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    <abstract>Autoregressive decoding in Large Language Models (LLMs) generates one token per step, causing high inference latency. Speculative decoding (SD) mitigates this through a guess-and-verify strategy, but existing training-free variants face trade-offs: retrieval-based drafts break when no exact match exists, while logits-based drafts lack structural guidance. We propose RACER (Retrieval-Augmented Contextual Rapid Speculative Decoding), a lightweight and training-free method that integrates retrieved exact patterns with logit-driven future cues. This unification supplies both reliable anchors and flexible extrapolation, yielding richer speculative drafts. Experiments on Spec-Bench, HumanEval, and MGSM-ZH demonstrate that RACER consistently accelerates inference, achieving more than 2\times speedup over autoregressive decoding, and outperforms prior training-free methods, offering a scalable, plug-and-play solution for efficient LLM decoding. Our source code is available at https://github.com/hkr04/RACER.</abstract>
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%0 Conference Proceedings
%T RACER: Retrieval-Augmented Contextual Rapid Speculative Decoding
%A Zhang, Zihong
%A Li, Zuchao
%A Zhang, Lefei
%A Wang, Ping
%A Zhao, Hai
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-racer
%X Autoregressive decoding in Large Language Models (LLMs) generates one token per step, causing high inference latency. Speculative decoding (SD) mitigates this through a guess-and-verify strategy, but existing training-free variants face trade-offs: retrieval-based drafts break when no exact match exists, while logits-based drafts lack structural guidance. We propose RACER (Retrieval-Augmented Contextual Rapid Speculative Decoding), a lightweight and training-free method that integrates retrieved exact patterns with logit-driven future cues. This unification supplies both reliable anchors and flexible extrapolation, yielding richer speculative drafts. Experiments on Spec-Bench, HumanEval, and MGSM-ZH demonstrate that RACER consistently accelerates inference, achieving more than 2\times speedup over autoregressive decoding, and outperforms prior training-free methods, offering a scalable, plug-and-play solution for efficient LLM decoding. Our source code is available at https://github.com/hkr04/RACER.
%U https://aclanthology.org/2026.findings-acl.998/
%P 19962-19988

Markdown (Informal)

[RACER: Retrieval-Augmented Contextual Rapid Speculative Decoding](https://aclanthology.org/2026.findings-acl.998/) (Zhang et al., Findings 2026)

• RACER: Retrieval-Augmented Contextual Rapid Speculative Decoding (Zhang et al., Findings 2026)

ACL

• Zihong Zhang, Zuchao Li, Lefei Zhang, Ping Wang, and Hai Zhao. 2026. RACER: Retrieval-Augmented Contextual Rapid Speculative Decoding. In Findings of the Association for Computational Linguistics: ACL 2026, pages 19962–19988, San Diego, California, United States. Association for Computational Linguistics.