arXiv Machine Learning July 9, 2026 By Ligong Han, Kai Xu, Hao Wang, Akash Srivastava

SNLP: Layer-Parallel Inference via Structured Newton Corrections

Read the original on arXiv Machine Learning →

arXiv:2605. 17842v3 Announce Type: replace Abstract: Autoregressive language models execute Transformer layers sequentially, creating a latency bottleneck that is not removed by conventional tensor or pipeline parallelism.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Aug 11

Full-bandwidth transformer

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

llms rag efficiency

arXiv AI

Jul 14

Turbo Connection: Reasoning as Information Flow from Higher to Lower Layers

arXiv:2602. 17993v2 Announce Type: replace-cross Abstract: Complex problems, whether in math, logic, or planning, are solved by humans through a sequence of steps where the result of one step informs the next.

By Mohan Tang, Sidi Lu

llms fine-tuning benchmarks

Hugging Face Trending Papers

Jul 9

Hidden Decoding at Scale: Latent Computation Scaling for Large Language Models

Scaling Large Language Models (LLMs) has been driven mainly by enlarging the Transformer backbone, but for an already-strong model this requires another round of costly pretraining. We study whether an existing backbone can keep improving by allocating more computation to each token while leaving the Transformer backbone fixed.

llms rag

arXiv Machine Learning

Sep 25

FlashLoop: Fast and Memory-Efficient Looped Transformers via Lazy Updates

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

llms efficiency

arXiv Machine Learning

Sep 2

Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior

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

llms

arXiv Machine Learning

Jun 10

PRISM: Parallel Residual Iterative Sequence Model

arXiv:2602. 10796v3 Announce Type: replace Abstract: Generative sequence modeling faces a fundamental tension between the expressivity of Transformers and the efficiency of linear sequence models.

By Jie Jiang, Ke Cheng, Xin Xu, Mengyang Pang, Tianhao Lu, Jiaheng Li, Yue Liu, Yuan Wang, Jun Zhang, Huan Yu, Zhouchen Lin

llms efficiency

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Related stories

Full-bandwidth transformer

Turbo Connection: Reasoning as Information Flow from Higher to Lower Layers

Hidden Decoding at Scale: Latent Computation Scaling for Large Language Models

FlashLoop: Fast and Memory-Efficient Looped Transformers via Lazy Updates

Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior

PRISM: Parallel Residual Iterative Sequence Model