MemMamba: Rethinking Memory Patterns in State Space Model

2025-09-28

AI Summary

Long-sequence modeling faces a fundamental trade-off between computational efficiency (O(n) complexity) and long-range memory retention: while Mamba achieves linear-time complexity, its state memory decays exponentially, causing distant information loss. This work identifies the root cause of this decay and proposes MemMamba—a novel architecture integrating state summarization, cross-layer state recalibration, and inter-token sparse attention—to preserve long-term dependencies without sacrificing efficiency. MemMamba maintains O(n) training and O(1) inference complexity. We further introduce horizontal–vertical memory fidelity metrics to quantitatively evaluate long-range information retention. On benchmarks including PG19 and Passkey Retrieval, MemMamba significantly outperforms Mamba variants and Transformers, achieving a 48% inference speedup. To our knowledge, it is the first approach to break the efficiency–memory trade-off both theoretically and architecturally.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

With the explosive growth of data, long-sequence modeling has become increasingly important in tasks such as natural language processing and bioinformatics. However, existing methods face inherent trade-offs between efficiency and memory. Recurrent neural networks suffer from gradient vanishing and explosion, making them hard to scale. Transformers can model global dependencies but are constrained by quadratic complexity. Recently, selective state-space models such as Mamba have demonstrated high efficiency with O(n) time and O(1) recurrent inference, yet their long-range memory decays exponentially. In this work, we conduct mathematical derivations and information-theoretic analysis to systematically uncover the memory decay mechanism of Mamba, answering a fundamental question: what is the nature of Mamba's long-range memory and how does it retain information? To quantify key information loss, we further introduce horizontal-vertical memory fidelity metrics that capture degradation both within and across layers. Inspired by how humans distill and retain salient information when reading long documents, we propose MemMamba, a novel architectural framework that integrates state summarization mechanism together with cross-layer and cross-token attention, which alleviates long-range forgetting while preserving linear complexity. MemMamba achieves significant improvements over existing Mamba variants and Transformers on long-sequence benchmarks such as PG19 and Passkey Retrieval, while delivering a 48% speedup in inference efficiency. Both theoretical analysis and empirical results demonstrate that MemMamba achieves a breakthrough in the complexity-memory trade-off, offering a new paradigm for ultra-long sequence modeling.

Problem

• Addresses exponential memory decay in selective state-space models like Mamba
• Quantifies information loss using horizontal-vertical memory fidelity metrics
• Proposes MemMamba framework to alleviate long-range forgetting with linear complexity

Innovation

• Integrates state summarization for memory retention
• Combines cross-layer attention to reduce forgetting
• Preserves linear complexity while enhancing long-range memory