[2509.26432] AdaBlock-dLLM: Semantic-Aware Diffusion LLM Inference via Adaptive Block Size

Computer Science > Machine Learning arXiv:2509.26432 (cs) [Submitted on 30 Sep 2025 (v1), last revised 2 Mar 2026 (this version, v3)] Title:AdaBlock-dLLM: Semantic-Aware Diffusion LLM Inference via Adaptive Block Size Authors:Guanxi Lu, Hao Mark Chen, Yuto Karashima, Zhican Wang, Daichi Fujiki, Hongxiang Fan View a PDF of the paper titled AdaBlock-dLLM: Semantic-Aware Diffusion LLM Inference via Adaptive Block Size, by Guanxi Lu and 5 other authors View PDF Abstract:Diffusion-based large language models (dLLMs) are gaining attention for their inherent capacity for parallel decoding, offering a compelling alternative to autoregressive LLMs. Among various decoding strategies, block-wise semi-autoregressive (semi-AR) approaches are widely adopted due to their support for KV caching and their favorable accuracy-speed trade-off. However, this paper identifies two fundamental limitations in the conventional semi-AR decoding approach that applies a fixed block size: i) late decoding overhead, where the unmasking of high-confidence tokens outside the current block is unnecessarily delayed, and ii) premature decoding error, where low-confidence tokens inside the current block are committed too early, leading to incorrect tokens. This paper presents the first systematic investigation challenging the fixed block size setting in semi-AR decoding. Through a statistical analysis of confidence dynamics during the denoising process, we identify a volatility band (VB) region during dLLM de...