AWhat changed
Made state-space parameters depend on the input and used a hardware-aware recurrent scan to selectively retain sequence information in an attention-free model.
Scientific scope
Made state-space parameters depend on the input and used a hardware-aware recurrent scan to selectively retain sequence information in an attention-free model. The candidate covers the general sequence architecture and non-biological language/audio evaluations. Linear sequence scaling is not a claim of universal Transformer superiority; life-science applications are excluded.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The candidate covers the general sequence architecture and non-biological language/audio evaluations. Linear sequence scaling is not a claim of universal Transformer superiority; life-science applications are excluded.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
- Mamba: Linear-Time Sequence Modeling with Selective State Spaces
- State-spaces team: Mamba implementation
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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