Speculative decoding accelerates large language model inference by proposing tokens with a lightweight draft model and selectively accepting them using a target model. This work introduces DropMatch, a novel approach that matches draft tokens to the predictive distribution of the target model via Monte Carlo dropout applied exclusively to the LM head, enabling sampling-based acceptance decisions. By generating multiple decoding paths, our method forms an empirical token distribution against which draft tokens are evaluated for consistency. This acceptance mechanism enables the model to adaptively control the size of decoding paths under an appropriate dropout probability, preventing substantial distortion of the target model predictive distribution. The proposed method operates in a training-free, data-free, and calibration-free manner, requires no architectural modification to pretrained models, and can be orthogonally integrated with a wide range of existing speculative decoding and inference acceleration techniques. Experiments across multiple benchmarks demonstrate that our approach increases acceptance length while maintaining competitive task performance, yielding inference speedups ranging from 1.09x to 1.33x over the standard baseline, and up to an additional 1.09x speedup when applied on top of EAGLE3.
AdaSD: Adaptive Speculative Decoding for Efficient Language Model Inference
Institute of Information Science, Academia Sinica（学术院信息研究所）；Department of Computer Science and Information Engineering, National Taiwan University（台湾大学计算机科学与信息工程系）
Speculative Decoding with CTC-based Draft Model for LLM Inference Acceleration
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory of Al Safety, Chinese Academy of Sciences（中国科学院人工智能安全重点实验室）
Draft-OPD: On-Policy Distillation for Speculative Draft Models
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；Peking University（北京大学）；Zhejiang University（浙江大学）
Traversal Verification for Speculative Tree Decoding
Lenovo AI Technology Center, Lenovo（联想人工智能技术中心，联想）；National Center for Mathematics and Interdisciplinary Sciences (NCMIS), AMSS, CAS（国家数学与交叉科学中心（NCMIS），中国科学院数学研究所）
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
FAIR at Meta（Meta FAIR研究院）；GenAI at Meta（Meta GenAI部门）；Reality Labs at Meta（Meta Reality Labs）；University of Toronto（多伦多大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Dana-Farber Cancer Institute（丹娜法伯癌症研究院）；Carnegie Mellon University（卡内基梅隆大学）
Semi-Clairvoyant Scheduling of Speculative Decoding Requests to Minimize LLM Inference Latency
School of Cyber Science and Engineering, Xi’an Jiaotong University（西安交通大学计算机科学与工程学院）；School of Computer Science and Engineering, The University of Aizu（台湾花莲大学计算机科学与工程学院）
Mistletoe: Stealthy Acceleration-Collapse Attacks on Speculative Decoding
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；South China University of Technology（华南理工大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Huawei Technology（华为技术）
DREAM: Drafting with Refined Target Features and Entropy-Adaptive Cross-Attention Fusion for Multimodal Speculative Decoding
Courant Institute of Mathematical Sciences, New York University（纽约大学数学科学学院）；Tandon School of Engineering, New York University（纽约大学工程学院）；Cerebras Systems Inc.（Cerebras Systems公司）；University of Pennsylvania（宾夕法尼亚大学）
Flatter Tokens are More Valuable for Speculative Draft Model Training
Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education（新一代人工智能技术及其交叉应用关键实验室（东南大学））；Southeast University（东南大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；Qiyuan Tech（启元科技）
DeLS-Spec: Decoupled Long-Short Contexts for Parallel Speculative Drafting
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence（工业和信息化部模式分析与机器智能重点实验室）；The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education（教育部脑机智能技术重点实验室）
FlexDraft: Flexible Speculative Decoding via Attention Tuning and Bonus-Guided Calibration
EPIC Lab, SJTU（上海交通大学EPIC实验室）；UESTC；School of Software Engineering, HUST（华中科技大学软件工程学院）；Tsinghua University（清华大学）；HKUST(GZ)（香港科技大学（广州））；Shanghai AI Laboratory（上海人工智能实验室）
DFlow: Enabling Verifier Information Flow in Block Diffusion Speculative Decoding
Peking University（北京大学）；University of Electronic Science and Technology of China（电子科技大学）；Shanghai Jiao Tong University（上海交通大学）；Institute of Computational Social Science, Peking University (Qingdao)（北京大学（青岛）计算社会科学研究所）
TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding
Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工联合研究技术联盟）；Dept. of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）；CSAIL, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）
AdaFlash: Adaptive Speculative Decoding via On-Policy Distilled Diffusion Drafters
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；Huawei Foundation Model Dept（华为基础模型部）
Red Note Hi-Lab（红笔记高研实验室）；Shanghai Jiaotong University（上海交通大学）；Nanjing University（南京大学）；Zhejiang University（浙江大学）；Chinese University of Hong Kong（香港中文大学）
