Unverified50% confidenceFactExact time
State space models (SSMs) such as the Mamba series follow the same recurrent paradigm: a fixed state cannot losslessly retain all historical details, and earlier information gets overwritten by subsequent updates.
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedState Space Models (SSMs) pull the direction back toward fixed-size recurrent memory, but use a different state structure and update rules.77% similarVerifiedState space models (SSMs) such as Mamba abandon the attention mechanism and use recurrent structures to achieve linear time complexity sequence modeling.76% similarVerifiedMamba 等基于状态空间模型（SSM）的架构以线性复杂度实现序列建模，更适合实时推理76% similarVerifiedState space models represented by Mamba compress historical information by maintaining a fixed-size hidden state, with both time and space complexity of O(1) during inference.74% similarUnverifiedDuring inference, Mamba can run in pure recurrent mode with constant memory usage.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1040232API
curl https://kongchang.com/api/v1/knowledge/claims/1040232MCP
get_claim(id=1040232)