arXiv AI July 14, 2026 By Yongchang Fu, Xinjie Huang, Chengjun Dai, Chengzhe Feng, Junshao Zhang, Hong Zhu

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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

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

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

Aug 18

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

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

Sep 2

AgentFactory: Towards Automated Agentic System Design and Optimization

arXiv:2609.01045v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and...

By Enci Zhang, Haofeng Wang, Yuesheng Zhu, Xiaole Cui, Guibo Luo

llms agents fine-tuning benchmarks

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

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

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

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

NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents

AgentFactory: Towards Automated Agentic System Design and Optimization