EverMine：剖析长周期Alpha研究中研究能力的自我进化

中文摘要 AI 辅助

自我进化智能体旨在将研究反馈转化为可复用的技能、工具和研究规则。这些累积的能力是否能持续改进后续研究，需要受控评估。长周期Alpha发现提供了一个状态依赖的环境：一旦新因子进入投资组合，已覆盖的预测信息发生变化，因此同一候选或经验的价值可能随时间变化。我们提出了EverMine，一个用于研究长周期Alpha发现中自我进化研究能力的实证框架。EverMine将研究状态分解为历史（Hist）、当前因子组合（Frontier）和可复用能力（Cap）。在匹配资源限制下，我们比较固定或进化Cap的完整运行，并在保持Hist和Frontier固定的情况下替换Cap，以估计累积能力的条件价值。我们还结合完整轨迹与历史状态回放，考察基于经验的决定如何影响候选选择和组合结果。在18条长周期轨迹中，端到端比较显示Cap进化没有一致增益。在来自共享Hist和Frontier状态的48条延续分支中，累积Cap也未一致优于初始Cap。现有因子结构的参数调优仍可改善组合。在一次探索性回放中，从一条进化轨迹的两个筛选批次中，一些被筛选出的候选在原始状态下具有正边际价值，然而顺序提交所有被筛选出的候选在两个批次中均略微降低了最终组合IC。这些结果表明候选价值依赖于进化的组合和提交顺序，并促使通过端到端结果、条件能力价值以及基于经验的决策后果来评估自我进化研究能力。

英文摘要

Self-evolving agents aim to turn research feedback into reusable skills, tools, and research rules. Whether these accumulated capabilities continue to improve later research requires controlled evaluation. Long-horizon alpha discovery provides a state-dependent setting: once a new factor enters the portfolio, the predictive information already covered changes, so the value of the same candidate or experience may change over time. We introduce EverMine, an empirical framework for studying self-evolving research capabilities in long-horizon alpha discovery. EverMine decomposes the research state into history (Hist), the current factor portfolio (Frontier), and reusable capabilities (Cap). Under matched resource limits, we compare complete runs with fixed or evolving Cap, and replace Cap while holding Hist and Frontier fixed to estimate the conditional value of accumulated capabilities. We also combine full trajectories with historical-state replay to examine how experience-based decisions affect candidate selection and portfolio outcomes. Across 18 long-horizon trajectories, end-to-end comparisons show no consistent gain from Cap evolution. Across 48 continuation branches from shared Hist and Frontier states, accumulated Cap also does not consistently outperform the initial Cap. Parameter tuning of existing factor structures can still improve the portfolio. In an exploratory replay of two screening batches from one Evolving trajectory, some screened-out candidates have positive marginal value at the original state, yet submitting all screened-out candidates sequentially slightly lowers final portfolio IC in both batches. These results show that candidate value depends on the evolving portfolio and submission order, and motivate evaluating self-evolving research capabilities through end-to-end outcomes, conditional capability value, and the consequences of experience-based decisions.

发表机构

• The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
• The Hong Kong Polytechnic University（香港理工大学）
• Singapore Management University（新加坡管理大学）

机构由 AI 辅助整理，请以论文原文为准。