AlphaGEAR: A Self-Evolving Agent for Alpha Mining via Library-Guided Exploration and Auditable Refinement

LLM agents can easily search alpha factors and explore program spaces at a scale difficult to achieve manually. The resulting candidates must then be selected and combined into a final predictor. Yet generating individually promising or diverse factors does not ensure that they contribute useful information to the final predictor, while repeated evaluation can amplify selection and feedback risks. We introduce AlphaGEAR, a self-evolving agent for constructing refined factor libraries. AlphaGEAR aligns search with deployment by guiding factor evolution with library-relative residual information and retaining complementary factors through stream-aware admission and marginal-contribution refinement for an equal-weight predictor. A discrete, query-limited certification interface makes additional held-out feedback bounded and auditable. Under a common equal-weight evaluation on CSI 300, AlphaGEAR achieves an average development RankIC of 0.0517 across three random seeds, compared with 0.0043 for the strongest competing agent, while retaining only 4-8 members against 23-44 in the competing libraries; the advantage persists after style orthogonalization. Frozen libraries pooled across search seeds attain forward RankICs of 0.0473, 0.0503, and 0.0589 on CSI 300, CSI 500, and CSI 1000, respectively, retaining 81%, 95%, and 103% of their corresponding development RankICs; every completed certified individual export is also positive out of sample. These results show that aligning LLM-driven exploration with library-level construction yields compact, auditable factor libraries that retain predictive value across windows and market universes.