arXiv Machine Learning
1d ago

Shallow Encoding Emits Deep Memories

arXiv:2611.06084v1 Announce Type: new Abstract: Hybrid language models interleave a few softmax-attention layers with many linear-attention or state-space layers, so every layer keeps a memory of ear...

By Mingyuan Ma, Yangmin Li, Harris Nover, Zheyu Fu, Po-Han Huang, Alex Settle, Ashwin Nanjappa
arXiv Machine Learning
Sep 14

Fixed State, Long Reach: What a Constant-Size Cache Buys Block Diffusion at Scale

The paper investigates how block‑diffusion language models can use a constant‑size cache to enable efficient parallel decoding. By employing sequence mixers that summarize completed blocks into a reusable state and a block‑causal training objective, the authors pretrain three 3B block‑diffusion denoisers (attention, Mamba, and hybrid) on 300 B tokens. The resulting state‑space cache remains O(1) in memory and latency regardless of context length, yielding significant speed‑up and memory savings compared to traditional attention‑based caches, especially at very long sequences.

By Vaibhav Singh, Pierre-Andr\'e No\"el, Torsten Scholak, Eugene Belilovsky, Oleksiy Ostapenko
arXiv Machine Learning
Oct 2

Low-Discrepancy Dither for Quantized Recurrent State Caches

The paper investigates rounding strategies for low‑precision recurrent state caches in Mamba‑style and hybrid language models. It shows that a deterministic golden‑ratio Weyl dither consistently yields quantized models closer to full precision than stochastic rounding, across various models, storage formats, and long decoding horizons, without extra cost. In contrast, round‑to‑nearest can appear effective in short tests but degrades over long generations due to error accumulation.

By Snigdha Chandan Khilar
arXiv Machine Learning
Aug 31

DAMP: Decay-Aware Mixed-Precision Recurrent-State Quantization

The paper introduces DAMP, a decay‑aware mixed‑precision quantization scheme for recurrent‑state representations in GDN and KDA language models. By identifying high‑risk channels through quantization‑error energy and decay persistence, DAMP stores these channels at higher precision while compressing the rest to INT8, achieving a 9.9‑bit average precision. Experiments on Qwen3.6‑35B and Kimi‑Linear‑48B show a 69.1% reduction in recurrent‑state storage, up to 2.01× faster state‑update kernels, and up to 10.9% lower full‑model TPOT while preserving accuracy close to the FP32 baseline.

By Tao Zhang, Jianchao Tan, Pingwei Sun, Yanqi Yu, Zixu Jiang, Yuchen Xie, Xunliang Cai, Ziqian Zeng