Newest papers

ExpEvolve: Structuring and Reusing Experience for Open-Ended Discovery

Yongqiang Chen, Chenxi Liu, Deyu Zou, Zhenhao Chen, Tongliang Liu, Bo Han, Kun Zhang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29303 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

进化编码代理,如AlphaEvolve是使用大语言模型(LLMs)建立人工智能科学家的显著成功之一。这些代理通过迭代改进和发展程序来解决开放的科学问题,利用LLM的先前知识和推理能力。尽管他们成功,在表面 heuristics上使用的程序的突变大多是低水平,没有持续的控制单元。相比之下,科学通过想法组合。在这项工作中,我们提出了ExpEvolve,它注入了想法水平的提取和控制进化:它发现并利用了从解释不同程序之间的差异的程序的高级因素。我们描述了当发现与想法因素提高样品效率时的理论条件。我们还构建了一个实用框架,以表明提取的高级概念因素有助于指导从经验和探索到机器盘时

Machine translation (Meta M2M100); the English original follows.

Abstract

Evolutionary coding agents such as AlphaEvolve are one of the notable successes in using Large Language Models (LLMs) to build AI Scientists. These agents tackle open-ended scientific problems by iteratively improving and evolving programs, leveraging the prior knowledge and reasoning capabilities of LLMs. Despite their success, mutations over programs are mostly low-level upon surface heuristics, without a persistent unit of control. In contrast, science compounds through ideas. In this work, we propose ExpEvolve, which injects idea-level extraction and control into evolution: it discovers and leverages the high-level factors from programs that explain scoring differences among different programs. We characterize theoretical conditions on when discovery with idea factors improves sample efficiency. We also build a practical framework to show that the extracted high-level idea factors are helpful for guiding exploitation from experience and exploration when discovery comes to a plateau. Across 11 open-ended mathematical tasks, ExpEvolve significantly improves the final performance and sample efficiency up to 5.0x. The improvements also transfer to 4 other LLMs and to 6 machine learning tasks from MLS-Bench. Analysis shows that ExpEvolve can discover idea-level factors to provide better exploration and exploitation for evolutionary discovery.

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Topics: Computer science, Reuse, Structuring, Artificial intelligence, Coding (social sciences), Machine learning

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange