The fusion of Large Language Models (LLMs) and Operations Research (OR) is transforming how optimization problems are understood, modeled, and solved. This repository provides a curated collection of cutting-edge research that showcases this evolution.

We track the latest papers, code, and resources demonstrating how LLMs are used to:

• Interpret and Analyze: Make complex optimization models more understandable and interactive.
• Automate Formulation: Translate natural language descriptions directly into solvable mathematical models.
• Guide Solution Search: Enhance the performance of solvers by generating heuristics, cuts, or strategies.

We also include surveys and vision papers that provide comprehensive overviews and future directions in this exciting intersection of AI and OR.

[2025/06] EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulations, ICML 2025 Workshop. [arXiv] [dataset] [official code]

• This study introduces EquivaMap, an LLM-driven framework that leverages a formal definition called "Quasi-Karp Equivalence" to automatically verify the equivalence of optimization problem formulations. EquivaMap achieved 100% accuracy across diverse equivalent transformations, significantly outperforming existing heuristic methods on the newly developed EquivaFormulation dataset.

[2025/02] Evaluating LLM Reasoning in the Operations Research Domain with ORQA, AAAI 2025. [arXiv] [dataset]

• The study introduces ORQA, a benchmark evaluating LLMs on Operations Research tasks requiring multistep reasoning. Testing models like LLaMA 3.1 and Mixtral reveals limited performance, highlighting LLMs' challenges in specialized domains.

[2025/01] Decision information meets large language models: The future of explainable operations research, ICLR 2025. [OpenReview] [official code] [dataset]

• This study present EOR, a novel framework that addresses transparency and interpretability challenges in OR. It introduces the concept of “Decision Information” through what-if analysis and use bipartite graphs to quantify changes in OR models.
• Establish a industrial benchmark for evaluating explanation quality in OR.

[2025/01] OptiChat: Bridging Optimization Models and Practitioners with Large Language Models, preprint. [arXiv] [official code]

• This study introduces OptiChat, a natural language dialogue system that empowers non-expert practitioners to interpret, analyze, and interact with complex optimization models by augmenting a large language model with specialized functional calls and code generation.

[2024/05] Towards Human-aligned Evaluation for Linear Programming Word Problems, LREC-COLING 2024. [paper]

• This study introduces a novel metric based on graph edit distance to more accurately evaluate LLM-generated solutions for linear programming word problems (LPWPs) by correctly identifying mathematically equivalent answers.

[2023/07] Large Language Models for Supply Chain Optimization, preprint. [arXiv] [official code]

• This study presents OptiGuide, a framework that leverages large language models (LLMs) to enable supply chain optimization with what-if analysis.

[2025/10] SolverLLM: Leveraging Test-Time Scaling for Optimization Problem via LLM-Guided Search, NeurIPS 2025. [OpenReview] [arXiv]

• This study introduces SolverLLM, a training-free framework that solves optimization problems by enhancing Monte Carlo Tree Search (MCTS) with dynamic expansion for incremental formulation, prompt backpropagation to guide exploration using solver feedback, and uncertainty backpropagation to weight decision reliability based on reward variance.

[2025/10] OptiTree: Hierarchical Thoughts Generation with Tree Search for LLM Optimization Modeling, NeurIPS 2025. [OpenReview] [arXiv] [official code]

• This study introduces OptiTree, a framework that overcomes the limitations of rigid, fixed-step prompting in optimization modeling. It introduces a hierarchical modeling tree that organizes operations research problems by complexity, allowing the system to adaptively decompose new, complex problems into simpler, standard subproblems via tree search. By retrieving and synthesizing structured "modeling thoughts" from these identified subproblems, OptiTree effectively transfers established domain knowledge to novel tasks, achieving over 10% improvement in modeling accuracy on challenging benchmarks.

