Structural Analysis of Sparse Model Architectures: Hidden Gaps, Recurring Patterns, and Five Untested Paradigm Candidates for Next-Generation LLMs

We present a structural analysis of sparse model architectures, identifying five gaps in the current paradigm, documenting recurring meta-patterns across major AI breakthroughs, and proposing five candidate architectures for the next paradigm shift. The central observation is that a single structural move — identify heterogeneity, separate components, optimize allocation — recurs across all major innovations. The most striking gap is the absence of write operations in conditional memory architectures. We further examine biological precedents from the hippocampus-neocortex system. This paper presents no experimental results; it presents a structural argument for where to look next.