Unverified50% confidenceFactExact time
Linear attention or SSM layers can process long sequences with near-linear complexity, but they are often weaker than full attention on precise recall and complex reasoning.
1
Sources
50%
Confidence
Long-term
Relevance
10/27/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedSSMs have a linear time complexity advantage in processing long sequences, whereas the attention computation complexity of standard Transformers is quadratic with respect to sequence length.79% similarUnverifiedHSTU uses a linear-complexity attention variant, so processing behavior sequences longer than several thousand does not result in quadratic growth in computational cost.78% similarUnverifiedSparse attention reduces computational complexity to near-linear by having each token attend only to a subset of the sequence.76% similarUnverifiedKimi delta attention is an improvement in the direction of linear attention, replacing explicit caching of the full sequence with incremental state updates, reducing memory complexity from growing with length to nearly constant.73% similarUnverifiedLinear attention, sparse attention, and State Space Models (such as Mamba) can reduce computational complexity to linear levels, opening the door to million-token context windows.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1105458API
curl https://kongchang.com/api/v1/knowledge/claims/1105458MCP
get_claim(id=1105458)