Ambiguity-Aware Multi-Agent Framework for Automated Operations Research under Logical Inconsistency

Abstract

Operations research (OR) problems are often described by stakeholders with incomplete knowledge and vague expressions in real-world settings. Such ambiguous descriptions can not be used to formulation directly. Thus, automating OR problems solving requires processing this logical inconsistency in advance. To address this challenge, we propose an \textbf{A}mbiguity-Aware \textbf{M}ulti-Agent Framework for \textbf{A}utomated \textbf{O}R Problem Solving, \textbf{AMAO}, which first addresses logical inconsistency through two-stage alignment before downstream formulation and coding. Specifically, a logical alignment agent with OR-guided experts structure first produces an aligned candidate using supervised routing over variables, parameters, objectives, and constraints. Then, a source-sufficiency verifier determines whether the original description supports the required repairs. When evidence is insufficient, an interactive repair agent requests additional information and revises the description before modeling and coding. In addition, an ambiguity-aware benchmark and its matched dialogue extension are proposed to support evaluation of both stages. The 32B model achieved 86.8\% error-recovery success and 60.4\% solution accuracy, reaching 1.98 and 1.86 times the respective base-model scores. The 8B model achieved 47.9\% solution accuracy, exceeding the best evaluated variant such as DeepSeek-V4-Flash and Claude-sonnet-4.5. Interactive repair further achieved 90.71\% complete-description accuracy when cases requiring clarification. These results support combining context-based repair with evidence assessment and targeted user clarification for automated OR modeling.