Generalize Continuum Attention to larger model scales

Determine whether Continuum Attention retains its observed predictive advantages and generalizes effectively at larger model scales.

Background

Continuum Attention is evaluated in the paper using approximately five-million-parameter causal Transformers on byte-level TinyStories language modeling. Although the reported experiments show improvements over matched softmax attention, the abstract explicitly identifies larger-scale generalization as unresolved. Establishing whether the gains persist in substantially larger models is therefore an open empirical problem.