id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2637d50b286df8bbee11e4a10a0a06661adf804b34461dba537faed835ea5169 | [
"arxiv",
"semantic_scholar"
] | EAGLE-Pangu: Accelerator-Safe Tree Speculative Decoding on Ascend NPUs | Autoregressive decoding remains a primary bottleneck in large language model (LLM) serving, motivating speculative decoding methods that reduce expensive teacher-model invocations by verifying multiple candidate tokens per step. Tree-structured speculation further increases parallelism, but is often brittle when ported... | [
"Chang Han",
"Yijie Hu",
"Jingling Liu"
] | [
"cs.LG",
"cs.PL"
] | [
"Computer Science"
] | 2026-03-09T00:00:00 | https://arxiv.org/abs/2603.08088 | https://arxiv.org/pdf/2603.08088v1 | 2603.08088 | 10.48550/arXiv.2603.08088 | 0 | 0 | false | null | arXiv.org | 0.5019 |
abaf72d9dcb8237f170e98af3b8e9308c2e965d7c8234d464348ebbfec8e115b | [
"arxiv",
"semantic_scholar"
] | Speculating Experts Accelerates Inference for Mixture-of-Experts | Mixture-of-Experts (MoE) models have gained popularity as a means of scaling the capacity of large language models (LLMs) while maintaining sparse activations and reduced per-token compute. However, in memory-constrained inference settings, expert weights must be offloaded to CPU, creating a performance bottleneck from... | [
"Vivan Madan",
"Prajwal Singhania",
"Abhinav Bhatele",
"Tom Goldstein",
"Ashwinee Panda"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-09T00:00:00 | https://arxiv.org/abs/2603.19289 | https://arxiv.org/pdf/2603.19289v1 | 2603.19289 | 10.48550/arXiv.2603.19289 | 0 | 0 | true | https://github.com/axonn-ai/yalis/tree/offload_prefetch | arXiv.org | 0.7756 |
940d2378569b310049174fb9a710aa7b4bf69280e958af18c8a3bc4f4319269d | [
"arxiv",
"semantic_scholar"
] | ConFu: Contemplate the Future for Better Speculative Sampling | Speculative decoding has emerged as a powerful approach to accelerate large language model (LLM) inference by employing lightweight draft models to propose candidate tokens that are subsequently verified by the target model. The effectiveness of this paradigm critically depends on the quality of the draft model. While ... | [
"Zongyue Qin",
"Raghavv Goel",
"Mukul Gagrani",
"Risheek Garrepalli",
"Mingu Lee",
"Yizhou Sun"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-09T00:00:00 | https://arxiv.org/abs/2603.08899 | https://arxiv.org/pdf/2603.08899v3 | 2603.08899 | 10.48550/arXiv.2603.08899 | 0 | 0 | false | null | arXiv.org | 0.5019 |
85ab3f800c4a10936be523e43141f048f35767e726ca2642a30d4b8cd256376b | [
"arxiv",
"semantic_scholar"
] | Balancing Coverage and Draft Latency in Vocabulary Trimming for Faster Speculative Decoding | Speculative decoding accelerates inference for Large Language Models by using a lightweight draft model to propose candidate tokens that are verified in parallel by a larger target model. Prior work shows that the draft model often dominates speculative decoding latency, since it generates tokens sequentially and incur... | [
"Ofir Ben Shoham"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-05T00:00:00 | https://arxiv.org/abs/2603.05210 | https://arxiv.org/pdf/2603.05210v1 | 2603.05210 | 10.48550/arXiv.2603.05210 | 1 | 0 | false | null | arXiv.org | 0.4973 |
9965bae6110495f0a86782639cc6e909e3b5d6df537f588e4dc5832c4fc4fc5d | [
"arxiv",
"semantic_scholar"
] | SLO-Aware Compute Resource Allocation for Prefill-Decode Disaggregated LLM Inference | Prefill-Decode (P/D) disaggregation has emerged as a widely adopted optimization strategy for Large Language Model (LLM) inference. However, there currently exists no well-established methodology for determining the optimal number of P/D hardware resources, subject to constraints on total throughput, service level obje... | [
"Luchang Li",
"Dongfang Li",
"Bozhao Gong",
"Yu Zhang"
] | [
"cs.DC",
"cs.IT",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2026-03-05T00:00:00 | https://arxiv.org/abs/2603.04716 | https://arxiv.org/pdf/2603.04716v1 | 2603.04716 | 10.48550/arXiv.2603.04716 | 0 | 0 | false | null | arXiv.org | 0.4973 |
9f9e27c8313a7425ad13a0442ab2169c45f549374fe6e4b1aa88ce20b0ed42e7 | [
"arxiv",
"semantic_scholar"
] | Speculative Speculative Decoding | Autoregressive decoding is bottlenecked by its sequential nature. Speculative decoding has become a standard way to accelerate inference by using a fast draft model to predict upcoming tokens from a slower target model, and then verifying them in parallel with a single target model forward pass. However, speculative de... | [
"Tanishq Kumar",
"Tri Dao",
"Avner May"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-03T00:00:00 | https://arxiv.org/abs/2603.03251 | https://arxiv.org/pdf/2603.03251v3 | 2603.03251 | 10.48550/arXiv.2603.03251 | 8 | 0 | true | null | arXiv.org | 0.765 |
c88adb913b99bb02f7b5e4f49fd9a2d7173101b964065942f0a52a2bf37500dd | [
"arxiv",
"semantic_scholar"
] | Accelerating OpenPangu Inference on NPU via Speculative Decoding | To mitigate the Memory Wall bottleneck encountered by Large Language Models (LLMs) during inference on \textbf{NPU} hardware, and addressing the scarcity of native support for mainstream speculative decoding algorithms on domestic infrastructure, this study presents an end-to-end speculative inference acceleration sche... | [
"Yuntao Dai",
"Jing Wu",
"Hang Gu",
"Teng Wang"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2026-03-03T00:00:00 | https://arxiv.org/abs/2603.03383 | https://arxiv.org/pdf/2603.03383v1 | 2603.03383 | 10.48550/arXiv.2603.03383 | 0 | 0 | false | null | arXiv.org | 0.495 |
d8e14c6dab3bff9aa086aa6ad24ffafbf19d0230d1470b6a7f4d7c8a10e82bfc | [
"arxiv",
"semantic_scholar"
] | Learning to Draft: Adaptive Speculative Decoding with Reinforcement Learning | Speculative decoding accelerates large language model (LLM) inference by using a small draft model to generate candidate tokens for a larger target model to verify. The efficacy of this technique hinges on the trade-off between the time spent on drafting candidates and verifying them. However, current state-of-the-art ... | [
"Jiebin Zhang",
"Zhenghan Yu",
"Liang Wang",
"Nan Yang",
"Eugene J. Yu",
"Zheng Li",
"Yifan Song",
"Dawei Zhu",
"Xingxing Zhang",
"Furu Wei",
"Sujian Li"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-03-02T00:00:00 | https://arxiv.org/abs/2603.01639 | https://arxiv.org/pdf/2603.01639v1 | 2603.01639 | 10.48550/arXiv.2603.01639 | 0 | 0 | false | null | arXiv.org | 0.4939 |
69cfaa5a10370e697fe383985d3b7d207f7a18f1057f0789847a2a3d3cfd7cd4 | [
"arxiv",
"semantic_scholar"
] | Quasar: Quantized Self-Speculative Acceleration for Rapid Inference via Memory-Efficient Verification | Speculative Decoding (SD) has emerged as a premier technique for accelerating Large Language Model (LLM) inference by decoupling token generation into rapid drafting and parallel verification. While recent advancements in self-speculation and lookahead decoding have successfully minimized drafting overhead, they have s... | [
"Guang Huang",
"Zeyi Wen"
] | [
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-02T00:00:00 | https://arxiv.org/abs/2603.01399 | https://arxiv.org/pdf/2603.01399v1 | 2603.01399 | 10.48550/arXiv.2603.01399 | 1 | 0 | true | https://github.com/Tom-HG/Quasar | arXiv.org | 0.7632 |
3fc5790597f01c3d465898874dbabfe2e0af582688e4cb6ae667c8ccf46d118a | [
"arxiv",
"semantic_scholar"
] | KERV: Kinematic-Rectified Speculative Decoding for Embodied VLA Models | Vision-Language-Action (VLA) models build a token-domain robot control paradigm, yet suffer from low speed. Speculative Decoding (SD) is an optimization strategy that can boost inference speed. Two key issues emerge when integrating VLA and SD: first, SD relies on re-inference to address token errors, which is computat... | [
"Zihao Zheng",
"Zhihao Mao",
"Maoliang Li",
"Jiayu Chen",
"Xinhao Sun",
"Zhaobo Zhang",
"Donggang Cao",
"Hong Mei",
"Xiang Chen"
] | [
"cs.RO",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-02T00:00:00 | https://arxiv.org/abs/2603.01581 | https://arxiv.org/pdf/2603.01581v2 | 2603.01581 | 10.48550/arXiv.2603.01581 | 6 | 0 | false | null | arXiv.org | 0.4939 |
3c52ce4295805bbf03880da0a8385ba7b764d0c84ceaa0df7970243c6137230c | [
"arxiv",
"semantic_scholar"
] | SJD-PV: Speculative Jacobi Decoding with Phrase Verification for Autoregressive Image Generation | Autoregressive (AR) image models have recently demonstrated remarkable generative capability, but their sequential nature results in significant inference latency. Existing training-free acceleration methods typically verify tokens independently, overlooking the strong co-occurrence patterns between adjacent visual tok... | [
"Zhehao Yu",
"Baoquan Zhang",
"Bingqi Shan",
"Xinhao Liu",
"Dongliang Zhou",
"Guotao Liang",
"Guangming Ye",
"Yunming Ye"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-03-02T00:00:00 | https://arxiv.org/abs/2603.06666 | https://arxiv.org/pdf/2603.06666v1 | 2603.06666 | 10.48550/arXiv.2603.06666 | 0 | 0 | false | null | arXiv.org | 0.4939 |
0f9321eafacdebba715e3ab1b3c36c9ef8568ec76d554dec4501d2d6ac5d1092 | [
"arxiv",
"semantic_scholar"
] | LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding | Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to propose candidate tokens that are then verified in parallel by the target model. The speedup is significantly determined by the acceptance rate, yet standard training minimizes Kullback-Leibler (KL... | [
"Alexander Samarin",
"Sergei Krutikov",
"Anton Shevtsov",
"Sergei Skvortsov",
"Filipp Fisin",
"Alexander Golubev"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-02-27T00:00:00 | https://arxiv.org/abs/2602.23881 | https://arxiv.org/pdf/2602.23881v2 | 2602.23881 | 10.48550/arXiv.2602.23881 | 5 | 0 | false | null | arXiv.org | 0.4904 |
a1ec30d62dc75806d0aa475f0ef3030088d533404888c920379eaa673c8aa51d | [
"arxiv",
"semantic_scholar"
] | Speculative Decoding Scaling Laws (SDSL): Throughput Optimization Made Simple | Speculative decoding is a technique that uses multiple language models to accelerate infer- ence. Previous works have used an experi- mental approach to optimize the throughput of the inference pipeline, which involves LLM training and can be costly. This study of spec- ulative decoding proposes a theory that ana- lyti... | [
"Amirhossein Bozorgkhoo",
"Igor Molybog"
] | [
"cs.CL",
"cs.IT",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2026-02-25T00:00:00 | https://arxiv.org/abs/2603.11053 | https://arxiv.org/pdf/2603.11053v1 | 2603.11053 | 10.48550/arXiv.2603.11053 | 0 | 0 | false | null | arXiv.org | 0.4881 |
