{
  "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": "2610.20068",
  "title": "The Code of Theseus: Can Agents Rebuild Software from Recovered Design Documents?",
  "summary": "The method replaces manually written or absent requirements and architecture documents with artefacts recovered automatically from a static dependency graph and a few key files, avoiding the context-saturating full-code reading of prior approaches. Cost is one offline generation pass per repository plus judge-based validation; the recovered artefacts improved structural fidelity but produced no significant gain in test pass rate, so the functional benefit is unproven. It can fail because an LLM judge rated the artefacts less reliably correct and at the right abstraction level, meaning misleading design documents could steer agents toward plausible but wrong structures.",
  "rationale": "The pipeline runs with a single LLM and static analysis at small scale, but its benefit is limited to structural similarity rather than correctness, so the reader should validate on their own repositories before relying on recovered design artefacts in production agent workflows.",
  "technique": "Design-document recovery from code to guide coding agents",
  "family": "agentes_de_codigo",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20068",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces manually written or absent requirements and architecture documents with artefacts recovered automatically from a static dependency graph and a few key files, avoiding the context-saturating full-code reading of prior approaches. Cost is one offline generation pass per repository plus judge-based validation; the recovered artefacts improved structural fidelity but produced no significant gain in test pass rate, so the functional benefit is unproven. It can fail because an LLM judge rated the artefacts less reliably correct and at the right abstraction level, meaning misleading design documents could steer agents toward plausible but wrong structures.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.20068",
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      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Recovered requirements and architecture raise module-structure F1 from 0.54 to 0.63 when an agent rebuilds 29 libraries, while held-out test pass rate is unchanged (33.7% vs 35.8%).",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2610.20068",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
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      "dimension": "compute",
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    },
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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": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}