Cacheback: Speculative Decoding With Nothing But Cache
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911289436012544 |
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| author | Ma, Zhiyao Gim, In Zhong, Lin |
| author_facet | Ma, Zhiyao Gim, In Zhong, Lin |
| contents | We present Cacheback Decoding, a training-free and model-agnostic speculative decoding method that exploits the locality in language to accelerate Large Language Model (LLM) inference. Cacheback leverages only Least Recently Used (LRU) cache tables of token n-grams to generate draft sequences. Cacheback achieves state-of-the-art performance among comparable methods despite its minimalist design, and its simplicity allows easy integration into existing systems. Cacheback also shows potential for fast adaptation to new domains. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_21699 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cacheback: Speculative Decoding With Nothing But Cache Ma, Zhiyao Gim, In Zhong, Lin Computation and Language Artificial Intelligence We present Cacheback Decoding, a training-free and model-agnostic speculative decoding method that exploits the locality in language to accelerate Large Language Model (LLM) inference. Cacheback leverages only Least Recently Used (LRU) cache tables of token n-grams to generate draft sequences. Cacheback achieves state-of-the-art performance among comparable methods despite its minimalist design, and its simplicity allows easy integration into existing systems. Cacheback also shows potential for fast adaptation to new domains. |
| title | Cacheback: Speculative Decoding With Nothing But Cache |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2511.21699 |