Lattice: A Confidence-Gated Hybrid System for Uncertainty-Aware Sequential Prediction with Behavioral Archetypes

Abstract: We introduce Lattice, a hybrid sequential prediction system that conditionally activates learned behavioral structure using binary confidence gating. The system clusters behavior windows into behavioral archetypes and uses binary confidence gating to activate archetype-based scoring only when confidence exceeds a threshold, falling back to baseline predictions when uncertain. We validate Lattice on recommendation systems (MovieLens), scientific time-series (LIGO), and financial markets, using LSTM and transformer backbones. On MovieLens with LSTM, Lattice achieves +31.9% improvement over LSTM baseline in HR@10 (p < 3.29 x 10^-25, 30 seeds), outperforming transformer baselines by 109.4% over SASRec and 218.6% over BERT4Rec. On LIGO and financial data, the system correctly refuses archetype activation when distribution shift occurs - a successful outcome demonstrating confidence gating prevents false activation. On transformer backbones, Lattice provides 0.0% improvement (neutral, no degradation), gracefully deferring when structure is already present. This bidirectional validation - activating when patterns apply, refusing when they don't, and deferring when redundant - supports confidence gating as a promising architectural principle for managing epistemic uncertainty in safety-critical applications.