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The Prism Transformer replaces uniform multi-head attention with a progressive head schedule that increases head count across layers, enabling a local-to-global hierarchy without extra parameters or FLOPs. It consistently outperforms standard Transformers on language modeling and zero-shot benchmarks at 124M, 354M, and 757M scales.