FactorRSI: LLM-Guided Temporal Factor Mining through Recursive Self-Improvement

Long behavioral histories can contain predictive signals that differ in temporal scope, sample applicability, and modeling requirements. Manually identifying and combining these signals is costly. LLM-guided factor proposals reduce this effort, but a fixed search policy can continue to allocate resources to unproductive directions as evidence accumulates. We formulate LLM-Guided Factor Evolution as joint search over executable sample conditions, temporal windows, and encoder families, followed by fusion of applicable experts. We then introduce RSI (Recursive Self-Improvement)-Guided Factor Evolution, which adapts the policy directing this search. An outer controller uses search outcomes and VALID-monitor feedback to revise search allocation and proposal preferences, retaining or rolling back each update after validation. Across five datasets and three LLM backbones, RSI-on improves 46 of 48 reported endpoint metrics over RSI-off; the five Q3.5-397B comparisons use 8.2–77.8% fewer input tokens. Factor analyses find useful window–encoder combinations, strong descendants especially from crossover, and gains from combining applicable experts.