发表机构
Google; University of Cambridge（谷歌公司; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AutoScientist-Quant是将量化研究视为带预算约束搜索问题的自进化编码智能体，修复了评估流程的前瞻问题，在CSI universe等场景下实现最优泛化性能。
AI 中文摘要
大语言模型智能体能够发现阿尔法（alpha），但现有方法存在三个缺陷：搜索过程在运行期间无法自适应调整；自动化通常仅停留在阿尔法生成阶段，而库选择和模型选择仍需手动完成；阿尔法发现可能通过循环反馈或代码问题读取测试窗口数据。本文提出AutoScientist-Quant，一种将量化研究视为带预算约束搜索问题的自进化搜索过程。单个控制器根据剩余预算条件化每一个决策，在每一轮选择是否改进、组合、转向或停止，要扩展哪个节点、生成多少个阿尔法，以及如何从共享记忆中检索过往轨迹。该核心还会从库中进行选择并调优模型，形成从假设到可部署策略的闭环。我们还对从先前工作复用的评估流程进行审查，修复了两个前瞻问题，并保持反馈窗口与保留的测试窗口互不重叠，因此每次比较都能测试真正的泛化能力。在CSI universe中，该框架在所有设置下几乎每项指标都取得最优值，且这些结论在多种主干网络和市场中均成立。
英文摘要
Large language model agents can discover alphas, yet current methods have three weaknesses. The search cannot adapt during the run, automation usually ends at alpha generation while library selection and model choice stay manual, and alpha discovery can read the test window through loop feedback or code problems. We present AutoScientist-Quant, a self evolving search process that regards quantitative research as one budgeted search problem. A single controller conditions every decision on the remaining budget, choosing at each round whether to improve, combine, pivot, or stop, which node to expand, how many alphas to generate, and how to retrieve past trajectories from the shared memory. The same core then selects from the library and tunes the model, closing the loop from hypothesis to deployable strategy. We also review the evaluation pipeline reused from prior work, fix two lookahead problems, and keep the feedback window disjoint from the held out test window, so every comparison tests true generalization. On CSI universes, the framework attains the best value of nearly every metric in every setting, and these conclusions hold across several backbones and markets.
