arXiv:2606. 01774v1 Announce Type: cross Abstract: Autoregressive (AR) large language models (LLMs) have achieved broad practical success, but sequential decoding remains a key bottleneck for low-latency deployment.
By Yuchen Zhu, Jing Shi, Chongjian Ge, Hao Tan, Yiran Xu, Wanrong Zhu, Jason Kuen, Koustava Goswami, Rajiv Jain, Yongxin Chen, Molei Tao, Jiuxiang Gu
arXiv:2610.01428v1 Announce Type: cross
Abstract: Generalization in large language models (LLMs) is the ability to produce consistent and semantically stable outputs when the same input is expressed...
By Nagham Omar, Mahmoud Jabarin, Maya Rozenshtein, Rom Himelstein, Avi Mendelson, Amit LeVi
arXiv:2606. 09635v1 Announce Type: cross Abstract: Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference-time adaptation.
By Hankun Lin, Ruqi Zhang
arXiv:2607. 23067v1 Announce Type: cross Abstract: Contrastive decoding methods such as DoLa improve the factuality of Large Language Models (LLMs) by contrasting the output distributions of mature and premature layers.
By Yusuke Sakai, Natthawut Kertkeidkachorn, Kiyoaki Shirai
arXiv:2506. 07406v3 Announce Type: replace-cross Abstract: Understanding the internal representations of large language models (LLMs) is a central challenge in interpretability research.
By Yifan Luo, Zhennan Zhou, Bin Dong
The paper introduces a method to improve audio‑visual speech recognition by applying contrastive decoding (CD) that contrasts audio‑only with audio‑visual conditioning within the same model. It addresses the issue of a fixed CD strength by scaling the influence adaptively for each token, using reliability signals from attention dynamics and predictive divergence. Experiments on the LRS3 dataset demonstrate consistent gains in both clean and low‑SNR scenarios.
By YoungChae Kim, Da-Hee Yang, Joon-Hyuk Chang