English(EN) On the Recall Scaling Laws in Mamba: A Theoretical and Mechanistic Study via Hashing

新理论表明 Mamba 模型使用哈希进行召回

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一个名为召回缩放定律的理论框架，用于分析 Mamba 模型的联想召回能力。该框架受到类似 Johnson-Lindenstrauss 引理的相似性保持哈希技术的启发，揭示了 Mamba 通过隐式学习线性哈希函数来执行召回。该研究确定了 Mamba 用于召回的具体内部机制，并根据词汇量和上下文中的事实数量，预测了实现完美召回所需的模型维度。实证结果验证了这些理论发现，并深入探讨了 Mamba 的召回能力如何随着各种模型参数和架构选择而扩展。 AI

影响为 Mamba 的记忆召回提供了理论基础，可能指导未来上下文学习的架构改进。

排序理由该集群包含一篇详细介绍人工智能模型能力理论和机制研究的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Yuval Koren, Assaf Ben-Kish, Raja Giryes, Lior Wolf, Itamar Zimerman · 2026-09-10 04:00

关于 Mamba 中召回率缩放定律的研究：通过哈希进行的理论与机制研究

arXiv:2609.07681v1 Announce Type: new Abstract: Associative Recall (AR) is the cognitive ability to learn and retrieve links between items in memory. In NLP, AR is used as a benchmark for evaluating the in-context memory capacity of architectures such as Mamba, and has been found…

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关于 Mamba 中召回率缩放定律的研究：通过哈希进行的理论与机制研究

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