arXiv Computation and Language

MIRROR: A Multi-Agent Framework with Iterative Adaptive Revision and Hierarchical Retrieval for Optimization Modeling in Operations Research

arXiv AI

Jul 7

OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement

arXiv:2607. 05346v1 Announce Type: new Abstract: We propose OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-ready mathematical formulation as well as executable code.

By Adriana Laurindo Monteiro, Nayse Fagundes, Gabriel Mattos Langeloh, Gustavo de Oliveira Kanno, Priscila Louise Aguirre, Thiago Costa Rizuti da Rocha, Victor Leme Beltran

agents benchmarks

arXiv AI

Jul 14

Opti-Agent-Bench: Benchmarking End-to-End Optimization R&D Agents on Real-World Business Problems

arXiv:2607. 10768v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed to solve optimization problems, yet existing benchmarks evaluate them on pre-structured mathematical formulations that bypass the most critical challenge: translating complex business requirements into correct models and solve efficiently.

By Yongchang Fu, Xinjie Huang, Chengjun Dai, Chengzhe Feng, Junshao Zhang, Hong Zhu

llms agents benchmarks

arXiv AI

Aug 12

LLM Agents Factory: Retrieval of Domain-Specific LLM Agents

arXiv:2608. 09934v1 Announce Type: cross Abstract: Large language model (LLM) agents improve task performance by decomposing problems into role-specialized behaviors.

By Vitalii Belov, Artyom Sosedka, Andrey Sakhovskiy, Elizaveta Kovtun, Artyom Boyarskikh, Semen Budennyy

llms rag agents fine-tuning efficiency 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 21

NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents

arXiv:2601. 21372v3 Announce Type: replace Abstract: We present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations using autonomous coding agents (ACAs).

By Yang Song, Anoushka Vyas, Zirui Wei, Sina Khoshfetrat Pakazad, Henrik Ohlsson, Graham Neubig

llms agents 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 Machine Learning

Jun 29

COOPA: A Modular LLM Agent Architecture for Operations Research Problems

arXiv:2606. 27611v1 Announce Type: new Abstract: Operations Research (OR) provides a rigorous framework for high-stakes decision-making, but effective OR modeling requires substantial domain knowledge, mathematical abstraction, and solver expertise.

By Chuanhao Li, Xiaoan Xu, Dirk Bergemann, Ethan X. Fang, Yehua Wei, Zhuoran Yang

llms agents benchmarks

arXiv AI

Aug 5

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.

By Penglin Zhu, Linhai Zhang, Jungang Xu, Xinchi Wei, Xiuqi Wu

llms agents benchmarks

arXiv Machine Learning

Aug 6

From Brute Force to Semantic Insight: Performance-Guided Data Transformation Design with LLMs

arXiv:2601. 03808v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved notable performance in code synthesis; however, data-aware augmentation remains a limiting factor, handled via heuristic design or brute-force approaches.

By Usha Shrestha, Dmitry Ignatov, Radu Timofte

llms reinforcement-learning fine-tuning benchmarks safety

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 AI

Jul 7

Council Mode: A Heterogeneous Multi-Agent Consensus Framework for Reducing LLM Hallucination and Bias

arXiv:2604. 02923v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated advanced capabilities but often suffer from factual inaccuracies (hallucinations) and systematic biases.

By Shuai Wu, Xue Li, Yanna Feng, Yufang Li, Zhijun Wang, Ran Wang

llms agents benchmarks safety

Hugging Face Trending Papers

Aug 24

FormuEvo: LLM-Guided Evolution for Discovering Solver-Efficient Mixed-Integer Programming Formulations

Mixed-integer programming (MIP) lies at the core of operations research and industrial optimization. While large language models (LLMs) have recently shown promise in automated MIP modeling from natur...

llms

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