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Abstract:Optimization modeling underpins actual-world resolution-making in logistics, manufacturing, power, and public services, but reliably fixing such issues from pure-language necessities remains difficult for present massive language models (LLMs). Agora-Opt allows a number of agent groups to independently produce finish-to-end solutions and reconcile them through an consequence-grounded debate protocol, whereas reminiscence stores solver-verified artifacts and past disagreement resolutions to help coaching-free improvement over time. This design is versatile across each backbones and methods: it reduces base-mannequin lock-in, transfers across completely different LLM families, and may be layered onto current pipelines with minimal coupling. Across public benchmarks, Agora-Opt achieves the strongest general performance among all compared strategies, outperforming strong zero-shot LLMs, training-centric approaches, and prior agentic baselines. Further analyses present robust good points across backbone selections and component variants, and demonstrate that decentralized debate affords a structural advantage over centralized choice by enabling agents to refine candidate solutions via interaction and even get well right formulations when all preliminary candidates are flawed. These results counsel that reliable optimization modeling advantages from combining collaborative cross-checking with reusable experience, and position Agora-Opt as a practical and extensible foundation for reliable optimization modeling assistance. Our code and data can be found at this https URL.

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