AI Agent Collaboration",
  "summary": "Replaces ad hoc, empirically designed multi-agent collaboration protocols with a block-coordinate scheme in which agents propose local edits with cross-part corrections and a coordinator merges proposals only after checking them against a global objective. Costs include extra coordinator inference per round, correction-computation overhead, and implementation complexity, all multiplied by the number of API or model calls across agents. Can fail when local corrections are inaccurate (raising the error floor), when proposals frequently interfere and combination checks reject them, or on tasks without a decomposable global objective, where the protocol adds cost without convergence benefit.",
  "rationale": "The protocol is implementable with API-based agents and no training, but its demonstrated gains are confined to routing and query-planning tasks with decomposable objectives, so the reader must validate it on their own workload before production adoption.",
  "technique": "Convergence-guaranteed multi-agent block coordinate optimization",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-10-20",
  "as_of": "2026-10-21",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.28286",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 10,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "source_url": "https://arxiv.org/abs/2610.28286",
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    },
    {
      "claim_id": "claim-02",
      "statement": "A six-agent team completes a large robot-routing search about 2x faster than a single agent (median), and merging the best checked combination of proposals cuts team search time by about 30% further.",
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    }
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      "dimension": "compatibility",
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    },
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      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
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      "dimension": "reproducibility",
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  ],
  "risks": [],
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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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}