Reference library
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper summary
Develops state-space duality linking selective state-space models and attention variants through structured matrices. This framework motivates Mamba-2 and a faster core implementation with language-model evaluations.
Relevance to the project
Useful for choosing mathematically related but differently scheduled GPU computations. Compare operation layout, state cost, and numerical behavior under representative F workloads.
Related formulations can still differ in precision, constraints, and practical performance.
Source record
- Reference ID
- mamba2
- Archived version
- 2405.21060v1
- Summary provenance
- reference-library/papers/mamba2/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.