[2025/10] AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library, preprint. [arXiv] [official code]

• This study presents AlphaOPT, a self-improving experience library framework that enables LLMs to learn optimization problem formulation from answer-only supervision through iteratively building and refining structured, solver-verified insights, achieving continual improvement without model retraining and outperforming the strongest baseline by 7.7% on out-of-distribution benchmarks.

[2025/09] LLM-OptiRA: LLM-Driven Optimization of Resource Allocation for Non-Convex Problems in Wireless Communications, preprint. [arXiv] [official code]

• Proposes the first LLM pipeline that automatically identifies and reformulates non-convex components in wireless resource allocation tasks, enabling end-to-end solving. Integrates solver-in-the-loop error correction and feasibility validation, reaching a 96% execution rate and 80% success rate with GPT-4, outperforming heuristic baselines.

[2025/08] RideAgent: An LLM-Enhanced Optimization Framework for Automated Taxi Fleet Operations, preprint. [arXiv]

• RideAgent is an LLM-enhanced framework for electric taxi fleet optimization that automatically converts natural language objectives into mathematical models and accelerates computation by intelligently fixing low-sensitivity variables based on historical data. Using Random Forest and Mixed-Integer Programming, it achieves 86% objective generation accuracy and reduces solution time by 53% with minimal optimality loss (2.42%) on NYC taxi data.

[2025/08] Guiding Large Language Models in Modeling Optimization Problems via Question Partitioning, IJCAI 2025. [paper]

• This study proposes PaMOP, a new framework that uses LLMs to automatically create mathematical models for optimization problems from natural language descriptions. The framework breaks down large problems into smaller parts using a tree structure, guiding the LLM to model each part separately with self-augmented prompts, and then iteratively corrects the final model.

[2025/08] Automated Optimization Modeling through Expert-Guided Large Language Model Reasoning, preprint. [arXiv] [dataset] [official code]

• This study enhances existing dataset annotations and introduces a new dataset LogiOR. The authors propose ORThought, a framework that automates optimization modeling through expert-guided chain-of-thought reasoning, validated by extensive empirical evaluation and systematic analysis.

[2025/07] ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research, ACL 2025. [paper] [official code]

• This study introduces ORMind, a cognitive-inspired structured workflow that enhances optimization through counterfactual reasoning.

[2025/06] LLM for Large-Scale Optimization Model Auto-Formulation: A Lightweight Few-Shot Learning Approach, preprint. [paper]

• This study introduces LEAN-LLM-OPT, a novel framework that uses few-shot learning to guide a team of LLM agents in automatically creating state-of-the-art formulations for large-scale optimization problems from a user's query.

[2025/05] OptimAI: Optimization from Natural Language Using LLM-Powered AI Agents, preprint. [arXiv] [presentation] [slides]

• This study introduces OptimAI, a multi-agent collaborative framework powered by LLMs. Its core strategy is "plan-before-code," where multiple solution strategies are formulated upfront, and a UCB algorithm is used to dynamically switch to the most promising path during debugging. The framework also demonstrates a synergistic effect by integrating heterogeneous LLMs in specialized roles, leading to significant gains in productivity and success rates for solving complex optimization problems.

[2025/05] Autoformulation of Mathematical Optimization Models Using LLMs, ICML 2025. [OpenReview] [official code]

• This study introduces a novel method that combines Large Language Models (LLMs) with Monte-Carlo Tree Search to automatically create optimization models from natural language, using techniques like symbolic pruning and LLM-guided evaluation to efficiently explore and generate correct formulations.

[2025/01] DRoC: Elevating Large Language Models for Complex Vehicle Routing via Decomposed Retrieval of Constraints, ICML 2025. [OpenReview] [official code] [slides] [poster]

• This study proposes Decomposed Retrieval of Constraints (DRoC), a novel framework aimed at enhancing large language models (LLMs) in exploiting solvers to tackle vehicle routing problems (VRPs) with intricate constraints.

[2024/10] CAFA: Coding as Auto-Formulation Can Boost Large Language Models in Solving Linear Programming Problem, NeurIPS 2024 Workshop MATH-AI. [OpenReview] [official code]

• This study introduces CAFA, a compact prompt guiding the LLMs to formalize the given problem text into lines of code.

