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Attention Routing Stabilizes Early: Working-Set Inference for Recurrent Language Models
Recurrent language models repeatedly apply shared network blocks to refine latent representations, but standard inference recomputes global attention at every recurrent step. We study attention dynamics across recurrent depth and find that attention support and distributions stabilize substantially
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