arXiv Machine Learning

Vegas: Self-Speculative Decoding with Verification-Guided Sparse Attention

arXiv:2602. 07223v2 Announce Type: replace Abstract: Long-context large language model (LLM) inference has become the norm for today's AI applications.

arXiv Machine Learning

Jun 25

Dustin: Draft-Augmented Sparse Verification for Efficient Long-Context Generation with Speculative Decoding

arXiv:2606. 24957v1 Announce Type: cross Abstract: While speculative decoding improves inference throughput for multi-batch long-context Large Language Models (LLMs), its efficiency is often limited by a verification bottleneck where Key-Value (KV) cache loading dominates latency.

By WenHung Lee, Jian-Jia Chen, Xiaolin Lin, Pei-Shuo Wang, Chi-Chih Chang, Chun-Che Yang, Ning-Chi Huang, Grace Li Zhang, Kai-Chiang Wu

llms efficiency

arXiv Machine Learning

Aug 4

Bole: Efficient Tree Speculation for Hybrid-Attention Language Models

arXiv:2608. 01651v1 Announce Type: cross Abstract: Hybrid-attention large language models combine full attention with recurrent linear attention to reduce long-context inference costs, yet their autoregressive decoding remains memory-bound.

By Li Wang, Yi Su, Xiabao Wu, Chiran You, Yongchao Liu, Zhan Qiu, Juelu Zhang, Jiajun Zheng, Fangxin Liu, Jie Zhang, Chen Tian, Chengying Huan

llms agents efficiency

arXiv AI

Jun 2

Hybrid Verified Decoding: Learning to Allocate Verification in Speculative Decoding

arXiv:2606. 01019v1 Announce Type: cross Abstract: Large Language Model (LLM) generation remains expensive because autoregressive decoding calls the model once for each new token.

By Xin Su, Dawid Majchrowski, Fangyuan Yu, Vanshil Atul Shah, Sebastian Rogawski, Pawel Morkisz, Anahita Bhiwandiwalla, Phillip Howard

llms agents

arXiv Machine Learning

Jul 3

Lynx: Progressive Speculative Quantization for accelerating KV Transfer in Long-Context Inference

arXiv:2607. 01831v1 Announce Type: cross Abstract: Long-context inference is increasingly common in large language model (LLM) serving, driven by retrieval-augmented generation and agentic systems.

By Wenchen Han, Gingfung Matthew Yeung, Marco Barletta, William Toner, Amory Hoste, Adam Barker

llms rag agents efficiency benchmarks

arXiv AI

Jun 3

KnapSpec: Self-Speculative Decoding via Adaptive Layer Selection as a Knapsack Problem

arXiv:2602. 20217v2 Announce Type: replace-cross Abstract: Self-speculative decoding (SSD) accelerates LLM inference by skipping layers to create an efficient draft model, yet existing methods often rely on static heuristics that ignore the dynamic computational overhead of attention in long-context scenarios.

By Seongjin Cha, Gyuwan Kim, Dongsu Han, Tao Yang, Insu Han

llms efficiency benchmarks

arXiv AI

Jun 2

BudgetDraft: Acceptance-Aware Multi-View Training for Sparse-KV Speculative Decoding

arXiv:2606. 00144v1 Announce Type: cross Abstract: Speculative decoding speeds up autoregressive decoding by using a drafter to propose multiple tokens that a verifier validates in parallel.

By Liang He, Jingbo Wen, Qishi Zhan, Yixiong Chen, Kangning Cui, Qizhen Lan, Xilu Wang

efficiency

arXiv Machine Learning

Jun 5

You Only Index Once: Cross-Layer Sparse Attention with Shared Routing

arXiv:2606. 06467v1 Announce Type: cross Abstract: Long-context inference in modern LLMs is increasingly constrained by decoding efficiency, especially in reasoning-heavy settings where models generate long intermediate chains of thought.

By Yutao Sun, Yanqi Zhang, Li Dong, Jianyong Wang, Furu Wei

llms efficiency benchmarks

arXiv Machine Learning

Jun 4

SparDA: Sparse Decoupled Attention for Efficient Long-Context LLM Inference

arXiv:2606. 04511v1 Announce Type: cross Abstract: Sparse attention reduces compute and memory bandwidth for long-context LLM inference.

By Yaosheng Fu, Guangxuan Xiao, Xin Dong, Song Han, Oreste Villa

llms efficiency

arXiv AI

Jun 30

Coverage-Driven KV Cache Eviction for Efficient and Improved Inference of LLM

arXiv:2606. 29563v1 Announce Type: cross Abstract: Large language models (LLMs) excel at complex tasks like question answering and summarization, thanks to their ability to handle long-context inputs.

By Shuvendu Roy, Mengyao Zhai, Hossein Hajimirsadeghi, Golnoosh Samei

llms nlp efficiency

arXiv Machine Learning

Jul 1

RaBitQCache: Rotated Binary Quantization for KVCache in Long Context LLM Inference

arXiv:2606. 31519v1 Announce Type: new Abstract: Long-context Large Language Model inference is severely bottlenecked by the massive Key-Value (KV) cache, yet existing sparse attention methods often suffer from static fixed-budget (Top-k) retrieval or rely on proxy scores that are computationally expensive and biased.

By Wenhao Li, Jinhao Dong, Hailin Zhang, Wenhang Shi, Wei Lu, Xiaoyong Du

llms efficiency benchmarks

arXiv Machine Learning

Jul 24

Windowed-MTP: Removing the Full-Context Draft-KV Tax at Million-Token Context

arXiv:2607. 21535v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by having a cheap draft propose tokens that a target verifies in parallel.

By Alagappan Valliappan

efficiency

arXiv AI

Jun 16

Nightjar: Dynamic Adaptive Speculative Decoding for Large Language Models Serving

arXiv:2512. 22420v5 Announce Type: replace-cross Abstract: Speculative decoding (SD) accelerates LLM inference by verifying draft tokens in parallel.

By Rui Li, Zhaoning Zhang, Libo Zhang, Huaimin Wang, Xiang Fu, Zhiquan Lai

llms efficiency benchmarks

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