614457d2f5b19b2841174f379475807c919b6cd0de49039533020870a4263f20 | [
"arxiv",
"semantic_scholar"
] | MineDraft: A Framework for Batch Parallel Speculative Decoding | Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to propose draft tokens that are subsequently verified by a larger target model. However, the performance of standard SD is often limited by the strictly sequential execution of these drafting and verification stages. To... | [
"Zhenwei Tang",
"Arun Verma",
"Zijian Zhou",
"Zhaoxuan Wu",
"Alok Prakash",
"Daniela Rus",
"Bryan Kian Hsiang Low"
] | [
"cs.CL",
"cs.AI",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2026-02-24T00:00:00 | https://arxiv.org/abs/2603.18016 | https://arxiv.org/pdf/2603.18016v2 | 2603.18016 | 10.48550/arXiv.2603.18016 | 2 | 0 | false | null | arXiv.org | 0.487 |
3a07305ce7f891698355f95b58fc4c94e26624805574d9a871b4fa5f098db6cd | [
"arxiv",
"semantic_scholar"
] | Greedy Multi-Path Block Verification for Faster Decoding in Speculative Sampling | The goal of $L$-step speculative decoding is to accelerate autoregressive decoding of a target model by using a cheaper draft model to generate a candidate path of $L$ tokens. Based on a verification algorithm involving target and draft model probabilities, a prefix of the candidate sequence is accepted, and an additio... | [
"Rahul Thomas",
"Arka Pal"
] | [
"cs.IT",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2026-02-18T00:00:00 | https://arxiv.org/abs/2602.16961 | https://arxiv.org/pdf/2602.16961v1 | 2602.16961 | 10.48550/arXiv.2602.16961 | 0 | 0 | false | null | arXiv.org | 0.4801 |
7ff93673f3d0de80cf9a5d268e83d2cfd323d63015bd65be696aa45af8421926 | [
"arxiv",
"semantic_scholar"
] | Privacy-Aware Split Inference with Speculative Decoding for Large Language Models over Wide-Area Networks | We present a practical system for privacy-aware large language model (LLM) inference that splits a transformer between a trusted local GPU and an untrusted cloud GPU, communicating only intermediate activations over the network. Our system addresses the unique challenges of autoregressive LLM decoding over high-latency... | [
"Michael Cunningham"
] | [
"cs.CR",
"cs.DC"
] | [
"Computer Science"
] | 2026-02-18T00:00:00 | https://arxiv.org/abs/2602.16760 | https://arxiv.org/pdf/2602.16760v1 | 2602.16760 | 10.48550/arXiv.2602.16760 | 0 | 0 | false | null | arXiv.org | 0.4801 |
f8028e71a8ea62bb19e7f8fc6c259b427149351e6c3097da0bd5378774eca8b0 | [
"arxiv",
"semantic_scholar"
] | MoE-Spec: Expert Budgeting for Efficient Speculative Decoding | Speculative decoding accelerates Large Language Model (LLM) inference by verifying multiple drafted tokens in parallel. However, for Mixture-of-Experts (MoE) models, this parallelism introduces a severe bottleneck: large draft trees activate many unique experts, significantly increasing memory pressure and diminishing ... | [
"Bradley McDanel",
"Steven Li",
"Sruthikesh Surineni",
"Harshit Khaitan"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-17T00:00:00 | https://arxiv.org/abs/2602.16052 | https://arxiv.org/pdf/2602.16052v1 | 2602.16052 | 10.48550/arXiv.2602.16052 | 3 | 0 | false | null | arXiv.org | 0.479 |
dae9fa5aba7ff05350cfa8cd1cf97257baf7db1b7dfd4ea5f2a7e23946ab3084 | [
"arxiv",
"semantic_scholar"
] | Sparrow: Text-Anchored Window Attention with Visual-Semantic Glimpsing for Speculative Decoding in Video LLMs | Although speculative decoding is widely used to accelerate Vision-Language Models (VLMs) inference, it faces severe performance collapse when applied to Video Large Language Models (Vid-LLMs). The draft model typically falls into the trap of attention dilution and negative visual gain due to key-value cache explosion a... | [
"Libo Zhang",
"Zhaoning Zhang",
"Wangyang Hong",
"Peng Qiao",
"Dongsheng Li"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-17T00:00:00 | https://arxiv.org/abs/2602.15318 | https://arxiv.org/pdf/2602.15318v1 | 2602.15318 | 10.48550/arXiv.2602.15318 | 3 | 0 | false | null | arXiv.org | 0.479 |
d0581e9dc5f171405f2f0f7344445b8c654db77136e29c8ed6317ec5451b93e4 | [
"arxiv",
"semantic_scholar"
] | Speculative Decoding with a Speculative Vocabulary | Speculative decoding has rapidly emerged as a leading approach for accelerating language model (LM) inference, as it offers substantial speedups while yielding identical outputs. This relies upon a small draft model, tasked with predicting the outputs of the target model. State-of-the-art speculative decoding methods u... | [
"Miles Williams",
"Young D. Kwon",
"Rui Li",
"Alexandros Kouris",
"Stylianos I. Venieris"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-02-14T00:00:00 | https://arxiv.org/abs/2602.13836 | https://arxiv.org/pdf/2602.13836v1 | 2602.13836 | 10.48550/arXiv.2602.13836 | 2 | 1 | false | null | arXiv.org | 0.4755 |
065d91c968177c81423660a305f9818dde4293c39bac8fe49ed564c09c76f506 | [
"arxiv",
"semantic_scholar"
] | MoE-SpAc: Efficient MoE Inference Based on Speculative Activation Utility in Heterogeneous Edge Scenarios | Mixture-of-Experts (MoE) models enable scalable performance but face severe memory constraints on edge devices. Existing offloading strategies struggle with I/O bottlenecks due to the dynamic, low-information nature of autoregressive expert activation. In this paper, we propose to repurpose Speculative Decoding (SD) no... | [
"Shuhuai Li",
"Jianghao Lin",
"Dongdong Ge",
"Yinyu Ye"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-02-12T00:00:00 | https://arxiv.org/abs/2603.09983 | https://arxiv.org/pdf/2603.09983v1 | 2603.09983 | 10.48550/arXiv.2603.09983 | 1 | 0 | true | https://github.com/lshAlgorithm/MoE-SpAc | arXiv.org | 0.7314 |
07d03f57bc1ae6cdd9f65c6895083a47037543009da6a609916b37ce9f736c39 | [
"arxiv",
"semantic_scholar"
] | Training-free Dropout Sampling for Semantic Token Acceptance in Speculative Decoding | 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 ap... | [
"Jeongtae Lee",
"Minjung Jo",
"Hyunjoon Jeong",
"Gunho Park",
"Sunghyeon Woo",
"Joonghoon Kim",
"Se Jung Kwon",
"Dongsoo Lee"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-02-11T00:00:00 | https://arxiv.org/abs/2603.03333 | https://arxiv.org/pdf/2603.03333v1 | 2603.03333 | 10.48550/arXiv.2603.03333 | 0 | 0 | false | null | arXiv.org | 0.4721 |
44af42eb5fe0a5d159f7cdf5e9750863cf446e41b32b72954094fcb96ff61429 | [
"arxiv",
"semantic_scholar"
] | StreamServe: Adaptive Speculative Flows for Low-Latency Disaggregated LLM Serving | Efficient LLM serving must balance throughput and latency across diverse, bursty workloads. We introduce StreamServe, a disaggregated prefill decode serving architecture that combines metric aware routing across compute lanes with adaptive speculative decoding that tunes speculation depth online from runtime signals. S... | [
"Satyam Kumar",
"Arpit Singh Gautam",
"Kailash Talreja",
"Saurabh Jha"
] | [
"cs.DC",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-11T00:00:00 | https://arxiv.org/abs/2604.09562 | https://arxiv.org/pdf/2604.09562v1 | 2604.09562 | 10.48550/arXiv.2604.09562 | 0 | 0 | false | null | arXiv.org | 0.4721 |
f41985dc055efb96a1291d4e6a3cced3615908215c8532e7e515d27ae5eaad48 | [
"arxiv",
"semantic_scholar"
] | SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding | Speculative Decoding (SD) has emerged as a critical technique for accelerating Large Language Model (LLM) inference. Unlike deterministic system optimizations, SD performance is inherently data-dependent, meaning that diverse and representative workloads are essential for accurately measuring its effectiveness. Existin... | [
"Talor Abramovich",
"Maor Ashkenazi",
"Izzy Putterman",
"Benjamin Chislett",
"Tiyasa Mitra",
"Bita Darvish Rouhani",
"Ran Zilberstein",
"Yonatan Geifman"
] | [
"cs.DC",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-10T00:00:00 | https://arxiv.org/abs/2604.09557 | https://arxiv.org/pdf/2604.09557v2 | 2604.09557 | 10.48550/arXiv.2604.09557 | 2 | 1 | false | null | arXiv.org | 0.4709 |
91d76cd55b98f485a8d9d828dc0284cb4bc1d2f4892450aff3fc42d234b84fbe | [
"arxiv",
"semantic_scholar"
] | Benchmarking the Energy Savings with Speculative Decoding Strategies | Speculative decoding has emerged as an effective method to reduce latency and inference cost of LLM inferences. However, there has been inadequate attention towards the energy requirements of these models. To address this gap, this paper presents a comprehensive survey of energy requirements of speculative decoding str... | [
"Rohit Dutta",
"Paramita Koley",
"Soham Poddar",
"Janardan Misra",
"Sanjay Podder",
"Naveen Balani",
"Saptarshi Ghosh",
"Niloy Ganguly"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-02-09T00:00:00 | https://arxiv.org/abs/2602.09113 | https://arxiv.org/pdf/2602.09113v1 | 2602.09113 | 10.48550/arXiv.2602.09113 | 0 | 0 | false | null | Conference of the European Chapter of the Association for Computational Linguistics | 0.4698 |
754971f7a2e1001cff3b1343c3a7c0299b9876bd6e6f05f2f92eea981d717fb1 | [
"arxiv",
"semantic_scholar"
] | Compiler-Assisted Speculative Sampling for Accelerated LLM Inference on Heterogeneous Edge Devices | LLM deployment on resource-constrained edge devices faces severe latency constraints, particularly in real-time applications where delayed responses can compromise safety or usability. Among many approaches to mitigate the inefficiencies of sequential token-by-token generation, Speculative Decoding (SD) has emerged as ... | [
"Alejandro Ruiz y Mesa",
"Guilherme Korol",
"Moritz Riesterer",
"João Paulo Cardoso de Lima",
"Jeronimo Castrillon"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-08T00:00:00 | https://arxiv.org/abs/2602.08060 | https://arxiv.org/pdf/2602.08060v2 | 2602.08060 | 10.48550/arXiv.2602.08060 | 0 | 0 | false | null | arXiv.org | 0.4686 |
5e54c53680c7efd6fbb43b4542d5b82996063a9b9f92b6f1b4aba13739fa942f | [
"arxiv",
"semantic_scholar"
] | Vegas: Self-Speculative Decoding with Verification-Guided Sparse Attention | Long-context large language model (LLM) inference has become the norm for today's AI applications. However, it is severely bottlenecked by the increasing memory demands of its KV cache. Previous works have shown that self-speculative decoding with sparse attention, where tokens are drafted using a subset of the KV cach... | [
"Yikang Yue",
"Yuqi Xue",
