The paper describes progress toward an AI Software Factory that accelerates every stage of the software development lifecycle—Targeting, Coding, Reviewing, and Ops—by generating a metadata exhaust for self‑improvement. It focuses on data systems and evolutionary coding tasks, reporting scaled deployments at Microsoft that achieved three‑fold engineering efficiency over agentic coding and up to 22‑fold token efficiency. The authors also outline several open challenges for further development.
By Anna Pavlenko, Bogdan Crivat, Brandon Haynes, Carlo Curino, Fotis Psallidas, Jaro Slawinski, Johannes Freischuetz, Laura Pereira Sanchez, Markus Weimer, Mathieu Demarne, Matthias Jasny, Mauktik Gandhi, Max Bovykin, Mirco Milletari, Purbasha Ghosh, Qiushi Bai, Raghu Ramakrishnan, Rahul Pandita, Sergiy Matusevich, Shivaram Venkataraman, Subru Krishnan, Md. Tareq Mahmood, Tiemo Bang, Venkatesh Emani, Xuan Zhao, Yiwen Zhu
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
arXiv:2609.37172v1 Announce Type: new
Abstract: Large language models (LLMs) have emerged as powerful tools for automated heuristic design (AHD), enabling iterative generation and refinement of heuri...
By Jianghan Zhu, Cong Zhang, Rongjie Zhu, Chi Zhang, Zhiguang Cao
arXiv:2605. 09018v4 Announce Type: replace-cross Abstract: We introduce the Evolving Ensemble of Agents (EvE), a decentralized framework that organizes existing, highly capable coding agents into a live, co-evolving system for algorithmic discovery.
By Zongmin Yu, Liu Yang
arXiv:2606. 26294v1 Announce Type: cross Abstract: Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains.
By Alex Iacob, Andrej Jovanovi\'c, William F. Shen, Daniel Burkhardt, Meghdad Kurmanji, Nurbek Tastan, Lorenzo Sani, Niccol\`o Alberto Elia Venanzi, Ambroise Odonnat, Zeyu Cao, Bill Marino, Xinchi Qiu, Nicholas D. Lane
arXiv:2602. 22480v4 Announce Type: replace Abstract: An important emerging application of coding agents is agent harness optimization: the iterative improvement of a target agent by editing and evaluating its code.
By Varun Ursekar, Apaar Shanker, Veronica Chatrath, Yuan Xue, Samuel Marc Denton
arXiv:2606. 04455v1 Announce Type: new Abstract: Current AI benchmarks evaluate agents on task execution within human-designed workflows.
By Xinyu Lu, Tianshu Wang, Pengbo Wang, zujie wen, Zhiqiang Zhang, Jun Zhou, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun
arXiv:2608.29096v1 Announce Type: cross
Abstract: The ongoing changes in software engineering requirements have created a substantial need for automated tools which can create secure source code from...
By Pravin Game, Vipin Ramakrishnan, Prathamesh Wagh
arXiv:2605. 09018v3 Announce Type: replace-cross Abstract: We introduce Evolutionary Ensemble (EvE), a decentralized framework that organizes existing, highly capable coding agents into a live, co-evolving system for algorithmic discovery.
By Zongmin Yu, Liu Yang
arXiv:2607. 03451v1 Announce Type: cross Abstract: While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines.
By Yifei Shen, Bo Li, Xinjie Zhang
arXiv:2606. 24245v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments.
By Pingchuan Ma, Zhaoyu Wang, Zimo Ji, Yuguang Zhou, Zhantong Xue, Zongjie Li, Shuai Wang, Xiaoqin Zhang
arXiv:2609.37834v1 Announce Type: new
Abstract: Harness optimization provides a practical setting for recursive self-improvement (RSI), where agent-generated modifications inform subsequent changes t...
By Haoyu Dong, Yuhang Zhou, Zihao Lin, Yifan Wu, Bo Peng, Mingyi Wang, Xiangjun Fan, Lizhu Zhang, Zhuokai Zhao