WhiteMatter lets every Transformer layer read past-token representations from any depth, beating matched baselines at half KV cache
The work introduces WhiteMatter, which uses a learned router to mix past-token representations from any depth into shared key-value (KV) channels so every layer can draw on cross-layer information, together with cyclic Gauss–Seidel iteration to speed up training and prefill; under matched training-token budgets, half-cache WhiteMatter improves perplexity and average downstream scores over matched standard Transformers at two scales up to about 1.3B parameters, while the full-cache version performs comparably to a deeper standard Transformer.