bicache enables shared-prefix KV caching in diffusion language models by dynamically selecting reusable shallow-layer depths based on prefix fraction, delivering 36.3-98.3% throughput gains with 0-1.8% accuracy difference.
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The paper guides ML use in economic history, identifies systematic prediction bias that distorts coefficients, and shows debiasing via small expert-labeled samples can correct it while preserving scale.
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Enabling KV Caching of Shared Prefix for Diffusion Language Models
bicache enables shared-prefix KV caching in diffusion language models by dynamically selecting reusable shallow-layer depths based on prefix fraction, delivering 36.3-98.3% throughput gains with 0-1.8% accuracy difference.