新研究通过深度递归和输入重塑改进状态空间模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

一篇新的研究论文介绍了两种增强状态空间模型（SSM）的方法，使其更高效、性能更好。第一种方法是深度递归，通过多次迭代一个较小的模型来减少SSM的内存占用，同时不牺牲性能。第二种方法通过调整时间粒度和特征维度来优化呈现给模型的信息，从而提高性能。这些技术在LRU、S5、LinOSS和LrcSSM等几种SSM架构上进行了测试，并显示出持续的益处。 AI

影响提高了状态空间模型的效率和性能，可能使其在边缘部署方面与LLM更具竞争力。

排序理由研究论文，详细介绍了改进现有模型的新颖方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · M\'onika Farsang, Ramin Hasani, Daniela Rus, Radu Grosu · 2026-10-02 04:00

重塑与递归：通过输入重塑和深度递归改进 SSM

arXiv:2605.16048v2 Announce Type: replace-cross Abstract: State Space Models (SSMs) are increasingly deployed in the Edge because they offer, at comparable performance, a smaller memory/training/inference footprint, compared to Large Language Models (LLMs). These three advantages…

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重塑与递归：通过输入重塑和深度递归改进 SSM

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