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

Paper: arXiv 2609.33524

Authors: Siyuan Li, Jiangfeng Zhang, Rui Yao, Weihua Qiu, Mingyang Xu, Zixuan Yuan

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.

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper introduces a novel framework, EverMine, to dissect the self-evolution of research capabilities in long-horizon alpha discovery. It combines rigorous empirical evaluation with a clear conceptual model, providing valuable insights into the effectiveness of evolving research agents. The findings, while showing no consistent gain from Cap evolution, are significant for future research in this area.

Research Flowchart

  flowchart TD
    A[Research Goal: Evaluate Self-Evolving Research Capabilities in Alpha Discovery] --> B{Key Methodology: EverMine Framework};
    B --> C[Inputs: History (Hist), Factor Portfolio (Frontier), Reusable Capabilities (Cap)];
    C --> D{Computational Process: Comparative Runs};
    D --> E[Outcomes: End-to-end Comparisons, Conditional Cap Value, Experience Replay];
    E --> F{Key Finding 1: No Consistent Gain from Cap Evolution};
    E --> G{Key Finding 2: Candidate Value Depends on Portfolio & Submission Order};
    F & G --> H[Conclusion: Evaluate Self-Evolving Capabilities via End-to-End Outcomes, Conditional Value, & Experience Effects];

Cited References (47)

1. AEL: Evolving Agent Harness in Open-Ended Environments (2026) — link
2. AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay (2025) — link
3. AlphaAgentEvo: Evolution-Oriented Alpha Mining via Self-Evolving Agentic Reinforcement Learning (2026) — link
4. AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents (2026) — link
5. The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting, and Non-Normality (2014) — link
6. AlphaSAGE: Structure-Aware Alpha Mining via GFlowNets for Robust Exploration (2026) — link
7. Cognitive Alpha Mining via LLM -Driven Code-Based Evolution (2026) — link
8. Approximate Submodularity and its Applications: Subset Selection, Sparse Approximation and Dictionary Selection (2018) — link

2 of 47 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.