@inproceedings{cheng-etal-2026-conditional,
title = "Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models",
author = "Cheng, Xin and
Zeng, Wangding and
Dai, Damai and
Chen, Qinyu and
Wang, Bingxuan and
Xie, Zhenda and
Huang, Kezhao and
Yu, Xingkai and
Hao, Zhewen and
Zhang, Han and
Li, Yu-Kun and
Zhang, Huishuai and
Zhao, Dongyan and
Liang, Wenfeng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.226/",
pages = "4968--4990",
ISBN = "979-8-89176-390-6",
abstract = "Mixture-of-Experts (MoE) scales capacity via conditional computation, but Transformers lack a native knowledge lookup primitive. We introduce conditional memory, instantiated via Deep Sparse Embedding (DSE), which indexes a massive embedding table using local n-grams for retrieval. We formalize sparsity allocation problem{---}how to split a fixed parameter budget between MoE experts and DSE memory{---}and find a U-shaped scaling law that identifies an optimal balance. Scaling to 27B parameters, DSE outperform an iso-parameter and iso-FLOPs MoE baseline across knowledge and reasoning benchmarks, and achieve markedly stronger long-context performance. Mechanistic analyses show that DSE offloads early-layer static recall into memory, freeing effective depth and attention for higher-level reasoning. DSE is also infrastructure-efficient: its deterministic hashing enables offloading massive parameters into host memory during inference with negligible throughput overhead."
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%0 Conference Proceedings
%T Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models
%A Cheng, Xin
%A Zeng, Wangding
%A Dai, Damai
%A Chen, Qinyu
%A Wang, Bingxuan
%A Xie, Zhenda
%A Huang, Kezhao
%A Yu, Xingkai
%A Hao, Zhewen
%A Zhang, Han
%A Li, Yu-Kun
%A Zhang, Huishuai
%A Zhao, Dongyan
%A Liang, Wenfeng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F cheng-etal-2026-conditional
%X Mixture-of-Experts (MoE) scales capacity via conditional computation, but Transformers lack a native knowledge lookup primitive. We introduce conditional memory, instantiated via Deep Sparse Embedding (DSE), which indexes a massive embedding table using local n-grams for retrieval. We formalize sparsity allocation problem—how to split a fixed parameter budget between MoE experts and DSE memory—and find a U-shaped scaling law that identifies an optimal balance. Scaling to 27B parameters, DSE outperform an iso-parameter and iso-FLOPs MoE baseline across knowledge and reasoning benchmarks, and achieve markedly stronger long-context performance. Mechanistic analyses show that DSE offloads early-layer static recall into memory, freeing effective depth and attention for higher-level reasoning. DSE is also infrastructure-efficient: its deterministic hashing enables offloading massive parameters into host memory during inference with negligible throughput overhead.
%U https://aclanthology.org/2026.acl-long.226/
%P 4968-4990
Markdown (Informal)
[Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models](https://aclanthology.org/2026.acl-long.226/) (Cheng et al., ACL 2026)
ACL
- Xin Cheng, Wangding Zeng, Damai Dai, Qinyu Chen, Bingxuan Wang, Zhenda Xie, Kezhao Huang, Xingkai Yu, Zhewen Hao, Han Zhang, Yu-Kun Li, Huishuai Zhang, Dongyan Zhao, and Wenfeng Liang. 2026. Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4968–4990, San Diego, California, United States. Association for Computational Linguistics.