{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per task.",
    "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": "2609.35357",
  "title": "Do Coding Agents Reuse Existing Code or Reinvent the Wheel?",
  "summary": "It replaces evaluation of coding agents based solely on pass rates with a multi-turn benchmark (RepoReuse) that measures reuse rate, recall, and cross-turn structural redundancy in real repositories. The cost is integrating its AST-based pipeline and execution-verified task synthesis into your evaluation harness, plus the compute of running multi-turn agent rollouts; the fully automated construction keeps this feasible on one GPU via API models, but adds evaluation latency and complexity. It can fail if synthesized tasks diverge from your real workload, if AST dependency analysis misses non-structural duplication, or if pass-rate pressure leads teams to ignore the redundancy signals the benchmark surfaces.",
  "rationale": "A small team deploying coding agents in production can adopt this evaluation practice with API-only models to detect duplicated logic that pass rates conceal, reducing maintenance debt before it accumulates unaudited.",
  "technique": "Multi-turn benchmark for code-reuse auditing",
  "family": "avaliacao_de_agente",
  "recommendation": "adotar",
  "published": "2026-09-28",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.35357",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "It replaces evaluation of coding agents based solely on pass rates with a multi-turn benchmark (RepoReuse) that measures reuse rate, recall, and cross-turn structural redundancy in real repositories. The cost is integrating its AST-based pipeline and execution-verified task synthesis into your evaluation harness, plus the compute of running multi-turn agent rollouts; the fully automated construction keeps this feasible on one GPU via API models, but adds evaluation latency and complexity. It can fail if synthesized tasks diverge from your real workload, if AST dependency analysis misses non-structural duplication, or if pass-rate pressure leads teams to ignore the redundancy signals the benchmark surfaces.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.35357",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "An audit over 3,000 turns finds agents leave duplicated logic in 50.8% of task chains by turn 5 while pass rates barely move, showing reuse failures are invisible to functional metrics.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.35357",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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      "dimension": "compute",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "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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}