OPT as a Cognitive Routing Layer for Modular LLM Architectures

Recent large language models increasingly rely on modular architectures to achieve scalability and efficiency. Among these approaches, Mixture-of-Experts (MoE) systems enable conditional computation by dynamically routing inputs to specialized expert modules. Although this mechanism allows models to expand parameter capacity while maintaining manageable computational cost, the routing processes that determine expert activation remain largely opaque. Current gating mechanisms primarily optimize statistical performance, offering limited insight into the structural logic governing how information flows through the system. This paper proposes a conceptual framework in which the Ontology–Process–Trajectory (OPT) model of cognition functions as a cognitive routing layer for modular large language model architectures. The OPT framework describes reasoning processes as dynamic pathways connecting generative signal sources with stabilization processes that resolve those signals into coherent interpretations or actions. By introducing this pathway perspective into modular AI architectures, expert routing can be interpreted as structured signal propagation rather than purely statistical gating. Within this perspective, modular experts correspond to stabilization mechanisms that transform signals within particular domains of processing, while routing mechanisms approximate the selection of cognitive pathways through which signals travel. The resulting architecture suggests a layered interpretation in which neural computation operates within a structural routing topology that organizes information flow. The present work does not introduce a new training algorithm. Instead, it proposes a structural interpretation of modular neural architectures and outlines how pathway-based routing principles could inform future model design. By linking cognitive pathway theory with modular machine learning systems, the OPT framework may offer a conceptual bridge between theories of reasoning and scalable artificial intelligence architectures.