MIRROR: A Multi-Agent Framework with Iterative Adaptive Revision and Hierarchical Retrieval for Optimization Modeling in Operations Research

• URL: http://arxiv.org/abs/2602.03318v2
• Date: Wed, 04 Feb 2026 08:04:51 GMT
• Title: MIRROR: A Multi-Agent Framework with Iterative Adaptive Revision and Hierarchical Retrieval for Optimization Modeling in Operations Research
• Authors: Yifan Shi, Jialong Shi, Jiayi Wang, Ye Fan, Jianyong Sun,
• Abstract summary: MIRROR is a fine-tuning-free, end-to-end multi-agent framework for operations research.<n>It translates natural language optimization problems into mathematical models and solver code.<n>Experiments show that MIRROR outperforms existing methods on standard Operations Research benchmarks.
• Score: 15.28095645151852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Operations Research (OR) relies on expert-driven modeling-a slow and fragile process ill-suited to novel scenarios. While large language models (LLMs) can automatically translate natural language into optimization models, existing approaches either rely on costly post-training or employ multi-agent frameworks, yet most still lack reliable collaborative error correction and task-specific retrieval, often leading to incorrect outputs. We propose MIRROR, a fine-tuning-free, end-to-end multi-agent framework that directly translates natural language optimization problems into mathematical models and solver code. MIRROR integrates two core mechanisms: (1) execution-driven iterative adaptive revision for automatic error correction, and (2) hierarchical retrieval to fetch relevant modeling and coding exemplars from a carefully curated exemplar library. Experiments show that MIRROR outperforms existing methods on standard OR benchmarks, with notable results on complex industrial datasets such as IndustryOR and Mamo-ComplexLP. By combining precise external knowledge infusion with systematic error correction, MIRROR provides non-expert users with an efficient and reliable OR modeling solution, overcoming the fundamental limitations of general-purpose LLMs in expert optimization tasks.

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