{
  "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": "2610.30284",
  "title": "SWE-VERIFIER: Selective Verification for Cost-Efficient Coding Agents",
  "summary": "Replaces always-on candidate sampling and per-step verification (or unverified single-action execution) with a lightweight 4B verifier that triggers sampling only on low-confidence steps and ranks candidate actions before execution. Costs training a small verifier model plus a modest inference overhead of roughly 1.1x API cost, and adds a preference-data training pipeline as integration complexity. Can fail if the verifier miscalibrates on out-of-distribution agent policies or task types, since gains are demonstrated only on the mini-swe-agent harness with two specific models and generalization to other agents is not guaranteed.",
  "rationale": "The absolute resolution gain is modest and validated on a specific harness and model pair, so a workload-specific pilot on the reader's own coding-agent setup is warranted before production adoption.",
  "technique": "Selective action verification for coding agents",
  "family": "agentes_de_codigo",
  "recommendation": "testar",
  "published": "2026-10-21",
  "as_of": "2026-10-25",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30284",
  "independent_implementations": 3,
  "total_implementations": 4,
  "stars": 34,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
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    {
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      "statement": "Improves resolution from 75.47% to 77.20% on MiniMax-M2.7 at 1.09x cost, and from 72.60% to 74.27% on DeepSeek-V4-Flash at 1.13x cost, verifying fewer than 4% of steps; gains are absolute point improvements, not a multiplicative factor.",
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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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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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      "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": "",
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  "equations_source_sha256": "",
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}