arXiv AI
Oct 15

History Replays Itself: Unleashing Long-Context Capability in Linear Attention

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 AI
Sep 10

Kalman Delta Networks: Uncertainty-aware Associative Memory

Kalman Delta Networks (KDNs) extend linear attention models by treating associative memory as a linear–Gaussian state‑space system, enabling the Kalman filter to optimally estimate both memory state and its uncertainty. Two GPU‑friendly approximations—Diagonal KDN and Isotropic KDN—use mean‑field variational inference or a single scalar uncertainty per head, respectively, to maintain tractable uncertainty recurrences during linear‑attention scans. Experiments on 750 M and 1.3 B‑parameter models show that KDN variants consistently lower perplexity and raise downstream accuracy compared to existing linear‑attention baselines.

By Ngoc Bui, Tinglin Huang, Rex Ying
arXiv Machine Learning
Aug 31

DARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging

The paper introduces DARTS, a method for tuning decoder representations during model merging. It addresses representation bias in autoregressive decoders by using an entropy‑weighted L1 loss and a per‑position additive bias to correct errors that accumulate across token positions. Experiments on code generation, mathematical reasoning, and instruction following with Llama‑2‑7B show that DARTS improves performance over standard surgery while adding only 0.1% extra parameters.

By Aaryan Ajay Sharma, Sai Nishanth Padala, Seganrasan Subramanian