{
  "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": "2610.31461",
  "title": "Adaptive Skill Granularity for Coding Agents: Runtime Routing and Macro-to-Micro Recovery",
  "summary": "The method replaces fixed-granularity skill execution (always primitive tool calls or always macro skills) with a feature-based runtime controller that selects granularity per task and a finite-state executor that falls back to micro-level recovery when a macro skill fails. It costs additional controller logic, state tracking, and process instrumentation, with possible extra token spend when routing chooses primitives or recovery paths; the evaluation uses a deterministic protocol without published latency or dollar figures, so cost impact on a real workload is unverified. It can fail when the routing features misclassify task structure (route mismatch), when macro skills are silently misapplied without triggering recovery, or when the recovery state machine itself contains incorrect transitions.",
  "rationale": "The idea is implementable with a single API-backed coding agent and no training, but the paper offers only process metrics and qualitative robustness claims, so the reader must validate recovery benefit and added token cost on their own workload before production adoption.",
  "technique": "Adaptive skill granularity routing with macro-to-micro recovery",
  "family": "agentes_de_codigo",
  "recommendation": "testar",
  "published": "2026-10-22",
  "as_of": "2026-10-23",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.31461",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 10,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces fixed-granularity skill execution (always primitive tool calls or always macro skills) with a feature-based runtime controller that selects granularity per task and a finite-state executor that falls back to micro-level recovery when a macro skill fails. It costs additional controller logic, state tracking, and process instrumentation, with possible extra token spend when routing chooses primitives or recovery paths; the evaluation uses a deterministic protocol without published latency or dollar figures, so cost impact on a real workload is unverified. It can fail when the routing features misclassify task structure (route mismatch), when macro skills are silently misapplied without triggering recovery, or when the recovery state machine itself contains incorrect transitions.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.31461",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Runtime routing and explicit macro-to-micro recovery improve robustness and recover controlled macro failures that fixed-granularity execution leaves unresolved; no quantitative factor is reported.",
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      "source_url": "https://arxiv.org/abs/2610.31461",
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    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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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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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}