{
  "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": "2610.13152",
  "title": "Quality, Not Just Outcome: A Structural Scoring Framework for Selecting Coding-Agent Trajectories for Supervised Fine-Tuning",
  "summary": "TQS replaces outcome-only filtering and LLM-judge-based selection of coding-agent SFT trajectories with a linear-time structural score measuring information focus, intent-action alignment, and policy dynamics. It costs only the scoring pass over an existing trajectory corpus, but presupposes that the reader has or can generate a large pool of successful trajectories and the budget to run SFT experiments. It can fail if the structural heuristics do not transfer to the reader's agent framework, tool schema, or task distribution, since the ranking signal was validated on one public corpus and one benchmark.",
  "rationale": "The method is cheap and judge-free, making it a plausible data-selection upgrade, but its value depends on having a suitable trajectory corpus and validating that the structural signals generalize to the reader's own agent setup.",
  "technique": "Structural trajectory quality scoring for SFT data selection",
  "family": "agentes_de_codigo",
  "recommendation": "testar",
  "published": "2026-10-09",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.13152",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 132,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "TQS replaces outcome-only filtering and LLM-judge-based selection of coding-agent SFT trajectories with a linear-time structural score measuring information focus, intent-action alignment, and policy dynamics. It costs only the scoring pass over an existing trajectory corpus, but presupposes that the reader has or can generate a large pool of successful trajectories and the budget to run SFT experiments. It can fail if the structural heuristics do not transfer to the reader's agent framework, tool schema, or task distribution, since the ranking signal was validated on one public corpus and one benchmark.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.13152",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Models fine-tuned on top-TQS-ranked trajectories consistently outperform those trained on random or bottom-ranked subsets on SWE-Bench-Verified; no numeric margin is reported in the abstract.",
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      "source_url": "https://arxiv.org/abs/2610.13152",
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    }
  ],
  "exposure_map": [
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      "dimension": "compute",
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    },
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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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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      "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": ""
}