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Looped Transformers offer a parameter-efficient approach to language modeling by repeatedly applying a shared backbone. Vanilla looped Transformers use each backbone output directly as the input to the next iteration. These outputs may contain information useful several iterations later, but retaining it across the loo...
Delta-K is proposed, a backbone-agnostic, plug-and-play inference framework that resolves omission by operating directly in the shared cross-attention Key space and significantly improves compositional alignment across both modern DiT and foundational U-Net architectures without requiring spatial masks, auxiliary train...
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