---
id: "AEB-025"
category: "architecture_fundamentals"
confidence: 0.94
date: "2026-02-15"
source_file: "Agentic OS Blueprint Research Synthesis (Active Memory and SSM section)"
---

# AEB-025

Structured State-Space Models (SSMs) achieve linear-time O(N) inference complexity by representing sequences as parameterized recurrences from dynamical system theory -- unlike Transformers that 'remember everything' stochastically, SSMs selectively update hidden state based on input, learning to remember only what matters through input-dependent parameters

## Relationships

