FactorMiner: Self-Evolving Agent with Skills and Experience Memory — arXiv:2602.14670
title: FactorMiner: A Self-Evolving Agent with Skills and Experience Memory for Financial Alpha Discovery arXiv: 2602.14670 date: 2026-02-16 tags: ['agent-memory', 'memory-retrieval', 'experience-memory', 'financial-agent', 'self-evolving'] reviewer: auto source: arXiv RSS/API
核心贡献¶
- （核心贡献待从原文补充）
方法¶
Formulaic alpha factor mining is a critical yet challenging task in quantitative investment, characterized by a vast search space and the need for domain-informed, interpretable signals. Finding novel signals becomes increasingly difficult as the library grows due to high redundancy.
The authors propose FactorMiner, a lightweight and flexible self-evolving agent framework designed for continuous knowledge accumulation. FactorMiner combines a Modular Skill Architecture that encapsulates systematic financial evaluation into executable tools with a structured Experience Memory that distills historical mining trials into actionable insights (successful patterns and failure constraints).
By instantiating the Ralph Loop paradigm — retrieve, generate, evaluate, and distill — FactorMiner iteratively uses memory priors to guide exploration, reducing redundant search while focusing on promising directions.
Experiments on multiple datasets across different assets and markets show that FactorMiner constructs a diverse library of high-quality factors with competitive performance while maintaining low redundancy among factors as the library scales.
为什么重要¶
本文提出了针对特定场景的 Agent 记忆系统设计。相比于将所有历史信息塞入上下文的简单方案，所提出的层次化/索引化经验记忆机制能够更高效地利用历史信息，同时支持跨任务的知识迁移。
与移动端/端侧的相关性¶
移动端 GUI Agent 需要在有限上下文窗口内处理长时程任务，经验记忆的压缩与索引机制对端侧部署具有重要意义。
参考文献¶
参考文献待从原文补充。