"Jian Huang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-06T00:00:00 | https://arxiv.org/abs/2602.07223 | https://arxiv.org/pdf/2602.07223v2 | 2602.07223 | null | 0 | 0 | true | https://github.com/platformxlab/vegas | null | 0.5511 |
092017e2097c57adce599a86e9bb7123673ae9cc3667d737dfd01c581e146d1b | [
"arxiv",
"semantic_scholar"
] | Anchored Decoding: Provably Reducing Copyright Risk for Any Language Model | Language models (LMs) tend to memorize portions of their training data and emit verbatim spans. When the underlying sources are sensitive or copyright-protected, such reproduction raises issues of consent and compensation for creators and compliance risks for developers. We propose Anchored Decoding, a plug-and-play in... | [
"Jacqueline He",
"Jonathan Hayase",
"Wen-tau Yih",
"Sewoong Oh",
"Luke Zettlemoyer",
"Pang Wei Koh"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-02-06T00:00:00 | https://arxiv.org/abs/2602.07120 | https://arxiv.org/pdf/2602.07120v2 | 2602.07120 | 10.48550/arXiv.2602.07120 | 0 | 0 | true | https://github.com/jacqueline-he/anchored-decoding | arXiv.org | 0.7207 |
fa131ba8f9cc9b5f3c200aadceabdbf6675f23d692788be68da85b430825700d | [
"arxiv",
"semantic_scholar"
] | DFlash: Block Diffusion for Flash Speculative Decoding | Autoregressive large language models (LLMs) deliver strong performance but require inherently sequential decoding, leading to high inference latency and poor GPU utilization. Speculative decoding mitigates this bottleneck by using a fast draft model whose outputs are verified in parallel by the target LLM; however, exi... | [
"Jian Chen",
"Yesheng Liang",
"Zhijian Liu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-02-05T00:00:00 | https://arxiv.org/abs/2602.06036 | https://arxiv.org/pdf/2602.06036v2 | 2602.06036 | 10.48550/arXiv.2602.06036 | 33 | 14 | true | https://github.com/z-lab/dflash | arXiv.org | 0.719 |
efd442573bfe236afe3518764162863d47ee0a03543753f6fe9a95538d2c2342 | [
"arxiv",
"semantic_scholar"
] | SDFP: Speculative Decoding with FIT-Pruned Models for Training-Free and Plug-and-Play LLM Acceleration | Large language models (LLMs) underpin interactive multimedia applications such as captioning, retrieval, recommendation, and creative content generation, yet their autoregressive decoding incurs substantial latency. Speculative decoding reduces latency using a lightweight draft model, but deployment is often limited by... | [
"Hanyu Wei",
"Zunhai Su",
"Peng Lu",
"Chao Li",
"Spandan Tiwari",
"Ashish Sirasao",
"Yuhan Dong"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-02-05T00:00:00 | https://arxiv.org/abs/2602.05499 | https://arxiv.org/pdf/2602.05499v1 | 2602.05499 | 10.48550/arXiv.2602.05499 | 0 | 0 | false | null | arXiv.org | 0.4652 |
b4513f6f0d82b39b39430b8c68903a284d0eddfbd3884947b569a90266aeeb4a | [
"arxiv",
"semantic_scholar"
] | Variational Speculative Decoding: Rethinking Draft Training from Token Likelihood to Sequence Acceptance | Speculative decoding accelerates inference for (M)LLMs, yet a training-decoding discrepancy persists: while existing methods optimize single greedy trajectories, decoding involves verifying and ranking multiple sampled draft paths. We propose Variational Speculative Decoding (VSD), formulating draft training as variati... | [
"Xiandong Zou",
"Jianshu Li",
"Jing Huang",
"Pan Zhou"
] | [
"cs.LG",
"cs.AI",
"math.PR"
] | [
"Computer Science",
"Mathematics"
] | 2026-02-05T00:00:00 | https://arxiv.org/abs/2602.05774 | https://arxiv.org/pdf/2602.05774v4 | 2602.05774 | null | 0 | 0 | false | null | null | 0.296 |
1f5da4d1db6c0e6343c3dd91b420c3358309f3bca24067be9d30d3d1bbaad33f | [
"arxiv",
"semantic_scholar"
] | TIDE: Temporal Incremental Draft Engine for Self-Improving LLM Inference | Speculative decoding can substantially accelerate LLM inference, but realizing its benefits in practice is challenging due to evolving workloads and system-level constraints. We present TIDE (Temporal Incremental Draft Engine), a serving-engine-native framework that integrates online draft adaptation directly into high... | [
"Jiyoung Park",
"Hankyu Jang",
"Changseok Song",
"Wookeun Jung"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-05T00:00:00 | https://arxiv.org/abs/2602.05145 | https://arxiv.org/pdf/2602.05145v1 | 2602.05145 | 10.48550/arXiv.2602.05145 | 1 | 0 | false | null | arXiv.org | 0.4652 |
b0f2c8b875e126495561622b98c904e33b28b54775b4984e682f473f252452aa | [
"arxiv",
"semantic_scholar"
] | LycheeDecode: Accelerating Long-Context LLM Inference via Hybrid-Head Sparse Decoding | The proliferation of long-context large language models (LLMs) exposes a key bottleneck: the rapidly expanding key-value cache during decoding, which imposes heavy memory and latency costs. While recent approaches attempt to alleviate this by sharing a single set of crucial tokens across layers, such coarse-grained sha... | [
"Gang Lin",
"Dongfang Li",
"Zhuoen Chen",
"Yukun Shi",
"Xuhui Chen",
"Baotian Hu",
"Min Zhang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-04T00:00:00 | https://arxiv.org/abs/2602.04541 | https://arxiv.org/pdf/2602.04541v1 | 2602.04541 | 10.48550/arXiv.2602.04541 | 3 | 0 | false | null | arXiv.org | 0.4641 |
56ab42c904e0b27e7cd5a547cc0b176f6ea976631f9580d89704d71bd3bfd8dc | [
"arxiv",
"semantic_scholar"
] | Beyond Tokens: Semantic-Aware Speculative Decoding for Efficient Inference by Probing Internal States | Large Language Models (LLMs) achieve strong performance across many tasks but suffer from high inference latency due to autoregressive decoding. The issue is exacerbated in Large Reasoning Models (LRMs), which generate lengthy chains of thought. While speculative decoding accelerates inference by drafting and verifying... | [
"Ximing Dong",
"Shaowei Wang",
"Dayi Lin",
"Boyuan Chen",
"Ahmed E. Hassan"
] | [
"cs.CL",
"cs.PF"
] | [
"Computer Science"
] | 2026-02-03T00:00:00 | https://arxiv.org/abs/2602.03708 | https://arxiv.org/pdf/2602.03708v2 | 2602.03708 | 10.48550/arXiv.2602.03708 | 0 | 0 | false | null | arXiv.org | 0.4629 |
61cc828d9569c50d6d74243c810300b60afc6d63d8278674a18a2478358f09ac | [
"arxiv",
"semantic_scholar"
] | Make Every Draft Count: Hidden State based Speculative Decoding | Speculative decoding has emerged as a pivotal technique to accelerate LLM inference by employing a lightweight draft model to generate candidate tokens that are subsequently verified by the target model in parallel. However, while this paradigm successfully increases the arithmetic intensity of memory-bound inference, ... | [
"Yuetao Chen",
"Xuliang Wang",
"Xinzhou Zheng",
"Ming Li",
"Peng Wang",
"Hong Xu"
] | [
"cs.CL",
"cs.AI",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2026-02-02T00:00:00 | https://arxiv.org/abs/2602.21224 | https://arxiv.org/pdf/2602.21224v1 | 2602.21224 | 10.48550/arXiv.2602.21224 | 1 | 0 | false | null | arXiv.org | 0.4618 |
b4f398f7a2d4c0ae59e38cc0ea5a6ce47816c77d0a7e11e49ef64e1e9ad1fe0c | [
"arxiv",
"semantic_scholar"
] | PRISM: Parametrically Refactoring Inference for Speculative Sampling Draft Models | Large Language Models (LLMs), constrained by their auto-regressive nature, suffer from slow decoding. Speculative decoding methods have emerged as a promising solution to accelerate LLM decoding, attracting attention from both systems and AI research communities. Recently, the pursuit of better draft quality has driven... | [
"Xuliang Wang",
"Yuetao Chen",
"Maochan Zhen",
"Fang Liu",
"Xinzhou Zheng",
"Xingwu Liu",
"Hong Xu",
"Ming Li"
] | [
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2026-02-02T00:00:00 | https://arxiv.org/abs/2602.01762 | https://arxiv.org/pdf/2602.01762v1 | 2602.01762 | 10.48550/arXiv.2602.01762 | 0 | 0 | false | null | arXiv.org | 0.4618 |
769e41cd1d22874d58fb8564839543c8a6179b06ca1fbc86a685124cb1e6449f | [
"arxiv",
"semantic_scholar"
] | PACER: Blockwise Pre-verification for Speculative Decoding with Adaptive Length | Speculative decoding (SD) is a powerful technique for accelerating the inference process of large language models (LLMs) without sacrificing accuracy. Typically, SD employs a small draft model to generate a fixed number of draft tokens, which are then verified in parallel by the target model. However, our experiments r... | [
"Situo Zhang",
"Yifan Zhang",
"Zichen Zhu",
"Hankun Wang",
"Da Ma",
"Danyang Zhang",
"Lu Chen",
"Kai Yu"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-01T00:00:00 | https://arxiv.org/abs/2602.01274 | https://arxiv.org/pdf/2602.01274v1 | 2602.01274 | 10.48550/arXiv.2602.01274 | 0 | 0 | false | null | arXiv.org | 0.4606 |
89d1e1fc975cced6084cc06ce3ffba8e4ba20a8b94bfd5fd49714bbf63298b44 | [
"arxiv",
"semantic_scholar"
] | SAGE: Accelerating Vision-Language Models via Entropy-Guided Adaptive Speculative Decoding | Speculative decoding has emerged as a promising approach to accelerate inference in vision-language models (VLMs) by enabling parallel verification of multiple draft tokens. However, existing methods rely on static tree structures that remain fixed throughout the decoding process, failing to adapt to the varying predic... | [
"Yujia Tong",
"Tian Zhang",
"Yunyang Wan",
"Kaiwei Lin",
"Jingling Yuan",
"Chuang Hu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-01-31T00:00:00 | https://arxiv.org/abs/2602.00523 | https://arxiv.org/pdf/2602.00523v1 | 2602.00523 | 10.48550/arXiv.2602.00523 | 1 | 0 | false | null | arXiv.org | 0.4595 |
9a5688d2f937cce7542260fd24d7eadb2ff8471d3cb861d072d4477441b2eb39 | [
"arxiv",
"semantic_scholar"
] | LLMs as High-Dimensional Nonlinear Autoregressive Models with Attention: Training, Alignment and Inference | Large language models (LLMs) based on transformer architectures are typically described through collections of architectural components and training procedures, obscuring their underlying computational structure. This review article provides a concise mathematical reference for researchers seeking an explicit, equation... | [
"Vikram Krishnamurthy"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"eess.SP"
] | [
"Computer Science",
"Engineering"
] | 2026-01-31T00:00:00 | https://arxiv.org/abs/2602.00426 | https://arxiv.org/pdf/2602.00426v1 | 2602.00426 | 10.48550/arXiv.2602.00426 | 1 | 0 | false | null | arXiv.org | 0.4595 |
f7a595a710135729e69c3c16bd32c089b67df85f11132127cf0243363a1df9a4 | [
"arxiv",
"semantic_scholar"