[2024/05] OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models, ICML 2024. [arXiv] [official code] [dataset]

• This study introduces OptiMUS, a Large Language Model (LLM)-based agent designed to formulate and solve (mixed integer) linear programming problems from their natural language descriptions. OptiMUS utilizes a modular structure to process problems, allowing it to handle problems with long descriptions and complex data without long prompts.

[2024/01] Chain-of-Experts: When LLMs Meet Complex Operation Research Problems, ICLR 2024. [OpenReview] [official code] [dataset] [poster]

• This study introduces Chain-of-Experts (CoE), a multi-agent LLM framework that boosts reasoning in complex operation research problems by integrating domain-specific agents under a conductor's guidance and reflection mechanism.

[2025/10] MURKA: Multi-Reward Reinforcement Learning with Knowledge Alignment for Optimization Tasks, NeurIPS 2025. [OpenReview]

• This study introduces MURKA, a collaborative agent framework (Extractor, Solver, Checker). It optimizes the Extractor using GRPO with a composite reward, enhances Solver's AMPL code generation via knowledge distillation, and ensures feasibility through iterative verification. MURKA achieves SOTA performance, improving solution accuracy by 5.9% and execution success rate by 5.1% across eight benchmarks.

[2025/09] OptiMind: Teaching LLMs to Think Like Optimization Experts, preprint. [arXiv]

• This work enhances MILP formulation accuracy by integrating optimization expertise into LLM training and inference. Training data is cleaned through class-based error analysis, and multi-turn inference strategies are introduced, guided by error summaries and solver feedback. Evaluation across multiple models shows this approach improves formulation accuracy by 14 percentage points on average, advancing robust LLM-assisted optimization.

[2025/09] StepORLM: A Self-Evolving Framework With Generative Process Supervision For Operations Research Language Models, preprint. [arXiv] [official code]

• This study introduces StepORLM, a self-evolving framework for training LLMs on Operations Research problems, where a policy model and a Generative Process Reward Model (GenPRM) co-evolve through dual feedback: external solver verification (outcome) and holistic trajectory evaluation (process). Using Weighted DPO, the 8B model achieves SOTA results across six benchmarks, outperforming much larger models.

[2025/07] BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving, ACL 2025. [paper] [official code] [dataset]

• This study proposes a new algorithm, BPP-Search, designed to enhance the ability to solve mathematical modeling problems by improving the Tree-of-Thought (ToT) framework. The paper first introduces a new dataset, StructuredOR, which provides detailed annotations of the modeling process.

[2025/07] Step-Opt: Boosting Optimization Modeling in LLMs through Iterative Data Synthesis and Structured Validation, preprint. [arXiv] [official code]

• This study introduces the Step-Opt framework, which enhances LLMs’ ability to generate accurate mathematical optimization models from natural language. By combining an iterative data synthesis process with a rigorous stepwise validation mechanism, Step-Opt enables fine-tuned LLMs to achieve SOTA performance on various benchmarks — especially for complex problem instances.

[2025/07] Auto-Formulating Dynamic Programming Problems with Large Language Models, preprint. [arXiv] [slides]

• This study presents DPLM, a state-of-the-art specialized model for dynamic programming, which is enabled by the novel DualReflect synthetic data pipeline and validated on DP-Bench, the first comprehensive benchmark for this task.

[2025/05] OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling, ICLR 2025. [OpenReview] [arXiv] [official code] [slides] [poster]

• This study introduces OPTIBENCH, a new benchmark designed to evaluate LLMs on realistic optimization problems, including non-linear and tabular data scenarios. It also proposes ReSocratic, a reverse data synthesis method that significantly improves the performance of open-source LLMs, raising Llama-3-8B-Instruct's accuracy on the benchmark by 37.5% to 51.1% and enabling it to outperform larger models in specific problem categories.

