TraceQuery: learning what to recompute from full-denoising trajectory in diffusion language models

Researchers propose TraceQuery, a method for efficient inference in diffusion language models that formulates dLLM inference as trajectory-guided querying. The approach leverages the full-denoising trajectory to determine which token representations to recompute, reducing inference cost while maintaining accuracy by keeping generation close to the full-denoising trajectory.