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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.27013 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | SokyB_Fug8Em |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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