] | TriSpec: Ternary Speculative Decoding via Lightweight Proxy Verification | Inference efficiency in Large Language Models (LLMs) is fundamentally limited by their serial, autoregressive generation, especially as reasoning becomes a key capability and response sequences grow longer. Speculative decoding (SD) offers a powerful solution, providing significant speed-ups through its lightweight dra... | [
"Haoyun Jiang",
"Junqi He",
"Feng Hong",
"Xinlong Yang",
"Jianwei Zhang",
"Zheng Li",
"Zhengyang Zhuge",
"Zhiyong Chen",
"Bo Han",
"Junyang Lin",
"Jiangchao Yao"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-01-30T00:00:00 | https://arxiv.org/abs/2601.23180 | https://arxiv.org/pdf/2601.23180v1 | 2601.23180 | 10.48550/arXiv.2601.23180 | 1 | 0 | false | null | arXiv.org | 0.4583 |
e653377ac773be3448a90ab49ee5a50810cfdcb3c85d636dfe531e5ae857cf2a | [
"arxiv",
"semantic_scholar"
] | StarSD: One-for-Many Speculative Decoding | Speculative decoding accelerates autoregressive generation by separating token proposal from verification, but most existing approaches are designed for single-node execution and do not scale well to multi-accelerator clusters used for serving modern Large Language Models (LLMs). We present StarSD, a one-for-many specu... | [
"Junhao He",
"Feiran You",
"Hongyang Du"
] | [
"eess.SY"
] | [
"Engineering",
"Computer Science"
] | 2026-01-29T00:00:00 | https://arxiv.org/abs/2601.21622 | https://arxiv.org/pdf/2601.21622v1 | 2601.21622 | 10.48550/arXiv.2601.21622 | 0 | 0 | false | null | arXiv.org | 0.4572 |
ce489f83ca124233083ae64ef35a607f69743de5cee2a10a19c3535b29f0d3ac | [
"arxiv",
"semantic_scholar"
] | TABED: Test-Time Adaptive Ensemble Drafting for Robust Speculative Decoding in LVLMs | Speculative decoding (SD) has proven effective for accelerating LLM inference by quickly generating draft tokens and verifying them in parallel. However, SD remains largely unexplored for Large Vision-Language Models (LVLMs), which extend LLMs to process both image and text prompts. To address this gap, we benchmark ex... | [
"Minjae Lee",
"Wonjun Kang",
"Byeongkeun Ahn",
"Christian Classen",
"Kevin Galim",
"Seunghyuk Oh",
"Minghao Yan",
"Hyung Il Koo",
"Kangwook Lee"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-01-28T00:00:00 | https://arxiv.org/abs/2601.20357 | https://arxiv.org/pdf/2601.20357v1 | 2601.20357 | 10.48550/arXiv.2601.20357 | 0 | 0 | true | https://github.com/furiosa-ai/TABED | Conference of the European Chapter of the Association for Computational Linguistics | 0.7048 |
307d3d85a4d5d45ddcfa6a0e380c060eeca2db027fe36c0aa0d6a19e4db304f8 | [
"arxiv",
"semantic_scholar"
] | DART: Diffusion-Inspired Speculative Decoding for Fast LLM Inference | Speculative decoding is an effective and lossless approach for accelerating LLM inference. However, existing widely adopted model-based draft designs, such as EAGLE3, improve accuracy at the cost of multi-step autoregressive inference, resulting in high drafting latency and ultimately rendering the drafting stage itsel... | [
"Fuliang Liu",
"Xue Li",
"Ketai Zhao",
"Yinxi Gao",
"Ziyan Zhou",
"Zhonghui Zhang",
"Zhibin Wang",
"Wanchun Dou",
"Sheng Zhong",
"Chen Tian"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-01-27T00:00:00 | https://arxiv.org/abs/2601.19278 | https://arxiv.org/pdf/2601.19278v1 | 2601.19278 | 10.48550/arXiv.2601.19278 | 11 | 2 | true | https://github.com/fvliang/DART | arXiv.org | 0.703 |
ec1cffae932cd51406ec96fbfe04ce78dd9a2df67f2b282cc6fde30174c0a006 | [
"arxiv",
"semantic_scholar"
] | LLM-42: Enabling Determinism in LLM Inference with Verified Speculation | In LLM inference, the same prompt may yield different outputs across different runs. At the system level, this non-determinism arises from floating-point non-associativity combined with dynamic batching and GPU kernels whose reduction orders vary with batch size. A straightforward way to eliminate non-determinism is to... | [
"Raja Gond",
"Aditya K Kamath",
"Ramachandran Ramjee",
"Ashish Panwar"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2026-01-25T00:00:00 | https://arxiv.org/abs/2601.17768 | https://arxiv.org/pdf/2601.17768v2 | 2601.17768 | 10.48550/arXiv.2601.17768 | 7 | 0 | true | https://github.com/microsoft/llm-42 | arXiv.org | 0.6995 |
440d3337d6a8a10ae8eabd7da0d34d6b7c5575cc4fa33589931467ba36e790fd | [
"arxiv",
"semantic_scholar"
] | MARS: Unleashing the Power of Speculative Decoding via Margin-Aware Verification | Speculative Decoding (SD) accelerates autoregressive large language model (LLM) inference by decoupling generation and verification. While recent methods improve draft quality by tightly coupling the drafter with the target model, the verification mechanism itself remains largely unchanged, relying on strict token-leve... | [
"Jingwei Song",
"Xinyu Wang",
"Hanbin Wang",
"Xiaoxuan Lei",
"Bill Shi",
"Shixin Han",
"Eric Yang",
"Xiao-Wen Chang",
"Lynn Ai"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-01-21T00:00:00 | https://arxiv.org/abs/2601.15498 | https://arxiv.org/pdf/2601.15498v2 | 2601.15498 | 10.48550/arXiv.2601.15498 | 1 | 0 | true | https://github.com/5SSjw/MARS | arXiv.org | 0.6924 |
d389a309b0d387da23092e00ff5101132f9df70d303a178d43587fb6f595264b | [
"arxiv",
"semantic_scholar"
] | Stabilizer-Assisted Inactivation Decoding of Quantum Error-Correcting Codes with Erasures | In this work, we develop a reduced complexity maximum likelihood (ML) decoder for quantum low-density parity-check (QLDPC) codes over erasures. Our decoder combines classical inactivation decoding, which integrates peeling with symbolic guessing, with a new dual peeling procedure. In the dual peeling stage, we perform ... | [
"Giulio Pech",
"Mert Gökduman",
"Hanwen Yao",
"Henry D. Pfister"
] | [
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2026-01-20T00:00:00 | https://arxiv.org/abs/2601.14236 | https://arxiv.org/pdf/2601.14236v1 | 2601.14236 | 10.48550/arXiv.2601.14236 | 0 | 0 | false | null | arXiv.org | 0.4469 |
aff354abaf6986106576eaed09db112bc85046fcf08c4a59ba54d6b5d51574aa | [
"arxiv",
"semantic_scholar"
] | WISP: Waste- and Interference-Suppressed Distributed Speculative LLM Serving at the Edge via Dynamic Drafting and SLO-Aware Batching | As Large Language Models (LLMs) become increasingly accessible to end users, an ever-growing number of inference requests are initiated from edge devices and computed on centralized GPU clusters. However, the resulting exponential growth in computation workload is placing significant strain on data centers, while edge ... | [
"Xiangchen Li",
"Jiakun Fan",
"Qingyuan Wang",
"Dimitrios Spatharakis",
"Saeid Ghafouri",
"Hans Vandierendonck",
"Deepu John",
"Bo Ji",
"Ali R. Butt",
"Dimitrios S. Nikolopoulos"
] | [
"cs.DC",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-15T00:00:00 | https://arxiv.org/abs/2601.11652 | https://arxiv.org/pdf/2601.11652v2 | 2601.11652 | 10.48550/arXiv.2601.11652 | 0 | 0 | false | null | null | 0.2807 |
005c13f4225924a16d6ce92bd7dbd5537faca06c2ed425b20777f98bb15d6ff5 | [
"arxiv",
"semantic_scholar"
] | Annealed Relaxation of Speculative Decoding for Faster Autoregressive Image Generation | Despite significant progress in autoregressive image generation, inference remains slow due to the sequential nature of AR models and the ambiguity of image tokens, even when using speculative decoding. Recent works attempt to address this with relaxed speculative decoding but lack theoretical grounding. In this paper,... | [
"Xingyao Li",
"Fengzhuo Zhang",
"Cunxiao Du",
"Hui Ji"
] | [
"cs.CV",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-01-14T00:00:00 | https://arxiv.org/abs/2601.09212 | https://arxiv.org/pdf/2601.09212v1 | 2601.09212 | 10.48550/arXiv.2601.09212 | 0 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.44 |
95ee200f10728e73faea9428052361304de5bd18298a44e8e342faf36c033172 | [
"arxiv",
"semantic_scholar"
] | HIPPO: Accelerating Video Large Language Models Inference via Holistic-aware Parallel Speculative Decoding | Speculative decoding (SD) has emerged as a promising approach to accelerate LLM inference without sacrificing output quality. Existing SD methods tailored for video-LLMs primarily focus on pruning redundant visual tokens to mitigate the computational burden of massive visual inputs. However, existing methods do not ach... | [
"Qitan Lv",
"Tianyu Liu",
"Wen Wu",
"Xuenan Xu",
"Bowen Zhou",
"Feng Wu",
"Chao Zhang"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-13T00:00:00 | https://arxiv.org/abs/2601.08273 | https://arxiv.org/pdf/2601.08273v1 | 2601.08273 | 10.48550/arXiv.2601.08273 | 2 | 0 | false | null | arXiv.org | 0.4389 |
6d78267baef039f0be8c9a0a4c9abdc16090a78de7e7488b42ba5562f36586cd | [
"arxiv",
"semantic_scholar"
] | TALON: Confidence-Aware Speculative Decoding with Adaptive Token Trees | Speculative decoding (SD) has become a standard technique for accelerating LLM inference without sacrificing output quality. Recent advances in speculative decoding have shifted from sequential chain-based drafting to tree-structured generation, where the draft model constructs a tree of candidate tokens to explore mul... | [
"Tianyu Liu",
"Qitan Lv",
"Yuhao Shen",
"Xiao Sun",
"Xiaoyan Sun"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-01-12T00:00:00 | https://arxiv.org/abs/2601.07353 | https://arxiv.org/pdf/2601.07353v1 | 2601.07353 | 10.48550/arXiv.2601.07353 | 8 | 0 | false | null | arXiv.org | 0.4377 |
5351116365b1b6f228e89daffd95a548d2b485d68f4a15f38abca686c6cf73b9 | [
"arxiv",
"semantic_scholar"
] | Overcoming Joint Intractability with Lossless Hierarchical Speculative Decoding | Verification is a key bottleneck in improving inference speed while maintaining distribution fidelity in Speculative Decoding. Recent work has shown that sequence-level verification leads to a higher number of accepted tokens compared to token-wise verification. However, existing solutions often rely on surrogate appro... | [
"Yuxuan Zhou",
"Fei Huang",
"Heng Li",
"Fengyi Wu",
"Tianyu Wang",
"Jianwei Zhang",
"Junyang Lin",
"Zhi-Qi Cheng"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-01-09T00:00:00 | https://arxiv.org/abs/2601.05724 | https://arxiv.org/pdf/2601.05724v2 | 2601.05724 | 10.48550/arXiv.2601.05724 | 1 | 0 | true | https://github.com/ZhouYuxuanYX/Hierarchical-Speculative-Decoding | arXiv.org | 0.6711 |
06f5a3e1424f3adfa47cfcf8890829cbc82a3d8d5ff2f3aebf5909ec5beaf65a | [
"arxiv",
"semantic_scholar"
] | Multi-Scale Local Speculative Decoding for Image Generation | Autoregressive (AR) models have achieved remarkable success in image synthesis, yet their sequential nature imposes significant latency constraints. Speculative Decoding offers a promising avenue for acceleration, but existing approaches are limited by token-level ambiguity and lack of spatial awareness. In this work, ... | [
