# When Large Language Model Meets Optimization

arXiv: https://arxiv.org/abs/2405.10098 · HTML: https://mlsanity.com/paper/2405.10098/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2024.

## Built on by (most cited first, 22 of 22)

- [A Systematic Survey on Large Language Models for Algorithm Design](https://mlsanity.com/paper/2410.14716/) (2024)
- [Large Language Models for Combinatorial Optimization: A Systematic Review](https://mlsanity.com/paper/2507.03637/) (2025)
- [A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving](https://mlsanity.com/paper/2509.08269/) (2025)
- [Decision Information Meets Large Language Models: The Future of Explainable Operations Research](https://mlsanity.com/paper/2502.09994/) (2025)
- [EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulations](https://mlsanity.com/paper/2502.14760/) (2025)
- [Optimizing Prompt Sequences using Monte Carlo Tree Search for LLM-Based Optimization](https://mlsanity.com/paper/2508.05995/) (2025)
- [EvoCut: Strengthening Integer Programs via Evolution-Guided Language Models](https://mlsanity.com/paper/2508.11850/) (2025)
- [SAC-Opt: Semantic Anchors for Iterative Correction in Optimization Modeling](https://mlsanity.com/paper/2510.05115/) (2025)
- [Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms](https://mlsanity.com/paper/2503.10968/) (2025)
- [Large Language Models in Operations Research: Methods, Applications, and Challenges](https://mlsanity.com/paper/2509.18180/) (2025)
- [Cooperative Design Optimization through Natural Language Interaction](https://mlsanity.com/paper/2508.16077/) (2025)
- [Refining Hybrid Genetic Search for CVRP via Reinforcement Learning-Finetuned LLM](https://mlsanity.com/paper/2510.11121/) (2025)
- [REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models](https://mlsanity.com/paper/2506.07759/) (2025)
- [Prior Knowledge or Search? A Study of LLM Agents in Hardware-Aware Code Optimization](https://mlsanity.com/paper/2605.19782/) (2026)
- [Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows](https://mlsanity.com/paper/2505.04354/) (2025)
- [Instruction Learning Paradigms: A Dual Perspective on White-box and Black-box LLMs](https://mlsanity.com/paper/2506.21573/) (2025)
- [AgenticPruner: MAC-Constrained Neural Network Compression via LLM-Driven Strategy Search](https://mlsanity.com/paper/2601.12272/) (2026)
- [Enhancing Mathematical Problem Solving in LLMs through Execution-Driven Reasoning Augmentation](https://mlsanity.com/paper/2602.03950/) (2026)
- [CHAL: Council of Hierarchical Agentic Language](https://mlsanity.com/paper/2605.12718/) (2026)
- [A novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images](https://mlsanity.com/paper/2605.19595/) (2026)
- [LP Mining with LP2Graph: A Use Case for Railway Rescheduling](https://mlsanity.com/paper/2607.11980/) (2026)
- [LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications](https://mlsanity.com/paper/2607.18147/) (2026)

## Abstract (arXiv)

Optimization algorithms and large language models (LLMs) enhance decision-making in dynamic environments by integrating artificial intelligence with traditional techniques. LLMs, with extensive domain knowledge, facilitate intelligent modeling and strategic decision-making in optimization, while optimization algorithms refine LLM architectures and output quality. This synergy offers novel approaches for advancing general AI, addressing both the computational challenges of complex problems and the application of LLMs in practical scenarios. This review outlines the progress and potential of combining LLMs with optimization algorithms, providing insights for future research directions.