用蒙特卡洛方法系统优化大模型生成代码，更高效找到科学问题解。
SMCEvolve: Principled Scientific Discovery via Sequential Monte Carlo Evolution
- 将代码演化看作奖励导向的采样过程，用序贯蒙特卡洛算法实现
- 在多个科学任务中以更少调用大模型次数达到更好结果
- 自动判断何时停止搜索，适合追求效率的自动化科研人员
基于大语言模型的程序演化已成为自动化科学发现的强大工具，但现有框架缺乏对组件设计的理论指导，也无法保证搜索收敛。本文提出SMCEvolve，将程序搜索重构为从奖励倾斜目标分布中采样，并通过序贯蒙特卡洛（SMC）采样器近似求解。由此自然导出三大原理性组件：自适应父代重采样、突变与接受的混合机制、自动收敛控制。我们进一步提供了有限样本复杂度分析，给出了达到指定近似误差所需的LLM调用预算上限。在数学推导、算法效率、符号回归及端到端机器学习研究基准测试中，SMCEvolve均超越现有先进演化系统，且在自我决定终止条件下使用更少的LLM调用。代码已开源：https://github.com/kongwanbianjinyu/SMCEvolve。
原文摘要 · Abstract (English)
LLM-driven program evolution has emerged as a powerful tool for automated scientific discovery, yet existing frameworks offer no principled guide for designing their individual components and provide no guarantee that the search converges. We introduce SMCEvolve, which recasts program search as sampling from a reward-tilted target distribution and approximates it with a Sequential Monte Carlo (SMC) sampler. From this view, three core mechanisms emerge as principled components: adaptive parent resampling, mixture of mutation with acceptance, and automatic convergence control. We further provide a finite-sample complexity analysis that bounds the LLM-call budget required to reach a target approximation error. Across math, algorithm efficiency, symbolic regression, and end-to-end ML research benchmarks, SMCEvolve surpasses state-of-the-art evolving systems while using fewer LLM calls under self-determined termination. The code is available at https://github.com/kongwanbianjinyu/SMCEvolve.
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