arXiv AI November 5, 2026 By Hengjie Zhu, Dayan Wu, Xinze Liu, Peng Fu, Zheng Lin, Weiping Wang

RECAP: Context-Aware Prediction Recycling with Request-Local and Persistent Memories for Multimodal Speculative Decoding

The paper introduces RECAP, a method that improves speculative decoding for vision‑language models by recycling a target model’s own predictions. RECAP stores predictions using one‑, two‑, and three‑token keys, allowing contextual memories that are request‑specific and token‑level memories that persist across requests. By fusing retrieved predictions based on entropy‑confidence weighting and using best‑first search to build a candidate tree, RECAP achieves significant speedups over autoregressive decoding without requiring training or a draft model.

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Related stories

CatchUp: Depth-Aware Re-Admission of Visual Tokens for Efficient Large Vision-Language Models

SEED: Self-Speculative Decoding via Implicit Encoder-Decoder

CoTA++: Understanding the Repeat Curse in dMLLMs from an Information Flow Perspective

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DEdit: Iterative Draft Editing for Speculative Decoding