Coleman Hooper — accepted AI-conference papers

2026

LoSA: Locality Aware Sparse Attention in Diffusion Language Models

Haocheng Xi, Harman Singh, Yuezhou Hu, Coleman Hooper, Rishabh Tiwari, Aditya Tomar +6

ICML 2026poster

Block-wise diffusion language models (DLMs) generate multiple tokens in parallel, offering a promising alternative to autoregressive decoding. However, their inference efficiency remains bottlenecked by memory-bound attention in long-context scenarios. Naïve sparse attention is ineffective for DLMs …

Cited by 0 Source ↗Scholar ↗

2026

Residual Context Diffusion Language Models

Yuezhou Hu, Harman Singh, Monishwaran Maheswaran, Haocheng Xi, Coleman Hooper, Jintao Zhang +7

ICML 2026poster

Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to purely autoregressive language models because they can decode multiple tokens in parallel. However, state-of-the-art block-wise dLLMs rely on a ``remasking" mechanism that decodes only the most confident tokens and di…

Cited by 0 Source ↗Scholar ↗