The paper introduces CS-MoE, a Transformer architecture that shares experts across layers to reduce inter‑layer parameter redundancy. By combining layer‑independent experts with a globally shared expert pool, CS‑MoE allows elastic control over token‑level parameter activation and computational cost. Experiments show that CS‑MoE achieves lower perplexity than equal‑scale dense Transformers while activating only 55% of parameters, and its performance scales with the number of activated experts, approaching MoE performance within a fixed FLOPs budget.
By Dian Jiao, Jiaxin Duan, Shuai Zhao, Jiabing Leng, Yiran Zhang, Feng Huang
arXiv:2608. 08888v1 Announce Type: new Abstract: Autoregressive transformers compute along two axes: horizontally across generated tokens, and vertically through model depth.
By Xi Wang, Ziyang Cai, Zheng Zhan, Harry Dong, Ying Fan, Gustavo de Rosa, Tim Pearce, John Langford
FlashLoop is a training‑free inference framework for Looped Transformers that reduces cross‑loop redundancy by employing token‑sparse updates, sparse attention, and KV‑residual quantization. It exploits observations that, as loops progress, state changes concentrate on a small token subset, attention differences are dominated by a sparse key subset, and KV residuals become amenable to low‑bit quantization. The method achieves lossless accuracy with up to 1.64× speedup and 6× KV‑cache memory reduction across several Looped Transformer models.
By Wanqi Yang, Shiwei Liu
arXiv:2608. 09468v1 Announce Type: cross Abstract: State Space Models (SSMs), as a mainstream research direction of linear Transformers, aim to achieve higher efficiency than standard Transformers in long-context modeling.
By Yu Guo, Lei Duan
arXiv:2601. 22580v2 Announce Type: replace-cross Abstract: The success of Large Language Models (LLMs) hinges on the stable training of deep Transformer architectures.
By Chao Wang, Bei Li, Jiaqi Zhang, Xinyu Liu, Yuchun Fan, Linkun Lyu, Xin Chen, Jingang Wang, Tong Xiao, Peng Pei, Xunliang Cai
arXiv:2606. 31717v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) is commonly viewed as an update-space approximation to full fine-tuning, yet this view is incomplete for self-gated Transformer feed-forward networks.
By Shuai Yuan, Sudong Cai, Bingzhi Chen, Shuyuan Zheng, Chuan Xiao, Makoto Onizuka, Rui Mao
arXiv:2502.09245v3 Announce Type: replace
Abstract: In contrast to RNNs, which compress their history into a single hidden state, Transformers can attend to all past tokens directly. However, standar...
By Gleb Gerasimov, Yaroslav Aksenov, Nikita Balagansky, Viacheslav Sinii, Daniil Gavrilov
arXiv:2411. 09816v5 Announce Type: replace Abstract: Large neural networks achieve state-of-the-art performance on many tasks, yet their sheer size hinders deployment on resource-constrained devices.
By Cem \"Uy\"uk, Mike Lasby, Mohamed Yassin, Utku Evci, Yani Ioannou
arXiv:2511. 17864v3 Announce Type: replace Abstract: Recent research has established that the impact of context in a vanilla transformer can be represented implicitly by forming a token-dependent, rank-1 patch to its MLP weights.
By Adrian Goldwaser, Michael Munn, Javier Gonzalvo, Benoit Dherin
arXiv:2607. 01218v1 Announce Type: cross Abstract: Transformers use the same forward computation stream to both predict the next token and store useful state for future token predictions.
By Giovanni Monea, Nathan Godey, Kiant\'e Brantley, Yoav Artzi
arXiv:2605.26797v2 Announce Type: replace
Abstract: We study Latent Recurrent Transformer (LRT), a lightweight augmentation of autoregressive transformers that reuses a high-level source-layer hidden...
By Zeyi Huang, Xuehai He, LiLiang Ren, Yiping Wang, Baolin Peng, Hao Cheng, Shuohang Wang, Pengcheng He, Jianfeng Gao, Yong Jae Lee, Yelong Shen
TopK-Guided is a training‑free method that improves activation sparsity for large language model inference by combining token‑level sparsity adaptation with block‑level budget allocation that accounts for block sensitivity. It addresses limitations of existing methods like TEAL, which adapts sparsity per token but lacks tight control, and WINA, which enforces a fixed sparsity across all tokens and blocks. Experiments on Llama‑2 and Llama‑3 show that TopK‑Guided consistently yields better perplexity and downstream accuracy while maintaining similar compute costs to WINA, especially at high sparsity levels.
By Mukund Agarwalla, Chih-Jen Lin