[2025/05] Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling, NeurIPS 2025. OpenReview [arXiv] [official code] [slides]

• This study introduces Solver-Informed Reinforcement Learning (SIRL), a framework that enhances LLM accuracy in mathematical optimization modeling by using professional solvers (like Gurobi) as verifiers to provide high-quality reward signals. This enables LLMs to generate syntactically correct, mathematically feasible optimization code, effectively addressing hallucination and error issues in this domain.

[2025/05] ORLM: A customizable framework in training large models for automated optimization modeling, Operations Research. [arXiv] [official code] [model]

• This study proposes a pathway for training open-source LLMs to automate optimization modeling and solving. It introduces OR-INSTRUCT, a semi-automated data synthesis framework for optimization modeling that allows for customizable enhancements tailored to specific scenarios or model types. Additionally, the research presents IndustryOR, an industrial benchmark designed to evaluate the performance of LLMs in solving practical OR problems.

[2025/03] LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch, ICLR 2025. [OpenReview] [official code]

• LLMOPT introduces a unified framework that effectively bridges natural language optimization problems to mathematical formulations, significantly improving solving accuracy across diverse optimization types.

[2025/09] LLMs for Cold-Start Cutting Plane Separator Configuration, CPAIOR 2025. [arXiv]

• This study introduced a LLM-based framework to configure which cutting plane separators to use for a given MILP problem with little to no training data based on characteristics of the instance, such as a natural language description of the problem and the associated LaTeX formulation.

[2025/09] Autonomous Code Evolution Meets NP-Completeness, preprint. [arXiv]

• This study introduced SATLUTION, an LLM-based framework that autonomously evolves full-scale SAT solver repositories. The system developed solvers that achieved new SOTA performance, outperforming human-designed champions in the SAT Competition 2025 by attaining the lowest PAR-2 scores and solving more instances.

[2025/08] EvoCut: Strengthening Integer Programs via Evolution-Guided Language Models, preprint. [arXiv] [official code]

• This study introduces EvoCut, a novel framework that accelerates Mixed-Integer Linear Programming (MILP) by injecting problem-specific cutting planes into the LP relaxation. These cuts reduce the feasible set of the LP relaxation and improve solver efficiency.

[2025/05] Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning, ICLR 2025 Workshop. [paper] [official code]

• This study introduces EvoTune, a method that continuously refines LLMs' generative policies through RL fine-tuning within an evolutionary search loop for algorithm discovery. This approach consistently accelerates the discovery of superior algorithms and enhances search space exploration across various combinatorial optimization and symbolic regression tasks, often outperforming static LLM-based baselines and, in some cases, human experts.

[2025/09] A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving, preprint. [arXiv] [official code]

[2025/05] A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions, IJCAI 2025. [paper] [official code]

[2024/05] Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process, EJOR. [ScienceDirect] [arXiv]

[2025/10] Democratizing Optimization with Generative AI, SSRN. [paper]

[2025/09] "It Was a Magical Box": Understanding Practitioner Workflows and Needs in Optimization, arxiv. (interview paper) [arXiv]

[2025/08] Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier, INFORMS Journal on Data Science. [paper]

[2025/07] Beyond Mere Automation: A Techno-functional Framework for Gen AI in Supply Chain Operations, KDD 2025 Workshop AI4SupplyChain. [OpenReview]

[2025/07] Large Language Models for Supply Chain Decisions, preprint. [arXiv]

[1987/08] Two Heads Are Better than One: The Collaboration between AI and OR, Interfaces. [paper]

Foundation Models for Combinatorial Optimization

• FM4CO contains interesting research papers (1) using Existing Large Language Models for Combinatorial Optimization, and (2) building Domain Foundation Models for Combinatorial Optimization.

Awesome Multi-Agent Papers

• A compilation of the best multi-agent papers by the Swarms Team.

timefold

• The open source Solver AI for Java and Kotlin to optimize scheduling and routing with good explainability.