"Elia Peruzzo",
"Guillaume Sautière",
"Amirhossein Habibian"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-01-08T00:00:00 | https://arxiv.org/abs/2601.05149 | https://arxiv.org/pdf/2601.05149v2 | 2601.05149 | 10.48550/arXiv.2601.05149 | 1 | 0 | false | null | arXiv.org | 0.4331 |
d46349153a3709ab81f615bc8c6471466fdbf2a9fdd4931a6860ab4049b92ff2 | [
"arxiv",
"semantic_scholar"
] | LoRA-Drop: Temporal LoRA Decoding for Efficient LLM Inference | Autoregressive large language models (LLMs) are bottlenecked by sequential decoding, where each new token typically requires executing all transformer layers. Existing dynamic-depth and layer-skipping methods reduce this cost, but often rely on auxiliary routing mechanisms or incur accuracy degradation when bypassed la... | [
"Hossein Rajabzadeh",
"Maryam Dialameh",
"Chul B. Park",
"Il-Min Kim",
"Hyock Ju Kwon"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-01-05T00:00:00 | https://arxiv.org/abs/2601.02569 | https://arxiv.org/pdf/2601.02569v1 | 2601.02569 | 10.48550/arXiv.2601.02569 | 0 | 0 | true | https://github.com/hosseinbv/LoRA-Drop.git | arXiv.org | 0.6641 |
b9dfa6ab794986bc558b147c423c668cee3f76d8115b4907b4751157b2b1a041 | [
"arxiv",
"semantic_scholar"
] | FlexSpec: Frozen Drafts Meet Evolving Targets in Edge-Cloud Collaborative LLM Speculative Decoding | Deploying large language models (LLMs) in mobile and edge computing environments is constrained by limited on-device resources, scarce wireless bandwidth, and frequent model evolution. Although edge-cloud collaborative inference with speculative decoding (SD) can reduce end-to-end latency by executing a lightweight dra... | [
"Yuchen Li",
"Rui Kong",
"Zhonghao Lyu",
"Qiyang Li",
"Xinran Chen",
"Hengyi Cai",
"Lingyong Yan",
"Shuaiqiang Wang",
"Jiashu Zhao",
"Guangxu Zhu",
"Linghe Kong",
"Guihai Chen",
"Haoyi Xiong",
"Dawei Yin"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2026-01-02T00:00:00 | https://arxiv.org/abs/2601.00644 | https://arxiv.org/pdf/2601.00644v1 | 2601.00644 | 10.48550/arXiv.2601.00644 | 3 | 0 | false | null | IEEE Transactions on Mobile Computing | 0.4263 |
6a37ecf101f70bd44f86bc1ec79283bf4ddba6aaab0277f37f8ea57d09725b37 | [
"arxiv",
"semantic_scholar"
] | Speculative Decoding: Performance or Illusion? | Speculative decoding (SD) has become a popular technique to accelerate Large Language Model (LLM) inference, yet its real-world effectiveness remains unclear as prior evaluations rely on research prototypes and unrealistically small batch sizes. We present, to our knowledge, the first systematic study of SD on a produc... | [
"Xiaoxuan Liu",
"Jiaxiang Yu",
"Jongseok Park",
"Ion Stoica",
"Alvin Cheung"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-31T00:00:00 | https://arxiv.org/abs/2601.11580 | https://arxiv.org/pdf/2601.11580v2 | 2601.11580 | 10.48550/arXiv.2601.11580 | 8 | 0 | false | null | arXiv.org | 0.424 |
f7fa9d9ad5bb6239527942757ebe4d044080c4c6b72e3feb5e53ca0658b899a9 | [
"arxiv",
"semantic_scholar"
] | Entropy-Aware Speculative Decoding Toward Improved LLM Reasoning | Speculative decoding (SD) accelerates large language model (LLM) reasoning by using a small draft model to generate candidate tokens, which the target LLM either accepts directly or regenerates upon rejection. However, excessive alignment between the draft and target models constrains SD to the performance of the targe... | [
"Tiancheng Su",
"Meicong Zhang",
"Guoxiu He"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-29T00:00:00 | https://arxiv.org/abs/2512.23765 | https://arxiv.org/pdf/2512.23765v1 | 2512.23765 | 10.48550/arXiv.2512.23765 | 2 | 0 | false | null | arXiv.org | 0.4217 |
fea2f8148499c044a32dcd8138ca3c35c6bc6a8a61306f1861540e27e9d0e9a2 | [
"arxiv",
"semantic_scholar"
] | Yggdrasil: Bridging Dynamic Speculation and Static Runtime for Latency-Optimal Tree-Based LLM Decoding | Speculative decoding improves LLM inference by generating and verifying multiple tokens in parallel, but existing systems suffer from suboptimal performance due to a mismatch between dynamic speculation and static runtime assumptions. We present Yggdrasil, a co-designed system that enables latency-optimal speculative d... | [
"Yue Guan",
"Changming Yu",
"Shihan Fang",
"Weiming Hu",
"Zaifeng Pan",
"Zheng Wang",
"Zihan Liu",
"Yangjie Zhou",
"Yufei Ding",
"Minyi Guo",
"Jingwen Leng"
] | [
"cs.LG",
"cs.PL"
] | [
"Computer Science"
] | 2025-12-29T00:00:00 | https://arxiv.org/abs/2512.23858 | https://arxiv.org/pdf/2512.23858v1 | 2512.23858 | 10.48550/arXiv.2512.23858 | 3 | 1 | false | null | arXiv.org | 0.4217 |
ec7402bcbd806d51eeaa663fb73a85956c2e5fde1105175c8b980600fc939c97 | [
"arxiv",
"semantic_scholar"
] | Nightjar: Dynamic Adaptive Speculative Decoding for Large Language Models Serving | Speculative decoding (SD) accelerates LLM inference by verifying draft tokens in parallel. However, this method presents a critical trade-off: it improves throughput in low-load, memory-bound systems but degrades performance in high-load, compute-bound environments due to verification overhead. Existing speculative dec... | [
"Rui Li",
"Zhaoning Zhang",
"Libo Zhang",
"Huaimin Wang",
"Xiang Fu",
"Zhiquan Lai"
] | [
"cs.DC",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-27T00:00:00 | https://arxiv.org/abs/2512.22420 | https://arxiv.org/pdf/2512.22420v5 | 2512.22420 | 10.48550/arXiv.2512.22420 | 3 | 0 | false | null | Journal of systems architecture | 0.4194 |
eedb793a0825b82ee5f0476441917a0bf7f9d4bf4543530592c1d806d8bafcca | [
"arxiv",
"semantic_scholar"
] | Accelerate Speculative Decoding with Sparse Computation in Verification | Speculative decoding accelerates autoregressive language model inference by verifying multiple draft tokens in parallel. However, the verification stage often becomes the dominant computational bottleneck, especially for long-context inputs and mixture-of-experts (MoE) models. Existing sparsification methods are design... | [
"Jikai Wang",
"Jianchao Tan",
"Yuxuan Hu",
"Jiayu Qin",
"Yerui Sun",
"Yuchen Xie",
"Xunliang Cai",
"Juntao Li",
"Min Zhang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-12-26T00:00:00 | https://arxiv.org/abs/2512.21911 | https://arxiv.org/pdf/2512.21911v1 | 2512.21911 | 10.48550/arXiv.2512.21911 | 0 | 0 | false | null | arXiv.org | 0.4182 |
74a85d93819e9e43dbac2ac476bb113996d249e0cf4fdfe7e9e202e9e91bc4ba | [
"arxiv",
"semantic_scholar"
] | Fail Fast, Win Big: Rethinking the Drafting Strategy in Speculative Decoding via Diffusion LLMs | Diffusion Large Language Models (dLLMs) offer fast, parallel token generation, but their standalone use is plagued by an inherent efficiency-quality tradeoff. We show that, if carefully applied, the attributes of dLLMs can actually be a strength for drafters in speculative decoding with autoregressive (AR) verifiers. O... | [
"Rui Pan",
"Zhuofu Chen",
"Hongyi Liu",
"Arvind Krishnamurthy",
"Ravi Netravali"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2025-12-23T00:00:00 | https://arxiv.org/abs/2512.20573 | https://arxiv.org/pdf/2512.20573v3 | 2512.20573 | 10.48550/arXiv.2512.20573 | 4 | 0 | true | https://github.com/ruipeterpan/failfast | arXiv.org | 0.641 |
c8c14eaa0db49706c434427c1ad212c0b3bdb8f610474766adc2115a0846be5a | [
"arxiv",
"semantic_scholar"
] | Fast Collaborative Inference via Distributed Speculative Decoding | Speculative decoding accelerates large language model (LLM) inference by allowing a small draft model to predict multiple future tokens for verification by a larger target model. In AI-native radio access networks (AI-RAN), this enables device-edge collaborative inference but introduces significant uplink overhead, as ... | [
"Ce Zheng",
"Ke Zhang",
"Chen Sun",
"Wenqi Zhang",
"Qiong Liu",
"Angesom Ataklity Tesfay"
] | [
"eess.SP"
] | [
"Engineering"
] | 2025-12-18T00:00:00 | https://arxiv.org/abs/2512.16273 | https://arxiv.org/pdf/2512.16273v2 | 2512.16273 | 10.1016/j.jiixd.2025.12.008 | 3 | 0 | false | null | Journal of Information and Intelligence | 0.4091 |
499684e1a913c91000964b15c55641304481c53ac2a6d836d928a4bc0ca45e41 | [
"arxiv",
"semantic_scholar"
] | Efficient Adaptive Rejection Sampling for Accelerating Speculative Decoding in Large Language Models | Speculative Decoding is a prominent technique for accelerating the autoregressive inference of large language models (LLMs) by employing a fast draft model to propose candidate token sequences and a large target model to verify them in parallel. However, its core component -- the rejection sampling mechanism -- relies ... | [
"Chendong Sun",
"Ali Mao",
"Lei Xu",
"mingmin Chen"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-15T00:00:00 | https://arxiv.org/abs/2512.13194 | https://arxiv.org/pdf/2512.13194v3 | 2512.13194 | 10.48550/arXiv.2512.13194 | 1 | 0 | false | null | arXiv.org | 0.4056 |
30507c9f738661addfb8d8658d3369800663d30373e2958894fc3615fac4659d | [
"arxiv",
"semantic_scholar"
] | TS-DP: Reinforcement Speculative Decoding For Temporal Adaptive Diffusion Policy Acceleration | Diffusion Policy (DP) excels in embodied control but suffers from high inference latency and computational cost due to multiple iterative denoising steps. The temporal complexity of embodied tasks demands a dynamic and adaptable computation mode. Static and lossy acceleration methods, such as quantization, fail to hand... | [
"Ye Li",
"Jiahe Feng",
"Yuan Meng",
"Kangye Ji",
"Chen Tang",
"Xinwan Wen",
"Shutao Xia",
"Zhi Wang",
"Wenwu Zhu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-13T00:00:00 | https://arxiv.org/abs/2512.15773 | https://arxiv.org/pdf/2512.15773v1 | 2512.15773 | 10.48550/arXiv.2512.15773 | 4 | 0 | false | null | arXiv.org | 0.4033 |
59a03ddf2e16daeaa14dc6cc178590dca8c3d72eca7829aa1b8c19168da8b458 | [
"arxiv",
"semantic_scholar"
] | AdaSD: Adaptive Speculative Decoding for Efficient Language Model Inference | Large language models (LLMs) have achieved remarkable performance across a wide range of tasks, but their increasing parameter sizes significantly slow down inference. Speculative decoding mitigates this issue by leveraging a smaller draft model to predict candidate tokens, which are then verified by a larger target mo... | [
"Kuan-Wei Lu",
"Ding-Yong Hong",
"Pangfeng Liu",
