Interactive lesson~20 minAdvanced
State Space Models & Mamba
State space models process sequences by updating a compact hidden state. They trade full pairwise attention for linear-time recurrence-like structure.
SSMS4Mamba
Mental model
Carry a smart summary forward instead of comparing every token to every other token.
SSMs and Mamba-style models are important for long sequences, streaming, genomics, audio, and low-latency inference.
Long recall
balanced66% modeled signal
Throughput
balanced72% modeled signal
Streaming fit
balanced63% modeled signal
Concept pipeline
Build the idea in four moves
Interactive lab
Tune a streaming sequence model.
Input
Convert a token or signal slice into a state update.
Focus lens
The part that usually clicks late
Linear time
Work grows with sequence length, not length squared.
Long recall
66
Throughput
72
Streaming fit
63
Knowledge check
Why are SSMs attractive for long sequences?
Next horizon
Where this topic is headed
Mamba-2
Hybrid attention-SSM blocks
Streaming multimodal models