Kosuke Protocol: A Computational Framework for Meaning Emergence in Dynamic Semantic Networks

This paper introduces Kosuke Protocol, a computational framework for modeling meaning as an emergent property of dynamic semantic networks. While recent advances in artificial intelligence have significantly improved the ability to generate language, the mechanisms by which meaning emerges, stabilizes, and evolves remain insufficiently understood. Kosuke Protocol addresses this gap by representing ideas as interconnected fragments within an evolving network structure. The framework introduces several key mechanisms, including meaning mass, conceptual gravity, stochastic connections, and the formation of higher-order structures such as clusters and idea galaxies. These mechanisms enable the observation of semantic dynamics over time, including the emergence of gravity hubs, network reorganization, and volatility patterns described as "meaning weather." A prototype implementation demonstrates how meaning can be modeled not as a static property encoded in individual units, but as a dynamic phenomenon arising from interactions within a network of ideas. Positioned at the intersection of AI and society, knowledge systems, and network science, this work proposes a shift from representation-based models of meaning toward interaction-based approaches, offering a new perspective on how knowledge may be organized and explored in the age of generative AI.