This paper introduces the Cognitive Fusion Mechanism (CFM) of the Larkos system, a novel architecture that integrates three independent information streams: LLM embeddings, neuron states, and episodic memory into a shared 64 dimensional space. The design ensures information preservation, prevents stream dominance, and enables dynamic, context-aware reasoning. We detail the deterministic projection, banded assembly, and cross-band mixing processes, and present empirical results from the Larkos training loop and inference runner. The system demonstrates strong per- formance in learning efficiency, domain transfer, continual learning, and meta-learning, while maintaining stability and affective coherence. Our experiments show that CFM enables robust, interpretable, and adaptive cognitive modeling, paving the way for more human-like AI systems.