"Jan-Jan Wu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-12-12T00:00:00 | https://arxiv.org/abs/2512.11280 | https://arxiv.org/pdf/2512.11280v2 | 2512.11280 | 10.48550/arXiv.2512.11280 | 1 | 0 | false | null | arXiv.org | 0.4022 |
67e315d660d5f1469f1263b315c90fdec539f557aa3179f8a111ba7052ab0cea | [
"arxiv",
"semantic_scholar"
] | Speculative Decoding Speed-of-Light: Optimal Lower Bounds via Branching Random Walks | Speculative generation has emerged as a promising technique to accelerate inference in large language models (LLMs) by leveraging parallelism to verify multiple draft tokens simultaneously. However, the fundamental limits on the achievable speedup remain poorly understood. In this work, we establish the first ``tight''... | [
"Sergey Pankratov",
"Dan Alistarh"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-12-12T00:00:00 | https://arxiv.org/abs/2512.11718 | https://arxiv.org/pdf/2512.11718v1 | 2512.11718 | 10.48550/arXiv.2512.11718 | 0 | 0 | false | null | Conference of the European Chapter of the Association for Computational Linguistics | 0.4022 |
670a39eec13d5f66aa9e877ad2f1d84fbcb03bf2c49eea0cf0014f0004c268c8 | [
"arxiv",
"semantic_scholar"
] | CXL-SpecKV: A Disaggregated FPGA Speculative KV-Cache for Datacenter LLM Serving | Large Language Models (LLMs) have revolutionized natural language processing tasks, but their deployment in datacenter environments faces significant challenges due to the massive memory requirements of key-value (KV) caches. During the autoregressive decoding process, KV caches consume substantial GPU memory, limiting... | [
"Dong Liu",
"Yanxuan Yu"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2025-12-11T00:00:00 | https://arxiv.org/abs/2512.11920 | https://arxiv.org/pdf/2512.11920v1 | 2512.11920 | 10.1145/3748173.3779188 | 7 | 0 | true | https://github.com/FastLM/CXL-SpecKV | Symposium on Field Programmable Gate Arrays | 0.6198 |
2e80d9b6fbf019bf647f2f04a06f3066ac60b83b23be734ec2b284fe908d6065 | [
"arxiv",
"semantic_scholar"
] | GoodSpeed: Optimizing Fair Goodput with Adaptive Speculative Decoding in Distributed Edge Inference | Large language models (LLMs) have revolutionized natural language processing, yet their high computational demands pose significant challenges for real-time inference, especially in multi-user server speculative decoding and resource-constrained environments. Speculative decoding has emerged as a promising technique to... | [
"Phuong Tran",
"Tzu-Hao Liu",
"Long Tan Le",
"Tung-Anh Nguyen",
"Van Quan La",
"Eason Yu",
"Han Shu",
"Choong Seon Hong",
"Nguyen H. Tran"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2025-12-10T00:00:00 | https://arxiv.org/abs/2512.09963 | https://arxiv.org/pdf/2512.09963v2 | 2512.09963 | 10.48550/arXiv.2512.09963 | 0 | 0 | false | null | arXiv.org | 0.3999 |
0557546589750fcbeab1a35e6bccf23929d4f4507b4d9454decdd8811ca5aefd | [
"arxiv",
"semantic_scholar"
] | A scalable and real-time neural decoder for topological quantum codes | Fault-tolerant quantum computing will require error rates far below those achievable with physical qubits. Quantum error correction (QEC) bridges this gap, but depends on decoders being simultaneously fast, accurate, and scalable. This combination of requirements remains unmet by a machine-learning decoder, nor by any ... | [
"Andrew W. Senior",
"Thomas Edlich",
"Francisco J. H. Heras",
"Lei M. Zhang",
"Oscar Higgott",
"James S. Spencer",
"Taylor Applebaum",
"Sam Blackwell",
"Justin Ledford",
"Akvilė Žemgulytė",
"Augustin Žídek",
"Noah Shutty",
"Andrew Cowie",
"Yin Li",
"George Holland",
"Peter Brooks",
"... | [
"quant-ph",
"cs.LG"
] | [
"Computer Science",
"Physics"
] | 2025-12-08T00:00:00 | https://arxiv.org/abs/2512.07737 | https://arxiv.org/pdf/2512.07737v2 | 2512.07737 | 10.48550/arXiv.2512.07737 | 19 | 2 | false | null | arXiv.org | 0.3976 |
442c163ae3ab78445c11b565f4c68cbc558b01e1bf82b7e0aa51bd34d7a50687 | [
"arxiv",
"semantic_scholar"
] | SJD++: Improved Speculative Jacobi Decoding for Training-free Acceleration of Discrete Auto-regressive Text-to-Image Generation | Large autoregressive models can generate high-quality, high-resolution images but suffer from slow generation speed, because these models require hundreds to thousands of sequential forward passes for next-token prediction during inference. To accelerate autoregressive text-to-image generation, we propose Speculative J... | [
"Yao Teng",
"Zhihuan Jiang",
"Han Shi",
"Xian Liu",
"Xuefei Ning",
"Guohao Dai",
"Yu Wang",
"Zhenguo Li",
"Xihui Liu"
] | [
"cs.CV"
] | [
"Medicine",
"Computer Science"
] | 2025-12-08T00:00:00 | https://arxiv.org/abs/2512.07503 | https://arxiv.org/pdf/2512.07503v1 | 2512.07503 | 10.48550/arXiv.2512.07503 | 0 | 0 | false | null | IEEE Transactions on Pattern Analysis and Machine Intelligence | 0.3976 |
be5a16eca63b9912582b7752969dbd11d89e3cda9093c17715c66ab5cd39ff9c | [
"arxiv",
"semantic_scholar"
] | SpecPV: Improving Self-Speculative Decoding for Long-Context Generation via Partial Verification | Growing demands from tasks like code generation, deep reasoning, and long-document understanding have made long-context generation a crucial capability for large language models (LLMs). Speculative decoding is one of the most direct and effective approaches for accelerating generation. It follows a draft-verify paradig... | [
"Zhendong Tan",
"Xingjun Zhang",
"Chaoyi Hu",
"Junjie Peng",
"Kun Xia"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-12-02T00:00:00 | https://arxiv.org/abs/2512.02337 | https://arxiv.org/pdf/2512.02337v1 | 2512.02337 | 10.48550/arXiv.2512.02337 | 4 | 0 | false | null | arXiv.org | 0.3907 |
bac73db2cf10fc389dccbcb151df7fbb6920d13207327a04aa286b38972c4e3b | [
"arxiv",
"semantic_scholar"
] | Accelerating Large-Scale Reasoning Model Inference with Sparse Self-Speculative Decoding | Reasoning language models have demonstrated remarkable capabilities on challenging tasks by generating elaborate chain-of-thought (CoT) solutions. However, such lengthy generation shifts the inference bottleneck from compute-bound to memory-bound. To generate each token, the model applies full attention to all previous... | [
"Yilong Zhao",
"Jiaming Tang",
"Kan Zhu",
"Zihao Ye",
"Chi-Chih Chang",
"Chaofan Lin",
"Jongseok Park",
"Guangxuan Xiao",
"Mohamed S. Abdelfattah",
"Mingyu Gao",
"Baris Kasikci",
"Song Han",
"Ion Stoica"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-01T00:00:00 | https://arxiv.org/abs/2512.01278 | https://arxiv.org/pdf/2512.01278v1 | 2512.01278 | 10.48550/arXiv.2512.01278 | 2 | 0 | false | null | arXiv.org | 0.3896 |
1812932d4c0c2e914ed10c58122b86492a9f2bfc70356a76770aae5a07324c65 | [
"arxiv",
"semantic_scholar"
] | Training-Free Loosely Speculative Decoding: Accepting Semantically Correct Drafts Beyond Exact Match | Large language models (LLMs) achieve strong performance across diverse tasks but suffer from high inference latency due to their autoregressive generation. Speculative Decoding (SPD) mitigates this issue by verifying candidate tokens in parallel from a smaller draft model, yet its strict exact-match verification discar... | [
"Jinze Li",
"Yixing Xu",
"Guanchen Li",
"Shuo Yang",
"Jinfeng Xu",
"Xuanwu Yin",
"Dong Li",
"Edith C. H. Ngai",
"Emad Barsoum"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-11-28T00:00:00 | https://arxiv.org/abs/2511.22972 | https://arxiv.org/pdf/2511.22972v3 | 2511.22972 | 10.48550/arXiv.2511.22972 | 8 | 2 | false | null | arXiv.org | 0.3861 |
7c109b21d12fa51179d73bf274895c52d4ac5a25433627e6e32fe929aa2f80c7 | [
"arxiv",
"semantic_scholar"
] | DSD: A Distributed Speculative Decoding Solution for Edge-Cloud Agile Large Model Serving | Large language model (LLM) inference often suffers from high decoding latency and limited scalability across heterogeneous edge-cloud environments. Existing speculative decoding (SD) techniques accelerate token generation but remain confined to single-node execution. We propose DSD, a distributed speculative decoding f... | [
"Fengze Yu",
"Leshu Li",
"Brad McDanel",
"Sai Qian Zhang"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2025-11-26T00:00:00 | https://arxiv.org/abs/2511.21669 | https://arxiv.org/pdf/2511.21669v2 | 2511.21669 | 10.48550/arXiv.2511.21669 | 4 | 1 | false | null | arXiv.org | 0.3839 |
c1e7923edff546035821afb67aea2c6cea26aa2ea99eea9cceceaaef82c18b42 | [
"arxiv",
"semantic_scholar"
] | Scaling LLM Speculative Decoding: Non-Autoregressive Forecasting in Large-Batch Scenarios | Speculative decoding accelerates LLM inference by utilizing otherwise idle computational resources during memory-to-chip data transfer. Current speculative decoding methods typically assume a considerable amount of available computing power, then generate a complex and massive draft tree using a small autoregressive la... | [
"Luohe Shi",
"Zuchao Li",
"Lefei Zhang",
"Baoyuan Qi",
"Guoming Liu",
"Hai Zhao"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-11-25T00:00:00 | https://arxiv.org/abs/2511.20340 | https://arxiv.org/pdf/2511.20340v1 | 2511.20340 | 10.48550/arXiv.2511.20340 | 2 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.3827 |
4b9f8426458debdbea1d191d159c55a38276c6df91da7a3f3883f67f4fb88a40 | [
"arxiv",
"semantic_scholar"
] | Reducing Latency of LLM Search Agent via Speculation-based Algorithm-System Co-Design | LLM-based search agents achieve strong performance but suffer from severe latency, as each step requires serialized LLM reasoning followed by action of tool execution. We revisit this bottleneck through the lens of speculation. While traditional predict-verify speculation paradigm can break serial execution, its benefi... | [
"Zixiao Huang",
"Wen Zeng",
"Tianyu Fu",
"Tengxuan Liu",
"Yizhou Sun",
"Ke Hong",
"Xinhao Yang",
"Chengchun Liu",
"Yan Li",
"Quanlu Zhang",
"Guohao Dai",
"Zhenhua Zhu",
"Yu Wang"
] | [
"cs.AI",
"cs.LG",
"cs.PF"
] | [
"Computer Science"
] | 2025-11-25T00:00:00 | https://arxiv.org/abs/2511.20048 | https://arxiv.org/pdf/2511.20048v1 | 2511.20048 | 10.48550/arXiv.2511.20048 | 1 | 1 | false | null | arXiv.org | 0.3827 |
0669f1c82d8e4cf5424ef9f2ab9e08268c702e0692afc844fa2d2e12c07c7380 | [
"arxiv",
"semantic_scholar"
] | Accelerating Time Series Foundation Models with Speculative Decoding | Modern web applications--from real-time content recommendation and dynamic pricing to CDN optimization--increasingly rely on time-series forecasting to deliver personalized experiences to billions of users. Large-scale Transformer-based models have achieved state-of-the-art performance in time-series forecasting but su... | [
"Pranav Subbaraman",
"Fang Sun",
