Reference library
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper summary
Mamba makes state-space updates input-dependent so sequence processing can select what to retain or forget. A hardware-aware recurrent implementation achieves linear sequence scaling and is evaluated across several modalities.
Relevance to the project
A comparison for efficient stateful computation inside F and history processing. Test content sensitivity, memory, and intrinsic dynamics independently.
Language performance does not establish stability of an untrained autonomous recurrent network.
Source record
- Reference ID
- mamba
- Archived version
- 2312.00752v2
- Summary provenance
- reference-library/papers/mamba/metadata.json · summary
- Review status
- initial orientation summary; not a critical full-paper review
- Reading scope
- Selected abstract/opening and available closing discussion/conclusion passages in the preserved text extract; broader project notes provide context. No reproduction of experiments, exhaustive methods review, or equation/figure audit.