Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models
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| Natura: | Preprint |
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2026
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| author | Cheng, Xin Zeng, Wangding Dai, Damai Chen, Qinyu Wang, Bingxuan Xie, Zhenda Huang, Kezhao Yu, Xingkai Hao, Zhewen Li, Yukun Zhang, Han Zhang, Huishuai Zhao, Dongyan Liang, Wenfeng |
| author_facet | Cheng, Xin Zeng, Wangding Dai, Damai Chen, Qinyu Wang, Bingxuan Xie, Zhenda Huang, Kezhao Yu, Xingkai Hao, Zhewen Li, Yukun Zhang, Han Zhang, Huishuai Zhao, Dongyan Liang, Wenfeng |
| contents | 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. To address this, we introduce conditional memory as a complementary sparsity axis, instantiated via Engram, a module that modernizes classic $N$-gram embedding for O(1) lookup. By formulating the Sparsity Allocation problem, we uncover a U-shaped scaling law that optimizes the trade-off between neural computation (MoE) and static memory (Engram). Guided by this law, we scale Engram to 27B parameters, achieving superior performance over a strictly iso-parameter and iso-FLOPs MoE baseline. Most notably, while the memory module is expected to aid knowledge retrieval (e.g., MMLU +3.4; CMMLU +4.0), we observe even larger gains in general reasoning (e.g., BBH +5.0; ARC-Challenge +3.7) and code/math domains~(HumanEval +3.0; MATH +2.4). Mechanistic analyses reveal that Engram relieves the backbone's early layers from static reconstruction, effectively deepening the network for complex reasoning. Furthermore, by delegating local dependencies to lookups, it frees up attention capacity for global context, substantially boosting long-context retrieval (e.g., Multi-Query NIAH: 84.2 to 97.0). Finally, Engram establishes infrastructure-aware efficiency: its deterministic addressing enables runtime prefetching from host memory, incurring negligible overhead. We envision conditional memory as an indispensable modeling primitive for next-generation sparse models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07372 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models Cheng, Xin Zeng, Wangding Dai, Damai Chen, Qinyu Wang, Bingxuan Xie, Zhenda Huang, Kezhao Yu, Xingkai Hao, Zhewen Li, Yukun Zhang, Han Zhang, Huishuai Zhao, Dongyan Liang, Wenfeng Computation and Language Artificial Intelligence 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. To address this, we introduce conditional memory as a complementary sparsity axis, instantiated via Engram, a module that modernizes classic $N$-gram embedding for O(1) lookup. By formulating the Sparsity Allocation problem, we uncover a U-shaped scaling law that optimizes the trade-off between neural computation (MoE) and static memory (Engram). Guided by this law, we scale Engram to 27B parameters, achieving superior performance over a strictly iso-parameter and iso-FLOPs MoE baseline. Most notably, while the memory module is expected to aid knowledge retrieval (e.g., MMLU +3.4; CMMLU +4.0), we observe even larger gains in general reasoning (e.g., BBH +5.0; ARC-Challenge +3.7) and code/math domains~(HumanEval +3.0; MATH +2.4). Mechanistic analyses reveal that Engram relieves the backbone's early layers from static reconstruction, effectively deepening the network for complex reasoning. Furthermore, by delegating local dependencies to lookups, it frees up attention capacity for global context, substantially boosting long-context retrieval (e.g., Multi-Query NIAH: 84.2 to 97.0). Finally, Engram establishes infrastructure-aware efficiency: its deterministic addressing enables runtime prefetching from host memory, incurring negligible overhead. We envision conditional memory as an indispensable modeling primitive for next-generation sparse models. |
| title | Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2601.07372 |