Minbo Ma, Honglin Zhang, Bangtao Zhou, Chunguang Yu, Dasheng Ju, Yichen Shen, Fengli Xu, Yong Li · 2026 · arXiv
DOI: 10.48550/arXiv.2610.24329 · open-access PDF
논리, 코딩 및 도구 사용의 진보는 LLM 에이전트가 반복적 인 문제 모델링, 프로그램 구축 및 검증을 통해 운영 연구 (또는) 문제를 해결할 수있게 해줍니다.그러나 자체 작성 검증을 통과하면 솔루션이 작업 제한을 충족시키고 최적화 품질을 충족시키는지 보장하지 않습니다.이 격차는 관련 사양 오류로 인해 발생할 수 있으며, 생성 된 프로그램과 검사가 작업 요구 사항의 부재 또는 잘못 해석을 공유하며, 불완전한 검증 커버 및 효과적이지 않은 최적화 전략을 제안합니다.
Machine translation (Meta M2M100); the English original follows.
Advances in reasoning, coding, and tool use enable LLM agents to solve operations research (OR) problems through iterative problem modeling, program construction, and verification. Yet passing self-authored verification does not ensure that the solutions satisfy task constraints and meet optimization quality. This gap can arise from correlated specification errors, where the generated program and its checks share omissions or misinterpretations of task requirements, alongside incomplete verification coverage and ineffective optimization strategies. We propose VERAS, a verification-guided adapt
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