"Yue Yao",
"Huacong Tang",
"Xiao Luo",
"Yizhou Sun"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-11-22T00:00:00 | https://arxiv.org/abs/2511.18191 | https://arxiv.org/pdf/2511.18191v1 | 2511.18191 | 10.48550/arXiv.2511.18191 | 1 | 1 | true | https://github.com/PranavSubbaraman/STRIDE | arXiv.org | 0.5861 |
42300a828df305c43b757c35800a31c6e2f426c43adb8614194d31602809506c | [
"arxiv",
"semantic_scholar"
] | Fast LLM Post-training via Decoupled and Fastest-of-N Speculation | Rollout dominates the training time in large language model (LLM) post-training, where the trained model is used to generate tokens given a batch of prompts. This work, SpecActor, achieves fast rollout with speculative decoding that deploys a fast draft path to accelerate the unparallelizable generation, while the corr... | [
"Rongxin Cheng",
"Kai Zhou",
"Xingda Wei",
"Siyuan Liu",
"Mingcong Han",
"Mingjing Ai",
"Yeju Zhou",
"Baoquan Zhong",
"Wencong Xiao",
"Rong Chen",
"Haibo Chen"
] | [
"cs.DC",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-20T00:00:00 | https://arxiv.org/abs/2511.16193 | https://arxiv.org/pdf/2511.16193v3 | 2511.16193 | 10.48550/arXiv.2511.16193 | 3 | 0 | false | null | arXiv.org | 0.377 |
4868fcf1f532f7e9defcefad5b634557cd8b43275d1cf72889cc0bfb8b6bd24b | [
"arxiv",
"semantic_scholar"
] | Comparative Analysis of Large Language Model Inference Serving Systems: A Performance Study of vLLM and HuggingFace TGI | The deployment of Large Language Models (LLMs) in production environments requires efficient inference serving systems that balance throughput, latency, and resource utilization. This paper presents a comprehensive empirical evaluation of two prominent open-source LLM serving frameworks: vLLM and HuggingFace Text Gener... | [
"Saicharan Kolluru"
] | [
"cs.LG",
"cs.DC",
"cs.PF"
] | [
"Computer Science"
] | 2025-11-17T00:00:00 | https://arxiv.org/abs/2511.17593 | https://arxiv.org/pdf/2511.17593v1 | 2511.17593 | 10.48550/arXiv.2511.17593 | 1 | 0 | true | null | arXiv.org | 0.5773 |
a47aaa8701ad8543fe841c4badb21ed10e029b487c238c6b4e48cdc2268d739b | [
"arxiv",
"semantic_scholar"
] | Beat the long tail: Distribution-Aware Speculative Decoding for RL Training | Reinforcement learning(RL) post-training has become essential for aligning large language models (LLMs), yet its efficiency is increasingly constrained by the rollout phase, where long trajectories are generated token by token. We identify a major bottleneck:the long-tail distribution of rollout lengths, where a small ... | [
"Zelei Shao",
"Vikranth Srivatsa",
"Sanjana Srivastava",
"Qingyang Wu",
"Alpay Ariyak",
"Xiaoxia Wu",
"Ameen Patel",
"Jue Wang",
"Percy Liang",
"Tri Dao",
"Ce Zhang",
"Yiying Zhang",
"Ben Athiwaratkun",
"Chenfeng Xu",
"Junxiong Wang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-11-17T00:00:00 | https://arxiv.org/abs/2511.13841 | https://arxiv.org/pdf/2511.13841v1 | 2511.13841 | 10.48550/arXiv.2511.13841 | 9 | 0 | false | null | arXiv.org | 0.3735 |
0a5f3ed1432b1b847bb5be16f5ba398f6f9f26621ba4b1591441ee34c4518193 | [
"arxiv",
"semantic_scholar"
] | Cacheback: Speculative Decoding With Nothing But Cache | 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-... | [
"Zhiyao Ma",
"In Gim",
"Lin Zhong"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-15T00:00:00 | https://arxiv.org/abs/2511.21699 | https://arxiv.org/pdf/2511.21699v1 | 2511.21699 | 10.18653/v1/2025.emnlp-main.1581 | 3 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.3713 |
5c970964f67e211782934047ac1149eaec7e55165aee8215dcf3bbb1c8ff7eae | [
"arxiv",
"semantic_scholar"
] | Striking the Right Balance between Compute and Copy: Improving LLM Inferencing Under Speculative Decoding | With the skyrocketing costs of GPUs and their virtual instances in the cloud, there is a significant desire to use CPUs for large language model (LLM) inference. KV cache update, often implemented as allocation, copying, and in-place strided update for each generated token, incurs significant overhead. As the sequence ... | [
"Arun Ramachandran",
"Ramaswamy Govindarajan",
"Murali Annavaram",
"Prakash Raghavendra",
"Hossein Entezari Zarch",
"Lei Gao",
"Chaoyi Jiang"
] | [
"cs.DC",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-15T00:00:00 | https://arxiv.org/abs/2511.12031 | https://arxiv.org/pdf/2511.12031v1 | 2511.12031 | 10.48550/arXiv.2511.12031 | 0 | 0 | false | null | arXiv.org | 0.3713 |
f380d052435b23fff7004248d2e7f09ba440c028ec4121c72a7c957cdaa69fd2 | [
"arxiv",
"semantic_scholar"
] | Speculative Decoding in Decentralized LLM Inference: Turning Communication Latency into Computation Throughput | Speculative decoding accelerates large language model (LLM) inference by using a lightweight draft model to propose tokens that are later verified by a stronger target model. While effective in centralized systems, its behavior in decentralized settings, where network latency often dominates compute, remains under-char... | [
"Jingwei Song",
"Wanyi Chen",
"Xinyuan Song",
" Max",
"Chris Tong",
"Gufeng Chen",
"Tianyi Zhao",
"Eric Yang",
"Bill Shi",
"Lynn Ai"
] | [
"cs.DC",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-13T00:00:00 | https://arxiv.org/abs/2511.11733 | https://arxiv.org/pdf/2511.11733v1 | 2511.11733 | 10.48550/arXiv.2511.11733 | 2 | 1 | false | null | arXiv.org | 0.369 |
876b9d510c25384844b4e76cba4cda89cca09c751648aaefd4de6c661b391d10 | [
"arxiv",
"semantic_scholar"
] | Steering Pretrained Drafters during Speculative Decoding | Speculative decoding accelerates language model inference by separating generation into fast drafting and parallel verification. Its main limitation is drafter-verifier misalignment, which limits token acceptance and reduces overall effectiveness. While small drafting heads trained from scratch compensate with speed, t... | [
"Frédéric Berdoz",
"Peer Rheinboldt",
"Roger Wattenhofer"
] | [
"cs.LG",
"cs.PF"
] | [
"Computer Science"
] | 2025-11-13T00:00:00 | https://arxiv.org/abs/2511.09844 | https://arxiv.org/pdf/2511.09844v1 | 2511.09844 | 10.48550/arXiv.2511.09844 | 0 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.369 |
c253e46bf368d665c679cb9f5cee11730d06b8b6bdfdd8a9f7580f5523520550 | [
"arxiv",
"semantic_scholar"
] | Principled Coarse-Grained Acceptance for Speculative Decoding in Speech | Speculative decoding accelerates autoregressive speech generation by letting a fast draft model propose tokens that a larger target model verifies. However, for speech LLMs that generate acoustic tokens, exact token matching is overly restrictive: many discrete tokens are acoustically or semantically interchangeable, r... | [
"Moran Yanuka",
"Paul Dixon",
"Eyal Finkelshtein",
"Daniel Rotman",
"Raja Giryes"
] | [
"eess.AS",
"cs.LG"
] | [
"Computer Science",
"Engineering"
] | 2025-11-05T00:00:00 | https://arxiv.org/abs/2511.13732 | https://arxiv.org/pdf/2511.13732v4 | 2511.13732 | 10.48550/arXiv.2511.13732 | 2 | 0 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.3598 |
c3f745d68b303decafed4e49057542b350d1f5eb3477a1d7f8fa0e42d08911cf | [
"arxiv",
"semantic_scholar"
] | Collaborative Large Language Model Inference via Resource-Aware Parallel Speculative Decoding | The growing demand for on-device large language model (LLM) inference highlights the need for efficient mobile edge computing (MEC) solutions, especially in resource-constrained settings. Speculative decoding offers a promising solution by partitioning token generation between a lightweight draft model on mobile device... | [
"Jungyeon Koh",
"Hyun Jong Yang"
] | [
"cs.LG",
"eess.SP"
] | [
"Computer Science",
"Engineering"
] | 2025-11-03T00:00:00 | https://arxiv.org/abs/2511.01695 | https://arxiv.org/pdf/2511.01695v4 | 2511.01695 | 10.48550/arXiv.2511.01695 | 0 | 0 | false | null | arXiv.org | 0.3575 |
82e7aad187c8d1079c665a2cf8dc154252b87c5d07f3d01b64cb9697c3dc5aa4 | [
"arxiv",
"semantic_scholar"
] | TapOut: A Bandit-Based Approach to Dynamic Speculative Decoding | Speculative decoding accelerates LLMs by using a lightweight draft model to generate tokens autoregressively before verifying them in parallel with a larger target model. However, determining the optimal number of tokens to draft remains a key challenge limiting the approach's effectiveness. Dynamic speculative decodin... | [
"Aditya Sridhar",
"Nish Sinnadurai",
"Sean Lie",
"Vithursan Thangarasa"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2025-11-03T00:00:00 | https://arxiv.org/abs/2511.02017 | https://arxiv.org/pdf/2511.02017v1 | 2511.02017 | 10.48550/arXiv.2511.02017 | 1 | 0 | false | null | arXiv.org | 0.3575 |
fb1eb6085b163a354ea9cd43f252586e3dcac20d4d22b4f74bfd3776ae10f2aa | [
"arxiv",
"semantic_scholar"
] | When, What, and How: Rethinking Retrieval-Enhanced Speculative Decoding | Speculative decoding (SD) has emerged as an effective technique to accelerate large language model (LLM) inference without compromising output quality. However, the achievable speedup largely depends on the effectiveness of the drafting model. While model-based methods like EAGLE-2 are accurate but costly, retrieval-en... | [
"Min Fang",
"Zhihui Fu",
"Qibin Zhao",
"Jun Wang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-03T00:00:00 | https://arxiv.org/abs/2511.01282 | https://arxiv.org/pdf/2511.01282v1 | 2511.01282 | 10.48550/arXiv.2511.01282 | 1 | 0 | false | null | arXiv.org | 0.3575 |
8cb9b7be3c81f03ae0f0a4ca5244c7510af10ca872daa5ddfa281a43a50b1d79 | [
"arxiv",
"semantic_scholar"
] | SpecDiff-2: Scaling Diffusion Drafter Alignment For Faster Speculative Decoding | Speculative decoding has become the standard approach for accelerating Large Language Model (LLM) inference. It exploits a lossless draft-then-verify procedure to circumvent the latency of autoregressive decoding, achieving impressive speed-ups. Yet, current speculative decoding approaches remain limited by two fundame... | [
"Jameson Sandler",
"Jacob K. Christopher",
"Thomas Hartvigsen",
"Ferdinando Fioretto"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-11-01T00:00:00 | https://arxiv.org/abs/2511.00606 | https://arxiv.org/pdf/2511.00606v2 | 2511.00606 | 10.48550/arXiv.2511.00606 | 10 | 0 | false | null | arXiv.org | 0.3552 |
5fa7d9ee24b82123aeeb6c8d91cc117789bdd3d684949b7f811080298a5c308a | [
"arxiv",
"semantic_scholar"
] | Reject Only Critical Tokens: Pivot-Aware Speculative Decoding | Speculative Decoding (SD) ensures that the output matches the target model's distribution exactly. However, we argue that this distribution matching requirement is too stringent and results in unnecessarily low acceptance rates, limiting potential speedups. Instead, we advocate a reformulation of the decoding objective... | [
