arXiv Computation and Language
Sep 4

Lngram v2: Latent N-Gram Memory with Interpretable Discrete Representations

Lngram v2 introduces a latent N‑gram memory system that decouples memory routes, memory dimension, and backbone width, enabling scalable memory capacity for transformers. It employs context‑aware grouped‑query attention, a zero‑value sink, and counterfactual surrogate gradients to improve readout selectivity and routing trainability while preserving hard discrete addressing. Experiments on vision‑language models up to 30B parameters show consistent performance gains, reduced memory parameters, and stable semantic structure in the discrete IDs.

By Yunao Zheng, Bin Wen, Xiaojie Wang
arXiv AI
Jul 14

Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models

arXiv:2601. 07372v2 Announce Type: replace-cross Abstract: While Mixture-of-Experts (MoE) scales capacity via conditional computation, Transformers lack a native primitive for knowledge lookup, forcing them to inefficiently simulate retrieval through computation.

By Xin Cheng, Rui Tian, Wangding Zeng, Damai Dai, Qinyu Chen, Bingxuan Wang, Zhenda Xie, Kezhao Huang, Xingkai Yu, Chengqi Deng, Shangyan Zhou, Chenggang Zhao, Zhewen Hao, Yukun Li, Han Zhang, Zhengyan Zhang, Yixu Wei, M. Y Xu, Huishuai Zhang, Dongyan Zhao, Wenfeng Liang
arXiv AI
Jun 4

L$^3$: Large Lookup Layers

arXiv:2601. 21461v3 Announce Type: replace-cross Abstract: Modern sparse language models typically achieve sparsity through Mixture-of-Experts (MoE) layers, which dynamically route tokens to dense MLP "experts.

By Albert Tseng, Christopher De Sa