{
  "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": "2610.18745",
  "title": "SolBehave: Evaluating the Behavioural Correctness of LLM-Generated Smart Contracts",
  "summary": "SolBehave replaces compile-only and ad hoc unit-test evaluation of LLM-generated Solidity with three behavioral checks: differential fuzzing against the original contract, historical on-chain transaction replay, and DCR-graph-guided synthetic call-sequence replay. The cost is evaluation infrastructure: a reproducible EVM execution environment, transaction traces, and non-trivial compute and engineering effort to run differential and replay testing at scale. It can fail through incomplete behavioral coverage (fuzzers and DCR graphs miss divergence paths), oracle dependence on the original contract's behavior (which may itself be buggy or diverge in acceptable ways), and limited transfer to settings where no reference implementation or transaction history exists.",
  "rationale": "If the reader ships LLM-generated code in high-stakes or immutable settings, behavioral validation beyond compilation is directly actionable on a single GPU or via APIs, but the benchmark itself is a measurement tool whose full replay pipeline requires workload-specific justification before adoption.",
  "technique": "Behavioral correctness evaluation of LLM-generated smart contracts",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-13",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.18745",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "SolBehave replaces compile-only and ad hoc unit-test evaluation of LLM-generated Solidity with three behavioral checks: differential fuzzing against the original contract, historical on-chain transaction replay, and DCR-graph-guided synthetic call-sequence replay. The cost is evaluation infrastructure: a reproducible EVM execution environment, transaction traces, and non-trivial compute and engineering effort to run differential and replay testing at scale. It can fail through incomplete behavioral coverage (fuzzers and DCR graphs miss divergence paths), oracle dependence on the original contract's behavior (which may itself be buggy or diverge in acceptable ways), and limited transfer to settings where no reference implementation or transaction history exists.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.18745",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
      "source_url": "",
      "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": [],
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  "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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}