arXiv AI August 5, 2026 By Penglin Zhu, Linhai Zhang, Jungang Xu, Xinchi Wei, Xiuqi Wu

IR2Solve: Structured Intermediate Representations for Cost-Efficient Optimization Autoformulation

arXiv:2608. 02641v1 Announce Type: cross Abstract: Large language models (LLMs) can translate natural-language optimization problems into solver-ready formulations, but direct code generation is brittle: schema, indexing, and semantic errors can cause compilation failures, infeasible models, or incorrect objectives, while iterative repair, search, and multi-agent workflows increase inference cost.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

1d ago

ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization

arXiv:2602. 15983v3 Announce Type: replace-cross Abstract: Large language models (LLMs) can translate natural language into optimization code, but silent failures pose a critical risk: code that executes and returns solver-feasible solutions may encode semantically incorrect formulations---a feasibility--correctness gap reaching 90 percentage points on compositional problems.

By Junbo Jacob Lian, Yujun Sun, Huiling Chen, Chaoyu Zhang, Hanzhang Qin, Chung-Piaw Teo

llms fine-tuning benchmarks

arXiv AI

Jun 16

Large Language Models as Optimizers: A Survey of Direct vs. Tool-Augmented Approaches and Their Performance Frontiers

arXiv:2606. 15577v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly involved in complex mathematical optimization, even if the pragmatic user who triggers them is unaware of it.

By Roko Peran, Luka Hobor, Mihael Kovac, Mario Brcic

llms benchmarks

arXiv AI

Jul 22

PEARL: Solver-in-the-Loop Interactive Optimization Modeling from Natural Language

arXiv:2607. 18256v1 Announce Type: new Abstract: Optimization modeling is the process of translating real-world decision problems, often described in natural language, into formal mathematical formulations and executable solver code.

By Hongliang Lu, Zhong Li, Yuxuan Chen, Yuan Lan, Fan Zhang, Zaiwen Wen

llms benchmarks

arXiv AI

Jun 30

OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale

arXiv:2407. 19633v4 Announce Type: replace Abstract: Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare.

By Ali AhmadiTeshnizi, Wenzhi Gao, Herman Brunborg, Shayan Talaei, Connor Lawless, Madeleine Udell

llms fine-tuning benchmarks

arXiv Machine Learning

Aug 5

BBOWP-Bench: Evaluating LLMs on Black-Box Optimization Word Problems

arXiv:2608. 02612v1 Announce Type: cross Abstract: Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise.

By Yutaro Yamada, Kei Hiroshima, Nozomu Yoshinari, Kento Uchida, Shinichi Shirakawa

llms benchmarks

arXiv AI

Jul 7

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving

arXiv:2509. 08269v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly integrated with evolutionary computation to support optimization tasks.

By Yisong Zhang, Ran Cheng, Guoxing Yi, Kay Chen Tan

llms agents benchmarks

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ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization

Large Language Models as Optimizers: A Survey of Direct vs. Tool-Augmented Approaches and Their Performance Frontiers

PEARL: Solver-in-the-Loop Interactive Optimization Modeling from Natural Language

OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale

BBOWP-Bench: Evaluating LLMs on Black-Box Optimization Word Problems

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving