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system architectureChapter 2arXiv · 2023

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Albert Gu (CMU), Tri Dao (Princeton)

Abstract

We present Mamba, a selective state space model that achieves linear-time sequence modeling. Mamba selectively propagates or forgets information based on content, enabling efficient long-sequence processing.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

Mamba's linear-time sequence modeling is a potential solution to the quadratic attention bottleneck that limits context length. For Zone III agents requiring very long contexts, Mamba-based architectures may provide a more efficient alternative to transformer attention.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →Selective state space model
  • →Linear-time sequence modeling
  • →Content-based information selection

Topics

state space modelsefficient attentionlong sequencesarchitecture