{
  "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": "2610.06192",
  "title": "Copies or Sources? Measuring How LLM Aggregators Count Restated Evidence in Multi-Agent Systems",
  "summary": "It replaces naive message-pooling aggregation, in which restated or forwarded observations are implicitly counted as independent evidence, with an explicit prompt-level rule that agents reference readings rather than restate them, optionally plus a declaration of what a copy contributes. The cost is negligible: a short instruction added to agent prompts and orchestration, with no extra model calls, memory, or infrastructure, though a copy-declaration paragraph may not fully eliminate overcounting on its own in all settings. It can fail when references are dropped, when agents misidentify which observation a statement refers to, or when genuinely corroborating reports are mistakenly deduplicated, and the measured gains may vary across models and communication protocols.",
  "rationale": "It is a zero-infrastructure prompt and protocol change that directly reduces a documented overcounting failure in multi-agent aggregation, making it immediately adoptable on a constrained single-GPU or API-based setup.",
  "technique": "Refer-instead-of-restate rule for multi-agent evidence aggregation",
  "family": "orquestracao_multiagente",
  "recommendation": "adotar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06192",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "It replaces naive message-pooling aggregation, in which restated or forwarded observations are implicitly counted as independent evidence, with an explicit prompt-level rule that agents reference readings rather than restate them, optionally plus a declaration of what a copy contributes. The cost is negligible: a short instruction added to agent prompts and orchestration, with no extra model calls, memory, or infrastructure, though a copy-declaration paragraph may not fully eliminate overcounting on its own in all settings. It can fail when references are dropped, when agents misidentify which observation a statement refers to, or when genuinely corroborating reports are mistakenly deduplicated, and the measured gains may vary across models and communication protocols.",
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      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.06192",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "A rule that has agents refer to readings instead of restating them cuts belief-implied early commitment from 11.2% to 1.1% while preserving genuine corroboration; a one-paragraph copy-contribution declaration brings copy weight on controlled logs to 0.08 or less.",
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      "source_url": "https://arxiv.org/abs/2610.06192",
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      "source_excerpt": ""
    }
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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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    {
      "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",
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}