DeepSeek 的 Engram 模块实现了 Tokenizer 无关性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-03 04:00

研究人员在 DeepSeek 的原始设计基础上，为大型语言模型开发了一个 Tokenizer 无关的 Engram 模块。新方法用多项式哈希取代了基于 XOR 的哈希，创建了一个联合嵌入空间，允许 Engram 嵌入在具有不同 Tokenizer 的模型之间重用。此修改实现了字节等价 Token 序列的哈希等价性，在保持可比性能的同时提高了 Engram 嵌入的可重用性。 AI

影响增强了大型语言模型中记忆模块的可重用性，可能降低训练成本并提高模型适应性。

排序理由该集群描述了对学术论文中提出的现有 AI 模块的修改。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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Newsworthiness bucket: Tool; 该集群描述了对学术论文中提出的现有 AI 模块的修改。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CL TIER_1 (SL) · Jia Peng Lim, Hai Leong Chieu · 2026-08-03 04:00

Tokenizer-Agnostic Engram Module

arXiv:2607.29065v1 Announce Type: new Abstract: Deepseek's Engram, a conditional memory module, was introduced to trade-off storage versus reasoning in large language models. However, the module relies on token-level $N$-gram hashing for Engram embedding lookup, introducing a tig…

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Tokenizer-Agnostic Engram Module

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