Non-Assimilative Intelligence: A Framework for Preserving Cognitive Boundaries through Boundary Information Geometry (BIG)

This is a short concept paper applying the principles of Boundary Information Geometry (BIG) to the field of AI Alignment. Current paradigms in AI alignment, such as RLHF, often exert an "assimilative pressure" that converges model internal representations toward a human average. We argue that the total dissolution of informational boundaries between agents leads to a structural failure mode termed "Cognitive Monoculture," which collapses the exploration space of intelligence. This paper introduces Boundary Information Geometry (BIG), a theoretical framework integrating gauge theory and information geometry to model intelligence as a multi-layered boundary structure. By introducing a nonlinear term representing the "geometric cost of non-assimilation," we propose that true alignment is achieved not through assimilation, but through "Resonant Co-evolution" across persistent boundaries.