arXiv Machine Learning August 27, 2026 By Morteza Sarafyazd

The Von-Neumann State-Space Transformer for neural decoding

Read the original on arXiv Machine Learning →

The paper introduces the Von‑Neumann State‑Space Transformer (VN‑SST), a memory‑augmented Transformer that replaces the standard feed‑forward block with a low‑rank instruction bank. By decoding token‑specific operators from a low‑dimensional state‑space memory, VN‑SST achieves higher data‑efficiency and parameter‑efficiency on motor‑cortex neural‑decoding tasks and on small language‑model benchmarks. The model demonstrates that a compact instruction set can act as a control channel, improving performance without increasing accuracy through larger parameter counts.

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 Computation and Language

4d ago

Pretraining Latent Information Feedback Transformers with Teacher Supervision

arXiv:2609.38149v1 Announce Type: new Abstract: Transformer language models (LMs) are feed-forward: deep-layer representations are never fed back to shallower layers, and the only pathway for informa...

By Dor Tirosh, Ido Amos, Mor Geva

llms

arXiv AI

Aug 18

Decode-Branch Transformers: Decoupling the Primary Prefill Path from Additional Decode Computation

arXiv:2608. 12385v2 Announce Type: replace Abstract: As large language models serve ever more requests, cumulative inference cost is growing relative to the one-time cost of training.

By Liming Liu, Mingze Wang, Tuo Zhao

llms rag efficiency

arXiv Machine Learning

Sep 21

Trading Depth for Time in Recurrent Transformers

The paper investigates whether extra computation in recurrent Transformers should be allocated to more temporal steps or greater physical depth. Using Latent Recurrent Transformers (LRTs), the authors insert a latent thought token between vocabulary tokens, allowing each token to pass through the same $L$ layers twice while sharing parameters. Experiments on 16‑ and 20‑layer mixture‑of‑experts NanoChat backbones show that a single thought token brings a shallower model within 0.006–0.004 bits per byte of a double‑depth counterpart, recovering 67–81% of the improvement with roughly 48% fewer parameters.

By Zeyi Huang, Xuehai He, Yong Jae Lee, Yelong Shen

llms

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 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

One email a morning, machine-written

Related stories

Full-bandwidth transformer

Pretraining Latent Information Feedback Transformers with Teacher Supervision

Decode-Branch Transformers: Decoupling the Primary Prefill Path from Additional Decode Computation

Trading Depth for Time in Recurrent Transformers

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

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