Newest papers

EverMine: Dissecting the Self-Evolution of Research Capabilities in Long-Horizon Alpha Research

Siyuan Li, Jiangfeng Zhang, Rui Yao, Weihua Qiu, Mingyang Xu, Zixuan Yuan · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.33524 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

自我进化的代理人旨在将研究反馈转化为可重复使用的技能、工具和研究规则。这些积累的能力是否会继续改善后续的研究需要进行控制的评估。长期视野的alpha发现提供了国家依赖的设置:一旦一个新的因素进入投资组合,预测信息已经覆盖了变化,因此同一候选人或经验的重复使用的价值可能会随着时间的推移而改变。我们引入了EverMine,用于研究长期视野的alpha发现中的自我进化的研究能力的经验框架。EverMine将研究因素分解为历史(Hist),目前的因素组合(Frontier)和可重复使用的能力(Cap)。在相匹配的资源限制下,我们将与固定的或不断演变的Cap进行比较,并取代Histfolio持有

Machine translation (Meta M2M100); the English original follows.

Abstract

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.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Portfolio, Computer science, Efficient frontier, Resource (disambiguation), Value (mathematics), Selection (genetic algorithm)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange