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Kangaroo: Lossless self-speculative decoding via double early exiting

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it

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cs.CL 3

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2026 2 2024 1

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representative citing papers

SpecBlock: Block-Iterative Speculative Decoding with Dynamic Tree Drafting

cs.CL · 2026-05-08 · unverdicted · novelty 7.0 · 2 refs

SpecBlock achieves 8-13% higher mean speedup than EAGLE-3 at 44-52% drafting cost via block-iterative drafting with hidden-state inheritance, dynamic rank-head branching, valid-prefix masking, and optional cost-aware bandit adaptation.

Two-dimensional early exit optimisation of LLM inference

cs.CL · 2026-03-27 · unverdicted · novelty 7.0

Coordinating layer-wise and sentence-wise early exits in LLMs produces multiplicative speedups of 1.4-2.3x over single-dimension early exit on sentiment classification tasks.

A Survey on Efficient Inference for Large Language Models

cs.CL · 2024-04-22 · accept · novelty 3.0

The paper surveys techniques to speed up and reduce the resource needs of LLM inference, organized by data-level, model-level, and system-level changes, with comparative experiments on representative methods.

citing papers explorer

Showing 3 of 3 citing papers.

  • SpecBlock: Block-Iterative Speculative Decoding with Dynamic Tree Drafting cs.CL · 2026-05-08 · unverdicted · none · ref 13 · 2 links

    SpecBlock achieves 8-13% higher mean speedup than EAGLE-3 at 44-52% drafting cost via block-iterative drafting with hidden-state inheritance, dynamic rank-head branching, valid-prefix masking, and optional cost-aware bandit adaptation.

  • Two-dimensional early exit optimisation of LLM inference cs.CL · 2026-03-27 · unverdicted · none · ref 15

    Coordinating layer-wise and sentence-wise early exits in LLMs produces multiplicative speedups of 1.4-2.3x over single-dimension early exit on sentiment classification tasks.

  • A Survey on Efficient Inference for Large Language Models cs.CL · 2024-04-22 · accept · none · ref 249

    The paper surveys techniques to speed up and reduce the resource needs of LLM inference, organized by data-level, model-level, and system-level changes, with comparative experiments on representative methods.