{
  "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": "2609.39933",
  "title": "ConflictGuide: AutoResearch Improves When Competing Behaviors Are Made Visible",
  "summary": "ConflictGuide replaces single scalar task-performance feedback in LLM-driven AutoResearch loops with a two-stage regime: scalar exploration first, then probe-based feedback on competing model behaviors once gains plateau, with a reusable skill that identifies conflicts and specifies probes for a code agent to implement as metrics. It costs the manual or agent-assisted effort of identifying behavior conflicts and building probe metrics per model, plus additional evaluation compute per proposal, and it adds pipeline complexity in the form of a staged search schedule. It can fail when the chosen competing behaviors or probes are misidentified, when probe thresholds for retaining marginal-gain edits are miscalibrated, or in workloads where no meaningful behavior trade-off exists, in which case Stage II adds overhead without benefit.",
  "rationale": "The method is implementable with a single GPU and API-based code agents, but its benefit depends on correct conflict identification and probe design, so the reader running LLM-assisted model-iteration loops should validate it on their own workload before adopting it as default practice.",
  "technique": "Two-stage AutoResearch with competing-behavior probe feedback",
  "family": "agentes_de_codigo",
  "recommendation": "testar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.39933",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 121,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "ConflictGuide replaces single scalar task-performance feedback in LLM-driven AutoResearch loops with a two-stage regime: scalar exploration first, then probe-based feedback on competing model behaviors once gains plateau, with a reusable skill that identifies conflicts and specifies probes for a code agent to implement as metrics. It costs the manual or agent-assisted effort of identifying behavior conflicts and building probe metrics per model, plus additional evaluation compute per proposal, and it adds pipeline complexity in the form of a staged search schedule. It can fail when the chosen competing behaviors or probes are misidentified, when probe thresholds for retaining marginal-gain edits are miscalibrated, or in workloads where no meaningful behavior trade-off exists, in which case Stage II adds overhead without benefit.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Reduces task and conflict-related errors by up to 28% and 14%, respectively, relative to scalar-only AutoResearch across five model families; these are percentage-point error reductions, not multiplicative factors.",
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      "source_url": "https://arxiv.org/abs/2609.39933",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}