{
  "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": "2610.11552",
  "title": "Safe, Persistent, and Evolving Agent Harness for Understanding Partially Observable Worlds",
  "summary": "Replaces ad-hoc tool-calling agent loops with a structured harness: a code approval layer that checks every action against policy before execution, plus a persistent ledger of verified hidden rules and evidence-backed state that is expanded through abductive diagnosis of execution trajectories. Costs additional LLM calls for trajectory diagnosis, abductive verification interactions, and per-action policy gating, adding latency, API spend, and engineering complexity to maintain the ledger and policy definitions. The abductively hypothesized hidden rules can be wrong or overfit to observed trajectories, so an incorrectly verified ledger rule may silently corrupt future state tracking, and gains are benchmark-specific with no guarantee of transfer to the reader's domain.",
  "rationale": "The approach is API-compatible and requires no training, but its 5-15 point gains are reported on specific enterprise and science benchmarks, so the reader should validate on their own workflows before committing to the added orchestration complexity.",
  "technique": "Policy-gated multi-agent harness with evolving world ledger",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11552",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 132,
  "citations": 0,
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      "statement": "E-Ledger with WorldAbduct improves safe task completion by 5-15 percentage points over the strongest evolution baseline across four LLM backbones on the World of Workflows benchmark.",
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