{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your coding assistant makes changes that still need substantial repair before merging.",
    "change": "Try the technique on held-out issues from a repository with reliable tests.",
    "baseline": "The same coding agent, repository snapshot, tools, and token 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": "Correct patches, regression failures, human repair time, and total cost per accepted change.",
    "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": "2610.30114",
  "title": "The End of Consensus Research?",
  "summary": "The method replaces manual expert-driven consensus protocol design and evaluation with an agent harness combining a protocol knowledge base, a modular implementation skeleton, and Lean-based formal verification. It costs substantial domain-specific scaffolding (knowledge base, reusable Lean specifications and proofs, evaluation infrastructure) plus expert oversight, rather than GPU or inference budget. The discovered protocol's gains apply only to geo-replicated state machines, and the paper itself shows agents can surface but also miss correctness issues, since its Lean formalization exposed previously unreported vulnerabilities in existing protocols.",
  "rationale": "The reader runs ML inference workloads, not geo-replicated state machines, so the protocol result is inapplicable; only the domain-specific agent harness pattern is worth monitoring as a template for agentic engineering work.",
  "technique": "Agent harness for distributed consensus discovery",
  "family": "agentes_de_codigo",
  "recommendation": "observar",
  "published": "2026-10-21",
  "as_of": "2026-10-22",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30114",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
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      "statement": "A-greement reduces mean latency of a geo-distributed key-value store by 12.2% (write-heavy) and 33.3% (read-heavy) over the fastest baseline, averaged across six 5- or 9-replica deployments; it matches the baseline on read-only workloads.",
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      "dimension": "compatibility",
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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}