Architectural generality beyond transformers

Ascertain whether alternative sequence-model architectures—structured state-space models, deep multilayer perceptrons with more sophisticated gating, and hybrid recurrent–attention systems—can form geometric Bayesian manifolds comparable to those identified in transformers under Bayesian wind-tunnel evaluations.

Background

The experiments demonstrate that small transformers, but not capacity-matched deep MLPs, achieve near-exact Bayesian posterior tracking in wind-tunnel tasks, suggesting that attention-based routing and residual compositionality are crucial.

Beyond these tested baselines, the authors highlight uncertainty about the ability of other architectures—such as state-space models and hybrid designs—to realize comparable geometric Bayesian structures, motivating a principled architectural comparison under wind-tunnel conditions.