The Geometric Inductive Bias of Grokking: Bypassing Phase Transitions via Architectural Topology
Mechanistic interpretability typically relies on post-hoc analysis of trained networks. We instead adopt an interventional approach: testing hypotheses a priori by modifying architectural topology to observe training dynamics. We study grokking - delayed generalization in Transformers trained on cyclic modular addition (Zp) - investigating if specific architectural degrees of freedom prolong the memorization phase. We identify two independent structural factors in standard Transformers: unbounded representational magnitude and data-dependent attention routing. First, we introduce a fully bounded spherical topology enforcing L2 normalization throughout the residual stream and an unembeddin...
| Authors | Alper Yıldırım |
|---|---|
| Primary category | cs.LG |
| Published | 5 March 2026 |
| Last updated at source | 4 May 2026 |
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