arXiv cs.LGPaper
Unlocking Lossless Speedups in LLMs via Discrete Diffusion
This solves a real problem in LLM inference: autoregressive generation is sequential and slow. Parallel token generation without quality loss is valuable for latency-critical applications. If this holds up in production, it becomes competitive with speculative decoding but simpler. Test it on your inference pipeline if you're constrained by generation speed.