AQuA：会自我改进的量化交易研究智能体 AQuA: Recursively Self-Improving Quantitative Trading Research Agents

精读摘要

这项研究探索量化投资研究层面的「递归自我改进」：自主系统能否用早期实验的证据，改进后续迭代提出的假设与候选。AQuA 包含两套独立的语言模型驱动研究系统——一套做符号化因子发现，一套做可训练模型开发，两者不共享智能体、记忆、候选空间或研究状态，各自闭环迭代。这种设计为「AI 研究员」在量化投资领域的自我进化提供了参照。 This work studies recursive self-improvement in quantitative investment research: whether an autonomous system can use evidence from earlier experiments to improve later hypotheses and candidates. AQuA comprises two separate LLM-driven research systems, one for symbolic factor discovery and one for trainable model development, each independently closing its own research loop without sharing agents, memories, or state. It offers a reference design for self-evolving AI researchers in finance.