{ "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": "2610.29143", "title": "VeeReview: Measuring Build-Verify Pairs in Agent-Authored Pull Requests from a V-Model Perspective", "summary": "VeeReview replaces informal human judgement of whether a pull request's verification evidence matches its proposed change with a hybrid of deterministic repository signals, one rule over the diff and tree, and a language-model judge for the remaining pairs. The cost is building repository-signal extraction plus LLM judging per pull request, and the authors themselves show the instrument did not meet its calibration target: agreement with adjudicated labels was only 4% to 60% against a required 80%, and inter-rater agreement was weak (kappa = 0.154). What can fail is exactly what the paper demonstrates: the model-judged pairs partly measure how a pull request is presented rather than the verification work performed, so adopting it for gating or comparing agent versus human output would encode an unvalidated measurement.", "rationale": "The paper's own results show the instrument fails its preregistered calibration criterion, so it is relevant to monitor as evaluation methodology but not ready to adopt for production comparisons of agent-authored and human-authored changes.", "technique": "V-Model build-verify pair classification of pull requests", "family": "avaliacao_de_agente", "recommendation": "observar", "published": "2026-10-21", "as_of": "2026-10-25", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2610.29143", "independent_implementations": 0, "total_implementations": 1, "stars": 0, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "VeeReview replaces informal human judgement of whether a pull request's verification evidence matches its proposed change with a hybrid of deterministic repository signals, one rule over the diff and tree, and a language-model judge for the remaining pairs. 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