> ML_LITERATURE // GU-2023-MAMBA-LINEAR-TIME-SEQUENCE-MODELING-SELECTIVE-STATE-SPACES_v1.0
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Albert Gu, Tri Dao · arXiv preprint (2023)
seminal-architecture2023industry-standardthirdPartyReproduced
Principal Contribution
Introduced time-varying selection mechanisms into Structured State Space models (S4) with a hardware-aware parallel associative scan, delivering 5x higher inference throughput and linear O(N) scaling.
Operational Relevance
Directly guides architectural decisions, alignment strategy, and serving infrastructure for task-text-generation, task-feature-extraction.
Assumptions
- Empirical distribution regularity holds and target domain adheres to pretraining linguistic/visual support
Limitations
- Resource scaling, inference memory requirements, and alignment robustness vary with model size and hardware topology