{
  "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": "2609.31422",
  "title": "Towards Mitigating Fabricated Consensus: The Active Provenance Gate for Multi-Agent Debate Synthesis",
  "summary": "The APG replaces uncontrolled post-debate summarization in multi-agent debate pipelines with a verification layer that audits each claim against the debate log, self-corrects, blocks unsupported claims, and emits divergence reports instead of fabricated consensus. It costs an extra verification and self-correction pass over the debate logs, adding latency, API or compute spend per synthesis, and implementation complexity for claim-level grounding checks. It can fail if the auditing model misjudges provenance, either blocking legitimate consensus or passing subtly unsupported claims, and the evidence comes from crisis simulations whose transfer to the reader's domain is unproven.",
  "rationale": "The method is implementable on constrained infrastructure as an additional LLM verification stage, but its fidelity gains are demonstrated only in crisis simulations, so workload-specific validation is required before production adoption.",
  "technique": "Active Provenance Gate for debate synthesis",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-09-25",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.31422",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 11,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The APG replaces uncontrolled post-debate summarization in multi-agent debate pipelines with a verification layer that audits each claim against the debate log, self-corrects, blocks unsupported claims, and emits divergence reports instead of fabricated consensus. It costs an extra verification and self-correction pass over the debate logs, adding latency, API or compute spend per synthesis, and implementation complexity for claim-level grounding checks. It can fail if the auditing model misjudges provenance, either blocking legitimate consensus or passing subtly unsupported claims, and the evidence comes from crisis simulations whose transfer to the reader's domain is unproven.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.31422",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "The self-healing mechanism more than doubles average Provenance Fidelity in difficult crisis-simulation scenarios; over 75% of human participants preferred an explicit divergence report over fabricated consensus in critical scenarios.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.31422",
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      "source_excerpt": ""
    }
  ],
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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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    }
  ],
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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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}