{ "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": "2610.11963", "title": "Can LLMs Fix It Without Code? Toward Automated Verification of No-Code Bug Fixes", "summary": "The method replaces manual developer verification of LLM-proposed no-code bug fixes (configuration changes, version updates, workflow adjustments) with an automated pipeline in which a computer-use executor agent applies the fix in a real browser and an issue-specific checker confirms resolution. The cost is substantial environment setup (only 17.6% of candidate issues passed both sanity gates), executor API or large-model inference expense, and per-issue checker engineering. It can fail because verdicts depend strongly on the chosen executor (only 46.9% three-way agreement), and agreement with human consensus ranged from 66.1% to 88.1%, so an unreliable executor can silently mislabel fixes as resolved.", "rationale": "If the reader ships LLM-generated support answers or fix suggestions, execution-based gating before user delivery is directly applicable via API executors, but the high executor sensitivity means the pipeline and its verdict reliability must be validated on their own issue distribution before production trust.", "technique": "Execution-based verification of LLM no-code fixes", "family": "avaliacao_de_agente", "recommendation": "testar", "published": "2026-10-08", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2610.11963", "independent_implementations": 0, "total_implementations": 0, "stars": 0, "citations": null, "claims": [ { "claim_id": "claim-01", "statement": "The method replaces manual developer verification of LLM-proposed no-code bug fixes (configuration changes, version updates, workflow adjustments) with an automated pipeline in which a computer-use executor agent applies the fix in a real browser and an issue-specific checker confirms resolution. The cost is substantial environment setup (only 17.6% of candidate issues passed both sanity gates), executor API or large-model inference expense, and per-issue checker engineering. It can fail because verdicts depend strongly on the chosen executor (only 46.9% three-way agreement), and agreement with human consensus ranged from 66.1% to 88.1%, so an unreliable executor can silently mislabel fixes as resolved.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.11963", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "Only 14.6% to 49.7% of 322 LLM-generated no-code fixes resolved the bug depending on the executor; the best configuration reached 74.1% under Claude Sonnet 5, and swapping executors shifted resolution rates by 38.8% on average.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.11963", "source_page": null, "source_excerpt": "" } ], "exposure_map": [ { "dimension": "quality", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compute", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "latency", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "operations", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compatibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "security", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "data_and_training", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "reproducibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" } ], "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": "" }