DARTree: draft trees push diffusion speculative decoding to 9.73x lossless speedup

The paper extends a pretrained autoregressive correction head from single chains to trees, training-free, for diffusion speculative decoding. Fixed-width candidate trees are scored per depth in one batch with best-first pruning. Each verification round accepts up to 12.97 tokens, up 98.6% over DFlash and 27.9% over Domino, for up to 9.73x lossless speedup across 7 benchmarks.

If the results reproduce, this is a material acceleration technique for inference serving, directly relevant to vLLM/SGLang-class stacks. There is no code repo yet, so treat it as watch-and-verify.