arXiv AI

ACEvo: Adversarial Co-Evolution of Problem Distributions and Solvers for Combinatorial Optimization

arXiv:2506. 02594v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to synthesize heuristic programs, yet most existing pipelines optimize solvers against fixed benchmark distributions.

arXiv AI

Jun 26

EvoOptiGraph: Weakness-Driven Coevolution via Graph-Based Structural Generation for Optimization Modeling

arXiv:2606. 26578v1 Announce Type: new Abstract: Automating optimization modeling from natural language with large language models (LLMs) faces two key challenges.

By Qingcan Kang, Mingyang Liu, Xiaojin Fu, Shixiong Kai, Tao Zhong, Mingxuan Yuan

llms agents reinforcement-learning fine-tuning

arXiv AI

Aug 11

MOSAIC: Adversarial Co-evolution of Specialist Heuristics and Problem Instances for LLM-based Automated Heuristic Design

arXiv:2608. 07544v1 Announce Type: cross Abstract: Automated heuristic design (AHD) with large language models (LLMs) has produced strong heuristics for combinatorial optimization problems (COPs).

By Oguzhan Gungordu, Siheng Xiong, Faramarz Fekri

llms benchmarks safety

arXiv Machine Learning

Jul 27

Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning

arXiv:2607. 21971v1 Announce Type: new Abstract: Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains.

By Shujin Wu, Cheng Qian, Xiusi Chen, Heng Ji

llms reinforcement-learning benchmarks

arXiv AI

Jun 10

Beyond Static Evaluation: Co-Evolutionary Mechanisms for LLM-Driven Strategy Evolution in Adversarial Games

arXiv:2606. 10389v1 Announce Type: new Abstract: Recent advances in LLM-driven code evolution have enabled automated discovery by iteratively generating and improving programs.

By Haoran Li, Zengle Ge, Ziyang Zhang, Xiaomin Yuan, Yui Lo, Qianhui Liu, Bocheng An, Dongke Rong, Jiaqun Liu, Annan Li, Jianmin Wu, Dawei Yin, Dou Shen

llms agents safety

arXiv AI

Jun 2

BenchEvolver: Frontier Task Synthesis via Solution-Centric Evolution

arXiv:2606. 01286v1 Announce Type: cross Abstract: The rapid progress of frontier large language models has led to widespread benchmark saturation, limiting the ability of existing datasets to differentiate model capabilities or provide useful training signal.

By Yangzhen Wu, Aaron J. Li, Wenjie Ma, Li Cao, Ziheng Zhou, Mert Cemri, Shu Liu, Yuran Xiu, Chenxiao Yan, Haikun Zhao, Bin Yu, Ion Stoica, Dawn Song

llms reinforcement-learning 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

arXiv AI

Jun 2

FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization

arXiv:2605. 25246v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines.

By Minwei Kong, Chonghe Jiang, Ao Qu, Wenbin Ouyang, Zhaoming Zeng, Xiaotong Guo, Zhekai Li, Junyi Li, Yi Fan, Xinshou Zheng, Xi Jing, Yikai Zhang, Zhiwei Liang, Seonghoo Kim, Runqing Yang, Zijian Zhou, Sirui Li, Han Zheng, Wangyang Ying, Ou Zheng, Chonghuan Wang, Jinglong Zhao, Hanzhang Qin, Cathy Wu, Paul Pu Liang, Jinhua Zhao, Hai Wang

llms agents benchmarks

arXiv AI

Jun 18

Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design

arXiv:2602. 23092v2 Announce Type: replace Abstract: The Capacitated Vehicle Routing Problem (CVRP), a fundamental combinatorial optimization challenge, focuses on optimizing fleet operations under vehicle capacity constraints.

By Zhuoliang Xie, Fei Liu, Zhenkun Wang, Qingfu Zhang

llms benchmarks

arXiv Machine Learning

Jul 7

EvoOtter: Evolutionary Reproduction Test Generator

arXiv:2607. 02854v1 Announce Type: cross Abstract: Before fixing an issue, it is useful to first reproduce it by generating a bug reproduction test (BRT).

By Toufique Ahmed, Jatin Ganhotra, Avraham Shinnar, Martin Hirzel

llms benchmarks

arXiv AI

Aug 6

Improving Auto-Design of Neural PDE Solvers with a Domain-Specific Language

arXiv:2608. 04384v1 Announce Type: new Abstract: Neural PDE solver auto-design is fundamentally a search-space representation problem.

By Shengxin Kong, Liwen Xu, Jingwen Fu

llms agents benchmarks

arXiv AI

Jul 1

From Search to Synthesis: Training LLMs as Zero-Shot Workflow Generators

arXiv:2606. 30704v1 Announce Type: cross Abstract: Large language models (LLMs) excel across a wide range of tasks, yet their instance-specific solutions often lack the structural consistency needed for reliable deployment.

By Gan Luo, Zihan Qin, Bin Dong, Wotao Yin

llms nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 2

AlgoBench: Benchmarking Algorithmic Adaptation in Code Generation

arXiv:2607. 00062v1 Announce Type: cross Abstract: High pass rates on established programming benchmarks such as HumanEval and LiveCodeBench do not always show whether a model can reason about algorithms.

By Xinyuan Song, Zekun Cai, Liang Zhao

llms benchmarks

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