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cs.LG2026
GradLev: Token-Parallel Test-Time Training Via Costate Prediction
Bo Liu, Qiang Liu
Test-time training (TTT) allows a model to improve its predictions at inference time by updating weights after every observed token. However, sequential gra- dient writes make para…
cs.LG2026
Maglev: Sliding Recurrent Memory
Bo Liu, Qiang Liu
We introduce \ours{}, a recurrent Transformer architecture with fixed-size memory that generalizes sliding-window attention while remaining parallelizable during training. \ours{}…
cs.CL2026
Gumbel Distillation for Parallel Text Generation
Chi Zhang, Xixi Hu, Bo Liu +1
The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods. However, these non-AR models often sacrifice generation qua…