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cs.CL2026
Survival-Guided Length Control for Efficient Diffusion Language Models
Ivan Kobyzev, Abbas Ghaddar, Yufei Cui
Diffusion language models (DLMs) generate text by iteratively denoising masked sequences, but standard decoding either fixes the sequence length or relies on ad hoc stopping rules,…
cs.CL2026
BOSCH: Black-Box Binary Optimization for Short-Context Attention-Head Selection in LLMs
Abbas Ghaddar, Ivan Kobyzev, Boxing Chen +1
Post-training hybridization of large language models (LLMs) often replaces quadratic self-attention with sliding-window attention (SWA) to reduce KV cache usage and improve latency…
cs.CL2026
Top 10 Open Challenges Steering the Future of Diffusion Language Model and Its Variants
Yunhe Wang, Kai Han, Huiling Zhen +13
The paradigm of Large Language Models (LLMs) is currently defined by auto-regressive (AR) architectures, which generate text through a sequential ``brick-by-brick'' process. Despit…