MemMamba: Rethinking Memory Patterns in State Space Model
This work addresses a fundamental bottleneck in efficient long-sequence modeling for applications like NLP and bioinformatics, offering a new paradigm rather than an incremental improvement.
The paper tackles the problem of long-range memory decay in selective state-space models like Mamba, which limits their effectiveness in ultra-long sequence modeling tasks such as natural language processing and bioinformatics. The proposed MemMamba framework integrates state summarization with attention mechanisms to alleviate forgetting while preserving linear complexity, achieving significant improvements over existing methods on benchmarks like PG19 and Passkey Retrieval with a 48% inference speedup.
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.