long-context memory 1model efficiency 1retrieval-augmented generation 1self-distillation 1transformer depth division 1
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cs.CL2026
Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory
Hanzuo Liu, Xuan Qi, Chunyu Liu +6
The paper proposes CoMem, a method that stores intermediate transformer layer states as memory to enable efficient long‑context retrieval, showing that using lower‑mid layers for c…
cs.CL2026
VoidPadding: Let [VOID] Handle Padding in Masked Diffusion Language Models so that [EOS] Can Focus on Semantic Termination
Chunyu Liu, Zhengyang Fan, Kaisen Yang +1
MDLMs generate text by denoising a preallocated masked response canvas, making response-length modeling central to instruction tuning. Existing MDLMs often inherit the autoregressi…
cs.CL2026
Improving Sampling for Masked Diffusion Models via Information Gain
Kaisen Yang, Jayden Teoh, Kaicheng Yang +2
Masked Diffusion Models (MDMs) enable flexible decoding orders, yet existing samplers remain largely greedy, selecting locally certain tokens without accounting for their downstrea…