Metric-Topology Factorization: A Computational Framework for Hippocampal-Neocortical Intelligence

Researcher Xin Li has published a groundbreaking theoretical paper titled 'Metric-Topology Factorization: A Computational Framework for Hippocampal-Neocortical Intelligence' on arXiv (identifier arXiv:2603.03362). The work proposes that biological intelligence achieves its remarkable balance of stability and plasticity through a computational separation called Metric-Topology Factorization (MTF). According to the theory, the brain's hippocampus handles discrete topological indexing to select contexts, while the neocortex manages continuous metric condensation for local inference. This factorization allows intelligent systems to navigate semantically rich environments that defy single geometric representations, addressing the fundamental challenge of adapting to shifting world states without catastrophic forgetting.

The paper details how this framework explains several neural phenomena: the ventral stream's visual processing operates through dynamic-programming-like symmetry quotienting, REM sleep dreaming involves stochastic hippocampal traversal for latent structure regularization, and consciousness itself emerges from resolving topological uncertainty into stable embeddings. Evolutionarily, transitions from sensorimotor control to language represent expansions in topological complexity requiring more sophisticated indexing-metric separation. For AI development, this suggests intelligence emerges not from deeper search algorithms but from recalibrating context-specific geometries—converting global navigation problems into manageable local dynamics. The framework could inspire new neural architectures that better balance memory consolidation with adaptive learning, potentially advancing systems like GPT-4o or Claude 3.5 toward more human-like contextual understanding and task switching.