将深度研究与算法进化结合，自动发现可执行的科学算法
Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep Research
- 融合外部检索与代码调试的迭代反馈机制
- 在9个科学领域持续改进初始算法并生成可运行新算法
- 适合需要自动化创新的科研人员和算法开发者
大语言模型有望成为科学助手，但现有代理要么仅依赖算法演化，要么仅进行孤立的深度研究，均存在明显局限。纯算法演化（如AlphaEvolve）仅依赖LLM内部知识，在复杂领域迅速达到瓶颈；而纯深度研究提出想法却缺乏验证，常产生不切实际或不可实现的方案。我们提出DeepEvolve，将深度研究与算法演化相结合，通过外部知识检索、跨文件代码编辑与系统化调试，在反馈驱动的迭代循环中协同推进。每轮不仅提出新假设，还同步完善、实现并测试，避免浅层优化或无效过度修正。在化学、数学、生物、材料和专利共九个基准测试中，DeepEvolve持续改进初始算法，成功生成可执行的新算法并取得稳定提升。该方法弥合了无引导演化与无实证研究之间的鸿沟，为科学算法发现提供了可靠框架。代码已开源：https://github.com/liugangcode/deepevolve。
原文摘要 · Abstract (English)
Large language models hold promise as scientific assistants, yet existing agents either rely solely on algorithm evolution or on deep research in isolation, both of which face critical limitations. Pure algorithm evolution, as in AlphaEvolve, depends only on the internal knowledge of LLMs and quickly plateaus in complex domains, while pure deep research proposes ideas without validation, resulting in unrealistic or unimplementable solutions. We present DeepEvolve, an agent that integrates deep research with algorithm evolution, uniting external knowledge retrieval, cross-file code editing, and systematic debugging under a feedback-driven iterative loop. Each iteration not only proposes new hypotheses but also refines, implements, and tests them, avoiding both shallow improvements and unproductive over-refinements. Across nine benchmarks in chemistry, mathematics, biology, materials, and patents, DeepEvolve consistently improves the initial algorithm, producing executable new algorithms with sustained gains. By bridging the gap between unguided evolution and research without grounding, DeepEvolve provides a reliable framework for advancing scientific algorithm discovery. Our code is available at https://github.com/liugangcode/deepevolve.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。