State-space & recurrent models

So wird trainiert

Sequence modeling losses are paired with recurrence or structured state-space layers. Parallel scans or related formulations can make training more parallel than a naive step-by-step RNN.

So läuft die Inferenz

Decode maintains a bounded recurrent state per layer. Hybrid architectures may alternate state-space, convolution, and attention layers to recover capabilities each mechanism handles well.

Stärken

• •Linear-time sequence processing in the core recurrence
• •Compact streaming state at inference
• •Natural fit for long or continuously arriving sequences

Zielkonflikte

• •Compressed state can lose precise distant details
• •Tooling and optimized kernels may be less mature than standard attention
• •Architecture-level complexity claims must be verified end to end

Geeignet, wenn

• Streaming or long-sequence efficiency is a first-order requirement
• The target runtime has optimized kernels
• Recall, quality, and throughput are benchmarked on the real workload

Vermeiden oder hinterfragen, wenn

• Exact arbitrary retrieval from long context is assumed without testing
• The serving stack cannot exploit the architecture
• A mature Transformer deployment already meets the budget