The DOI matches its Crossref record.
Sequoia: Scalable and Robust Speculative Decoding
Overview
Integrity signals
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Static token trees are constructed once using fixed Top-K sampling and remain unchanged.
“However, static token trees, most existing work adopted (Miao et al. 2024; Luo et al. 2025; Chen et al. 2024), are constructed once using fixed Top-K sampling and remain unchanged.”
From DIAA: A Decoding-Efficient Inference Acceleration Approach for On-Device Large Language Models (2026) · cites this paper as Chen et al. 2024
Existing tree-based works employ static verification trees that lack adaptability by static structures, which cannot dynamically adjust to the changing context of the generated text, leading to suboptimal draft acceptance rates and limited speedup.
“Existing tree-based works (Luo et al. 2025; Miao et al. 2024; Chen et al. 2024) employ static verification trees that lack adaptability by static structures, which cannot dynamically adjust to the changing context of the generated text, leading to suboptimal draft acceptance rates and limited speedup.”
From DIAA: A Decoding-Efficient Inference Acceleration Approach for On-Device Large Language Models (2026) · cites this paper as Chen et al. 2024
Citations
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DIAA: A Decoding-Efficient Inference Acceleration Approach for On-Device Large Language Models
Tian, H., Lu, S., Tian, F., Cui, G., Li, Z., Zhang, X., Sheng, Q. Z., & Dou, W. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i31.39789
Metadata
- Authors
- Chen, Z., May, A., Svirschevski, R., Huang, Y., Ryabinin, M., Jia, Z., & Chen, B.
- ORCID
- Jia, Zhihao
- Venue
- Advances in Neural Information Processing Systems 37
- Published
- 2024
- Pages
- 129531-129563
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W4415800630
- Cited by
- 5
- References
- 0
- Citations by year
- 2026: 4 · 2025: 1
- FWCI
- 1.48
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- neural information processing systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- speculative decoding, large language models, decoding efficiency, robust decoding, scalable inference
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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