MIRROR: A Multi-Agent Framework with Iterative Adaptive Revision and Hierarchical Retrieval for Optimization Modeling in Operations Research
arXiv:2602.03318v3 Announce Type: replace 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 langua
The growing capabilities of LLMs and the increasing demand for automation in complex problem-solving are converging to enable more sophisticated multi-agent AI frameworks.
This development can significantly accelerate the application of operations research in diverse fields by automating complex modeling, making it accessible to non-experts and adapting to novel scenarios.
The process of developing optimization models will shift from a slow, expert-driven task to a more automated and adaptable process, potentially expanding the scope and speed of problem-solving in operations research.
- · Businesses relying on optimization models
- · Operations Research professionals (augmented)
- · AI/ML developers
- · SaaS providers incorporating advanced optimization
- · Traditional manual optimization modeling consultants
- · Organizations slow to adopt AI-driven OR solutions
More efficient and accurate optimization models will be developed across various industries.
This could lead to substantial cost savings and performance improvements in supply chains, logistics, resource allocation, and manufacturing.
The widespread automation of complex modeling might lead to new classes of optimized systems and services, creating novel economic opportunities or disruptions.
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