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近年来，研究者们开始探索替代方案，其中Mamba架构作为一种基于选择性状态空间模型的新型序列建模方法，在保持线性时间复杂度的同时实现了对长序列的高效处理。 文章从理论基础、技术实现、性能分析及应用场景等方面，详细阐述了该混合架构的特点与前景，同时分析了其在计算效率、训练稳定性等方面的挑战，并展望了未来优化方向，如动态路由机制和多模态扩展，为高效序列建模提供了新思路。 最近，（Gu & Dao，2023）提出了一种数据依赖的SSM层，并构建了一个通用的语言模型骨干，Mamba，在大规模真实数据上，Mamba在各种规模上都优于Transformer，并在序列长度上享有线性扩展性
Mamba作为一种基于选择性状态空间模型的新型序列建模架构，在长序列处理方面展现出显著的计算效率优势。 MemMamba通过"做笔记"机制突破了SSM的记忆瓶颈，在400k tokens的长序列任务中仍保持90%的准确率。这项由人大、上财和上海AI实验室联合完成的研究，不仅揭示了Mamba的记忆衰减本质，更为长序列建模提供了新范式，实现了效率与记忆能力的平衡。 什么是 Mamba 模型？ Mamba 作为 状态空间模型 (SSM) 衍生的 神经网络 架构，适用于语言建模及其他序列建模任务。Mamba 架构凭借极速推理效能与计算效率（尤其长序列场景），成为自回归 LLM 领域首个可匹敌 Transformer 的替代架构。
Mamba模型作为一种新兴的序列建模架构,以其线性复杂度和出色的性能引起了广泛关注。 本文全面介绍Mamba模型的发展历程、核心原理及其在各领域的应用进展。
我们介绍了Mamba-3，这是一个SSM模型，在三个维度上基于SSM原理进行了改进： (i) 通过梯形离散化提高质量； (ii) 通过复SSM恢复状态跟踪，从而获得新能力； (iii) 通过MIMO公式提高算术强度，从而获得更高的推理效率。 Mamba模型是一个创新的线性时间序列建模方法，巧妙地结合了递归神经网络（RNN）和卷积神经网络（CNN）的特点，解决了处理长序列时的计算效率问题。
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