# WaveFront Decoding

The work introduces WaveFront Decoding (WFD), a training-free self-speculative decoding framework for looped language models that exploits two properties of these architectures, namely that intermediate recurrence outputs provide effective draft predictions and that weight sharing lets token states at different positions and recurrence depths be processed in one batched recurrent-block call, organizing these mixed-depth states into a diagonal wavefront so that drafting and verification proceed concurrently within the same recurrent calls while rejected drafts are corrected using full-depth predictions; across six Spec-Bench task categories it achieves 2.42x speedup on Ouro-2.6B and 3.54x on Huginn-3.5B over autoregressive decoding, rising to 4.81x on Huginn-3.

The work introduces WaveFront Decoding (WFD), a training-free self-speculative decoding framework for looped language models that exploits two properties of these architectures, namely that intermediate recurrence outputs provide effective draft predictions and that weight sharing lets token states at different positions and recurrence depths be processed in one batched recurrent-block call, organizing these mixed-depth states into a diagonal wavefront so that drafting and verification proceed concurrently within the same recurrent calls while rejected drafts are corrected using full-depth predictions; across six Spec-Bench task categories it achieves 2.42x speedup on Ouro-2.6B and 3.54x on Huginn-3.5B over autoregressive decoding, rising to 4.81x on Huginn-3.