The paper restates existing alignment metrics and claims, with no numerical evidence, that structured architectures show more stable cross-model representation geometry.
Controlling the inductive bias of wide neural networks by modifying the kernel’s spectrum,
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Cross-Model Semantics in Representation Learning
The paper restates existing alignment metrics and claims, with no numerical evidence, that structured architectures show more stable cross-model representation geometry.