The Spectral Cognitive Resonator: A Dynamic Architecture for Agent Memory, Routing, and Self-Adaptation

Current AI agent architectures either use static retrieval (RAG) or unstructured agent loops (ReAct, Reflexion) with no formal guarantees on memory utilization, routing optimality, or safe self-adaptation. We introduce the Spectral Cognitive Resonator (SCR): a six-layer architecture where agent state is decomposed into spectral modes, memory is compressed into a mode manifold, task routing is driven by spectral energy profiles, and self-adaptation is gated by five explicit safety checks. The system state at time \(t\) is \(S_t = (M_t, G_t, R_t, U_t, P_t)\) — memory, graph, resonator, uncertainty, and policy states — evolved under a bounded operator with provable stability. We show that the SCR framework strictly generalizes both flat RAG retrieval and reactive agent loops, and provide concrete algorithms for each layer. The architecture is grounded in the existing Nous system, where the SCR formalizes and extends patterns already emerging from empirical use.