AnalysisAI ModelsOctober 17, 2026

DLite speeds speculative decoding for agentic AI without history

Read original source →arxiv.org

DLite drops historical-context maintenance in speculative decoding, using the target model's hidden states for local draft predictions to raise acceptance length and cut overhead. The paper reports a two-stage training scheme and a sequential head aimed at long, continuously evolving agentic workloads.

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