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20242026
most citedTowards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective

1 citations · 1 across the 9 of their papers we have counts for

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cs.CL2026

AsyncLane: Decoupling Refinement from Advancement in Diffusion Language Model Decoding

Yingxuan Ren, Yuxuan Lou, Yong Liu +4

Block-wise semi-autoregressive decoding is the standard inference paradigm for diffusion large language models (DLMs), but it imposes a strict dependency between blocks: the next b…

cs.CL2026

How Does Personalized Memory Shape LLM Behavior? Benchmarking Rational Preference Utilization in Personalized Assistants

Xueyang Feng, Weinan Gan, Xu Chen +2

Large language model (LLM)-powered assistants have recently integrated memory mechanisms that record user preferences, leading to more personalized and user-aligned responses. Howe…