TraceRelay is an attention‑aligned recurrent architecture that distributes persistent representations over a rolling sequence of low‑dimensional traces. It uses right‑looking attention to form increments from lower‑layer representations, delays delivery until all attended inputs are in the causal past, and accumulates these increments with a fixed additive phase recurrence. Experiments on 36 small‑model runs show that recurrent phase inheritance yields high accuracy on Equal Repeats (up to 99.69%) but accuracy drops beyond the training range, and that the optimal size of recurrent traces varies across tasks such as Dyck closing‑type prediction and causal Most‑Freq generation.
By Sungwoo Goo, Hwi-yeol Yun, Sangkeun Jung
The paper investigates how attention dynamics evolve across recurrent depth in language models, finding that attention support stabilizes early while hidden states and outputs take longer. It proposes WISE, a training‑free method that uses full attention in early steps and then reuses the discovered sparse working set for later steps, preserving performance on multi‑hop QA tasks. Experiments show that WISE maintains quality up to 2K context, offers measurable speedups, and highlights the importance of recurrent discovery of attention support.
By Ke Wan, Chen Chen
arXiv:2610.15715v1 Announce Type: new
Abstract: Linear attention promises the efficiency needed for long-context language modelling, yet its fixed-size recurrent state must simultaneously support ong...
By Li Nanbo, Firas Laakom, Yilan Zhang, Piotr Pi\k{e}kos, Yimeng Chen, Liangyu Wang, Yifan Xu, Mateusz Ostaszewski, Dandan Guo, Wenyi Wang, Yanning Dai, Yuhui Wang, J\"{u}rgen Schmidhuber
arXiv:2609.36337v1 Announce Type: new
Abstract: Tabular foundation models achieve strong performance by conditioning on labelled examples in context, but softmax attention limits their use on large d...
By David Schnurr, Felix Sarnthein, Thomas Hofmann, Imanol Schlag
The paper introduces recirculation, an inference‑time architectural enhancement for foundation models that reduces perplexity and improves accuracy on generation and reasoning tasks without adding significant latency. Recirculation adds a specific form of recurrence, enabling the model to function as a dynamical system that tracks belief states, and is distinct from chain‑of‑thought or depth‑recurrence methods. An adaptive variant requires minimal hyperparameter tuning and achieves notable gains on the Gemma3 family, including a 23% perplexity drop and a 21% accuracy increase on GSM8k.
By Michael C. Mozer, Shoaib Ahmed Siddiqui, Danny Sawyer, Sunny Sanyal, Rosanne Liu
arXiv:2610.16301v1 Announce Type: new
Abstract: Self-attention assigns two functions to one mechanism-where to read and how to mix-at the price of O(L^2) training, an O(T) decode state, and position...
By Helong Hu (Zhanjiang Campus, Guangdong Ocean University, Zhanjiang, Guangdong, China), Hongdan Pan (Maoming Administrative Service Center, Maoming, Guangdong, China), Shuiqing Hu (China Pacific Insurance)