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
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
The paper introduces OptTips, a knowledge base of 50 expert optimization modeling techniques, and OptDachshund, a multi‑agent framework that generates mathematical models and solver code from natural‑language problem descriptions. Using these tools, the authors create the EfficientOpt benchmark, comprising 561 expert‑reviewed tasks with paired reference implementations, to evaluate large language models (LLMs) on both correctness and computational efficiency. Their experiments with 11 LLMs show a consistent efficiency gap: even when LLMs produce correct solutions, the resulting programs often take longer to solve than expert‑crafted counterparts, highlighting the need to assess both accuracy and runtime performance in LLM‑based optimization modeling.
By Zhong Li, Xin Huang, Jinhui Wan, Xiangyi Wang, Shenkai Zhang, Ruiqi Chen, Wenyu Liu, Zaiwen Wen, Ziyan Luo
Meta-Reflective Evolution Effectively Improves Production AI Systems describes CoCoEvolve, an evolutionary optimizer that uses meta-reflection to retrieve evidence from the entire evolutionary history and probe live systems before proposing mutations. The approach is applied to data agents, LLM-powered data pipelines, and data engineering workflows, achieving state‑of‑the‑art results on DABStep, multiple LLM pipeline workloads, and data-eng-bench. Ablation studies confirm that each component of CoCoEvolve contributes to its performance gains.
By Seung Won Wilson Yoo, Chang Zeng, Aditi Somayajula, Sahithi Ankireddy, Rohan Kosalge, Avishree Khare, John Mitchell, Aditya G. Parameswaran, Rajhans Samdani, Anupam Datta
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
arXiv:2511. 00802v2 Announce Type: replace-cross Abstract: With data-driven development now widely adopted, online A/B testing is an established method for measuring the effects of new technologies.
By Jie JW Wu, Ayanda Patrick Herlihy, Ahmad Saleem Mirza, Ali Afoud, Fatemeh Fard