Generating Robust Portfolios of Optimization Models using Large Language Models
The paper discusses a novel algorithm for generating robust portfolios of optimization models using large language models (LLMs). This approach addresses the challenges of creating reliable optimization models by leveraging the dual capabilities of LLMs as generators and evaluators. The authors provide theoretical and empirical validation of their method, demonstrating its effectiveness across various optimization tasks.
- ▪The proposed algorithm generates a portfolio of optimization models to enhance reliability.
- ▪It utilizes large language models in two roles: as a stochastic generator and as a reasoning evaluator.
- ▪The method ensures that at least one high-quality candidate is included in the portfolio, allowing for informed decision-making.
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Computer Science > Artificial Intelligence arXiv:2605.27013 (cs) [Submitted on 26 May 2026] Title:Generating Robust Portfolios of Optimization Models using Large Language Models Authors:Eleni Straitouri, Cheol Woo Kim, Milind Tambe View a PDF of the paper titled Generating Robust Portfolios of Optimization Models using Large Language Models, by Eleni Straitouri and 2 other authors View PDF HTML (experimental) Abstract:Mathematical optimization is a powerful tool for structured decision-making across domains such as resource allocation and planning. Formulating optimization models faithful to reality, though, remains a significant bottleneck as it typically demands both domain expertise and optimization knowledge that are often scarce.
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