Precision Tracked Transformer via Kalman Filtering, Kriging and Process Noise

In three linesBayesian Filtering Transformer (BFT) integrates uncertainty handling into Transformers via Kalman filtering and kriging. Attention becomes precision-weighted kriging, residual connections become adaptive Kalman updates. BFT improves sequential recommendation (cold-start users) and LLM robustness on noisy data with negligible overhead.

Summary generated by Claude — human-verified