OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement
Adriana Laurindo Monteiro, Nayse Fagundes, Gabriel Mattos Langeloh, Gustavo de Oliveira Kanno, Priscila Louise Aguirre, Thiago Costa Rizuti da Rocha, Victor Leme Beltran
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Key claim
OptiAgent automates optimization problem formulation from natural language.
In plain English
Operations Research problems often require complex mathematical formulations that can be difficult to generate from natural language descriptions. Current methods may struggle with misinterpretation and structural defects, leading to inefficient solutions. OptiAgent addresses these issues by using dedicated agents that extract key structures and provide iterative self-correction, improving both accuracy and transparency. Builders might find this framework useful for automating and refining the optimization process, ultimately saving time and resources.
The introduction of a multi-agent framework for mathematical modeling is a significant advancement.
The framework demonstrates strong performance across multiple benchmarks, indicating solid reliability.
Deep reliability assessment
The methodology supports the claim that OptiAgent can generate solver-ready mathematical formulations from natural language descriptions, but the paper may overclaim its generalizability across all types of optimization problems without extensive testing on diverse datasets.
Reproducibility
Yes, the datasets used are available at https://github.com/ZJU-TSELab/ORThought.
Key figure
Figure 1 likely illustrates the multi-agent architecture of OptiAgent, highlighting the feedback loops and agent interactions for optimization modeling.