New MemGuard-Alpha framework detects and filters out memorized data in LLM-based financial predictions, achieving 18.57x better contamination detection than existing methods.

LLMs memorize historical financial data from training sets, creating fake predictive accuracy that fails when tested on new data, undermining quantitative trading strategies

MemGuard-Alpha uses two algorithms: a Composite Score (MCS) combining five membership inference attack methods with temporal features, and Cross-Model Disagreement detection

The MCS achieves Cohen's d = 18.57 for separating contaminated signals versus d = 0.39-1.37 using membership inference features alone

Unlike expensive retraining or information-loss-inducing anonymization, MemGuard-Alpha offers zero-cost, real-time signal filtering for practical trading applications