BlockBatch: Multi-Scale Consensus Decoding for Efficient dLLM Inference

AcceptedFindings of EMNLP 2026

Accepted to Findings of EMNLP 2026. BlockBatch is a training-free inference framework for diffusion language models that reduces denoising NFEs by 26.6% and achieves a 1.33× average speedup over Fast-dLLM.

Hy²: Accelerating Hybrid Mamba-Transformer Serving Through Adaptive GPU–PIM Co-Design

A co-design framework for serving hybrid Mamba-Transformer models on GPU–PIM heterogeneous hardware, achieving 1.51× throughput over prior art and 2.47× over GPU-only baselines while reducing tail latency to 0.61×.

HiDeS: Hierarchical Delta Sparsity for Efficient Diffusion LLM Inference

An algorithm–system co-design that exploits cross-step temporal redundancy at three independent granularities — layers, tokens, and columns — achieving 2.66× and 2.30× throughput over dense baselines on A100 and H100 with negligible accuracy loss.