Large language model (LLM) agents are increasingly applied to long-horizon tasks such as scientific discovery and machine learning engineering (MLE), where sustained self-evolution becomes a key capability. However, existing MLE agents suffer from inter-branch information isolation, memoryless search, and lack of hierarchical control, which together hinder long-horizon optimization. We present MLEvolve, an LLM-based self-evolving multi-agent framework for end-to-end machine learning algorithm discovery. By extending tree search to Progressive MCGS, MLEvolve enables cross-branch information flow through graph-based reference edges and gradually shifts the search from broad exploration to focused exploitation with an entropy-inspired progressive schedule. To allow the agent to evolve with accumulated experience, we introduce Retrospective Memory, which combines a cold-start domain knowledge base with a dynamic global memory for task-specific experience retrieval and reuse. For stable long-horizon iteration, we further decouple strategic planning from code generation with adaptive coding modes. Evaluation on MLE-Bench shows that MLEvolve achieves state-of-the-art performance across multiple dimensions including average medal rate and valid submission rate under a 12-hour budget (half the standard runtime). Moreover, MLEvolve also outperforms specialized algorithm discovery methods including AlphaEvolve on mathematical algorithm optimization tasks, demonstrating strong cross-domain generalization. Our code is available at https://github.com/InternScience/MLEvolve.

翻译：大语言模型智能体越来越多地被应用于科学发现与机器学习工程等长周期任务，其中持续自我进化成为一项关键能力。然而，现有机器学习工程智能体存在分支间信息隔离、无记忆搜索以及缺乏层级控制等问题，这些因素共同制约了长周期优化的实现。我们提出MLEvolve——一种基于大语言模型的自我进化多智能体框架，用于端到端机器学习算法发现。通过将树搜索扩展为渐进式蒙特卡洛图搜索，MLEvolve利用基于图的参考边实现跨分支信息流动，并借助熵启发的渐进式调度策略，逐步将搜索从广泛探索转向聚焦利用。为使智能体能随积累经验进化，我们引入回溯记忆机制，该机制将冷启动领域知识库与动态全局记忆相结合，用于任务特定经验的检索与复用。为实现稳定的长周期迭代，我们进一步将策略规划与采用自适应编码模式的代码生成解耦。在MLE-Bench上的评估表明，MLEvolve在12小时预算（为标准运行时的一半）下，在平均奖牌率与有效提交率等多个维度均达到最先进性能。此外，MLEvolve在数学算法优化任务上亦超越了包括AlphaEvolve在内的专用算法发现方法，展现出强大的跨领域泛化能力。我们的代码已开源至https://github.com/InternScience/MLEvolve。