"Amir Ziashahabi",
"Yavuz Faruk Bakman",
"Duygu Nur Yaldiz",
"Mostafa El-Khamy",
"Sai Praneeth Karimireddy",
"Salman Avestimehr"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2025-11-01T00:00:00 | https://arxiv.org/abs/2511.00351 | https://arxiv.org/pdf/2511.00351v1 | 2511.00351 | 10.48550/arXiv.2511.00351 | 2 | 0 | true | https://github.com/amir-zsh/PAD | arXiv.org | 0.549 |
8416dc812eaed8f798cbf438e74eadcf685191de4939aa6b78968bd7904f199e | [
"arxiv",
"semantic_scholar"
] | SpecAttn: Speculating Sparse Attention | Large Language Models (LLMs) face significant computational bottlenecks during inference due to the quadratic complexity of self-attention mechanisms, particularly as context lengths increase. We introduce SpecAttn, a novel training-free approach that seamlessly integrates with existing speculative decoding techniques ... | [
"Harsh Shah"
] | [
"cs.CL",
"cs.LG",
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2025-10-31T00:00:00 | https://arxiv.org/abs/2510.27641 | https://arxiv.org/pdf/2510.27641v1 | 2510.27641 | 10.48550/arXiv.2510.27641 | 0 | 0 | false | null | arXiv.org | 0.3541 |
2e174777c59543ca0d26699225763832092fc478ef07f85e86ed6def7ac342ba | [
"arxiv",
"semantic_scholar"
] | CAS-Spec: Cascade Adaptive Self-Speculative Decoding for On-the-Fly Lossless Inference Acceleration of LLMs | Speculative decoding has become a widely adopted as an effective technique for lossless inference acceleration when deploying large language models (LLMs). While on-the-fly self-speculative methods offer seamless integration and broad utility, they often fall short of the speed gains achieved by methods relying on spec... | [
"Zhiyuan Ning",
"Jiawei Shao",
"Ruge Xu",
"Xinfei Guo",
"Jun Zhang",
"Chi Zhang",
"Xuelong Li"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-30T00:00:00 | https://arxiv.org/abs/2510.26843 | https://arxiv.org/pdf/2510.26843v1 | 2510.26843 | 10.48550/arXiv.2510.26843 | 0 | 0 | false | null | arXiv.org | 0.3529 |
a07d6ed6c705aa74327ac24e68fc95aaeab3d5422fecd79dbc0663cd379de51a | [
"arxiv",
"semantic_scholar"
] | Polybasic Speculative Decoding Through a Theoretical Perspective | Inference latency stands as a critical bottleneck in the large-scale deployment of Large Language Models (LLMs). Speculative decoding methods have recently shown promise in accelerating inference without compromising the output distribution. However, existing work typically relies on a dualistic draft-verify framework ... | [
"Ruilin Wang",
"Huixia Li",
"Yuexiao Ma",
"Xiawu Zheng",
"Fei Chao",
"Xuefeng Xiao",
"Rongrong Ji"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-10-30T00:00:00 | https://arxiv.org/abs/2510.26527 | https://arxiv.org/pdf/2510.26527v1 | 2510.26527 | 10.48550/arXiv.2510.26527 | 0 | 0 | false | null | International Conference on Machine Learning | 0.3529 |
e74f829e608057929e2acaa671b6e92e04fb164c8bc6d45cfb82f7ff9ab0ab79 | [
"arxiv",
"semantic_scholar"
] | ReSpec: Towards Optimizing Speculative Decoding in Reinforcement Learning Systems | Adapting large language models (LLMs) via reinforcement learning (RL) is often bottlenecked by the generation stage, which can consume over 75\% of the training time. Speculative decoding (SD) accelerates autoregressive generation in serving systems, but its behavior under RL training remains largely unexplored. We ide... | [
"Qiaoling Chen",
"Zijun Liu",
"Peng Sun",
"Shenggui Li",
"Guoteng Wang",
"Ziming Liu",
"Yonggang Wen",
"Siyuan Feng",
"Tianwei Zhang"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2025-10-30T00:00:00 | https://arxiv.org/abs/2510.26475 | https://arxiv.org/pdf/2510.26475v1 | 2510.26475 | 10.48550/arXiv.2510.26475 | 12 | 2 | false | null | arXiv.org | 0.3529 |
6c5571a9a8c1a70f1d4d0a1a6b41025a1d1f8b78ac0ef4ef36ec34854191e76d | [
"arxiv",
"semantic_scholar"
] | Speculative Coupled Decoding for Training-Free Lossless Acceleration of Autoregressive Visual Generation | Autoregressive (AR) modeling has recently emerged as a promising new paradigm in visual generation, but its practical adoption is severely constrained by the slow inference speed of per-token generation, which often requires thousands of steps to produce a single sample. While several Speculative Decoding (SD)-based me... | [
"Junhyuk So",
"Hyunho Kook",
"Chaeyeon Jang",
"Eunhyeok Park"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-10-28T00:00:00 | https://arxiv.org/abs/2510.24211 | https://arxiv.org/pdf/2510.24211v2 | 2510.24211 | null | 2 | 0 | true | https://github.com/junhyukso/SCD | null | 0.4144 |
3e1f76c61d3fd3eb0e72cb811966a9cc8143dcfe426fef547478ada559e87e39 | [
"arxiv",
"semantic_scholar"
] | Batch Speculative Decoding Done Right | Speculative decoding must produce outputs distribution identical to standard autoregressive generation-this output equivalence is not an optimization target but the defining criterion of valid speculative decoding. We demonstrate that all existing batch speculative decoding implementations violate this fundamental requ... | [
"Ranran Haoran Zhang",
"Soumik Dey",
"Ashirbad Mishra",
"Hansi Wu",
"Binbin Li",
"Rui Zhang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-26T00:00:00 | https://arxiv.org/abs/2510.22876 | https://arxiv.org/pdf/2510.22876v3 | 2510.22876 | 10.48550/arXiv.2510.22876 | 1 | 0 | true | https://github.com/eBay/spec_dec | arXiv.org | 0.5383 |
424cf2d40c53b479a9b54901307ef04f87102ad6f915e42b195b5e96f5c53a9e | [
"arxiv",
"semantic_scholar"
] | FastVLM: Self-Speculative Decoding for Fast Vision-Language Model Inference | Vision-language Models (VLMs) have made significant strides in visual understanding and query response generation, but often face challenges of high computational cost and inference latency due to autoregressive decoding. In this work, we introduce an imitation-learning-based Self-Speculative Decoding (SSD) framework, ... | [
"Divya Jyoti Bajpai",
"Manjesh Kumar Hanawal"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-26T00:00:00 | https://arxiv.org/abs/2510.22641 | https://arxiv.org/pdf/2510.22641v1 | 2510.22641 | 10.48550/arXiv.2510.22641 | 2 | 1 | false | null | null | 0.2217 |
9f10a6bfd30a7547fe28f913e5dbd921840f2eb08c48a7e6612bd15e92e9dc9b | [
"arxiv",
"semantic_scholar"
] | Language Ranker: A Lightweight Ranking framework for LLM Decoding | Conventional research on large language models (LLMs) has primarily focused on refining output distributions, while paying less attention to the decoding process that transforms these distributions into final responses. Recent advances, such as scaling the computation of inference time with reward models, have undersco... | [
"Chenheng Zhang",
"Tianqi Du",
"Jizhe Zhang",
"Mingqing Xiao",
"Yifei Wang",
"Yisen Wang",
"Zhouchen Lin"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-23T00:00:00 | https://arxiv.org/abs/2510.21883 | https://arxiv.org/pdf/2510.21883v1 | 2510.21883 | 10.48550/arXiv.2510.21883 | 1 | 0 | false | null | arXiv.org | 0.3449 |
3fa3ac9526ecf22e5b087fbac6759bdf31765418cf52dd9ce391e9af20dd8678 | [
"arxiv",
"semantic_scholar"
] | Not-a-Bandit: Provably No-Regret Drafter Selection in Speculative Decoding for LLMs | Speculative decoding is widely used in accelerating large language model (LLM) inference. In this work, we focus on the online draft model selection problem in speculative decoding. We design an algorithm that provably competes with the best draft model in hindsight for each query in terms of either the token acceptanc... | [
"Hongyi Liu",
"Jiaji Huang",
"Zhen Jia",
"Youngsuk Park",
"Yu-Xiang Wang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-10-22T00:00:00 | https://arxiv.org/abs/2510.20064 | https://arxiv.org/pdf/2510.20064v2 | 2510.20064 | 10.48550/arXiv.2510.20064 | 3 | 2 | true | null | arXiv.org | 0.5312 |
468aba3d5dbe2f421933777ca7432eb3797d420859add9220c4b914c583549fc | [
"arxiv",
"semantic_scholar"
] | Fast Inference via Hierarchical Speculative Decoding | Transformer language models generate text autoregressively, making inference latency proportional to the number of tokens generated. Speculative decoding reduces this latency without sacrificing output quality, by leveraging a small draft model to propose tokens that the larger target model verifies in parallel. In pra... | [
"Clara Mohri",
"Haim Kaplan",
"Tal Schuster",
"Yishay Mansour",
"Amir Globerson"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-10-22T00:00:00 | https://arxiv.org/abs/2510.19705 | https://arxiv.org/pdf/2510.19705v2 | 2510.19705 | 10.48550/arXiv.2510.19705 | 1 | 0 | false | null | arXiv.org | 0.3438 |
568a9bc978c86633380376765bd736d91ae693e0ec7d9fb4cc3f9df7ef325065 | [
"arxiv",
"semantic_scholar"
] | AdaSPEC: Selective Knowledge Distillation for Efficient Speculative Decoders | Speculative Decoding (SD) accelerates large language model inference by employing a small draft model to generate predictions, which are then verified by a larger target model. The effectiveness of SD hinges on the alignment between these models, which is typically enhanced by Knowledge Distillation (KD). However, conv... | [
"Yuezhou Hu",
"Jiaxin Guo",
"Xinyu Feng",
"Tuo Zhao"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-10-22T00:00:00 | https://arxiv.org/abs/2510.19779 | https://arxiv.org/pdf/2510.19779v1 | 2510.19779 | 10.48550/arXiv.2510.19779 | 7 | 0 | true | https://github.com/yuezhouhu/adaspec | arXiv.org | 0.5312 |
eccaa7e525efb5095c757c9e5121e1353b0ab0a04221e0e590a34958742a4267 | [
"arxiv",
"semantic_scholar"
] | From Quarter to All: Accelerating Speculative LLM Decoding via Floating-Point Exponent Remapping and Parameter Sharing | Large language models achieve impressive performance across diverse tasks but exhibit high inference latency due to their large parameter sizes. While quantization reduces model size, it often leads to performance degradation compared to the full model. Speculative decoding remains lossless but typically incurs extra o... | [
"Yushu Zhao",
"Yubin Qin",
"Yang Wang",
"Xiaolong Yang",
"Huiming Han",
"Shaojun Wei",
"Yang Hu",
"Shouyi Yin"
] | [
"cs.AR"
] | [
"Computer Science"
] | 2025-10-21T00:00:00 | https://arxiv.org/abs/2510.18525 | https://arxiv.org/pdf/2510.18525v1 | 2510.18525 | 10.48550/arXiv.2510.18525 | 1 | 0 | false | null | arXiv.org | 0.3426 |
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