{
  "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": "2610.01138",
  "title": "Auditing Action Settlement in LLM Agent Environments: Order, Progress, and Replay",
  "summary": "The method replaces ad-hoc or implicit arbitration of concurrent LLM agent actions with a typed snapshot-settlement contract plus a full-state journal for replay verification. It costs implementation complexity in the environment layer and policy-dependent throughput, since conservative settlement sharply reduces task completion. Order sensitivity persists under fixed priorities, priority arbitration can miss the small-instance optimum, and the evidence covers execution semantics only, not human realism or long-run fairness.",
  "rationale": "Teams running concurrent LLM agents on constrained infrastructure can adopt deterministic settlement and replay journaling at negligible compute cost, but the choice of settlement policy must be validated against their own workload because completion rates vary by 59 percentage points across policies.",
  "technique": "Typed snapshot-settlement with journaling for agent actions",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.01138",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 135,
  "citations": 0,
  "claims": [
    {
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      "statement": "Random-ticket settlement completes 90.28% of agents in a six-agent doorway task versus 31.25% for conservative rejection, a paired improvement of 59.03 percentage points (95% bootstrap interval 50.00-68.06); the journal audit replays 156 checkpoints exactly and rejects 1,332 constructed corruptions.",
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    }
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      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "dimension": "reproducibility",
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  ],
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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}