The paper introduces ARTEMIS, a no-code evolutionary optimization platform that automatically tunes large language model (LLM) agents by jointly optimizing prompts, tool descriptions, and parameters using semantically-aware genetic operators. Starting from a benchmark script and natural language goals, ARTEMIS discovers configurable components, extracts performance signals from execution logs, and evolves configurations without architectural changes. Experiments on four agent systems show significant gains: a 13.6% increase in acceptance rate for the ALE Agent, a 10.1% performance boost for the Mini‑SWE Agent, a 36.9% token‑reduction for the CrewAI Agent, and a 22% accuracy improvement for the MathTales‑Teacher Agent using a smaller open‑source model.
By Paul Brookes, Vardan Voskanyan, Rafail Giavrimis, Matthew Truscott, Mina Ilieva, Chrystalla Pavlou, Alexandru Staicu, Manal Adham, Will Evers- Hood, Jingzhi Gong, Kejia Zhang, Matvey Fedoseev, Vishal Sharma, Roman Bauer, Zheng Wang, Hema Nair, Wei Jie, Tianhua Xu, Aurora Constantin, Leslie Kanthan, Michail Basios
ExpEvolve introduces idea-level extraction and control into evolutionary coding agents, enabling the discovery of high-level factors that explain differences in program performance. By leveraging these factors, the framework guides both exploitation of existing experience and exploration when progress stalls, leading to significant gains in sample efficiency and final performance. Across 11 open-ended mathematical tasks and additional benchmarks, ExpEvolve improves results by up to 5.0× and transfers benefits to other large language models and machine learning tasks.
By Yongqiang Chen, Chenxi Liu, Deyu Zou, Zhenhao Chen, Tongliang Liu, Bo Han, Kun Zhang
arXiv:2602. 13769v3 Announce Type: replace Abstract: Automating heuristic design in complex, experiment-driven domains requires more than iterative mutation of solution algorithms.
By Qi Liu, Ruochen Hao, Can Li, Wanjing Ma
arXiv:2602.16805v2 Announce Type: replace
Abstract: Code evolution is a family of techniques that rely on large language models to search through possible computer programs by evolving existing code....
By Yonatan Gideoni, Sebastian Risi, Yarin Gal
OR-Agent is a multi‑agent research framework that automates heuristic design for optimization problems by structuring heuristic search as a tree‑based workflow with explicit hypothesis generation and systematic backtracking. It introduces a hierarchical, optimization‑inspired reflection system that uses short‑term reflections as verbal gradients, long‑term reflections as verbal momentum, and memory compression as semantic weight decay to guide research dynamics. Experiments on classical combinatorial optimization tasks and simulation‑based cooperative driving scenarios show that OR‑Agent outperforms strong evolutionary search baselines, with all code and data publicly available.
By Qi Liu, Ruochen Hao, Can Li, Wanjing Ma
arXiv:2606. 26173v1 Announce Type: new Abstract: Recent work shows that Large Language Models (LLMs) can act as semantic mutation operators for the evolutionary discovery of programs and proofs.
By Dhruv Sharma, Gautam Shroff
arXiv:2605. 02411v2 Announce Type: replace Abstract: A semantic gap separates how users describe tasks from how tools are documented.
By Kyle Zheng, Han Zhang, Renliang Sun, Chenchen Ye, Wei Wang
arXiv:2608. 05651v1 Announce Type: cross Abstract: Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long evolutionary runs is costly.
By Sichun Luo, Yi Huang, Guanzhi Deng, Haibo Wang, Haochen Luo, Lei Li, Zefa Hu, Junlan Feng, Qi Liu
SEDIMA is a persistent hierarchical insight memory designed for evolutionary search agents that use large language models. It transforms raw search traces into natural‑language insights, clusters them by semantic similarity, and retrieves relevant guidance to inform future mutations, thereby accumulating transferable knowledge across runs and problems. When added as a drop‑in module, SEDIMA improves average final performance by 5.5% on AlgoTune and 6.6% on ALE‑Bench LITE, and reduces the number of iterations needed to reach baseline‑best performance by 32.3% on OpenEvolve.
By Amirhossein Abaskohi, Mahdi Mostajabdaveh, Zirui Zhou
The paper "Code2Math: Can Your Code Agent Evolve Math Problems Through Exploration?" explores how code agents can autonomously generate more complex variations of existing math problems. It introduces a multi‑agent framework that evolves problems while ensuring they remain solvable and increasingly difficult. Experiments show that with sufficient exploration, code agents can produce new, structurally distinct, and challenging problems, demonstrating a scalable method for creating high‑difficulty mathematical reasoning tasks.
By Dadi Guo, Yuejin Xie, Qingyu Liu, Weixian Huang, Jiayu Liu, Zhiyuan Fan, Qihan Ren, Shuai Shao, Tianyi Zhou, Jianjie Feng, Wenze Su, Yujiu Yang, Dongrui Liu, Yi R. Fung
Recent systems for optimizing prompts, programs, and ML workflows typically rely on explicit outer-loop controllers such as evolutionary search, bandits, or textual-gradient methods. We ask a fundamentally different question: how much of this search policy can be internalized by a single tool-using agent?
The paper introduces a Bayesian Evolutionary Optimization framework for automatically discovering task‑specific multi‑agent system (MAS) topologies. It combines evolutionary operators that generate diverse workflow graphs with a graph‑aware Gaussian Process and Expected Improvement to guide costly evaluations toward promising candidates. Experiments on BigCodeBench for code generation and on tabular preprocessing benchmarks show that the evolved MAS architectures outperform other evaluated agent systems and improve performance on temporal datasets while remaining competitive on IID data.
By Dominik Jehle, Tom Zehle