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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
07ba491326287450a2b5b2a096cc34c9cd80454793b44913b10dd0fc4e6c8b9b | [
"arxiv",
"semantic_scholar"
] | TokenTiming: A Dynamic Alignment Method for Universal Speculative Decoding Model Pairs | Accelerating the inference of large language models (LLMs) has been a critical challenge in generative AI. Speculative decoding (SD) substantially improves LLM inference efficiency. However, its utility is limited by a fundamental constraint: the draft and target models must share the same vocabulary, thus limiting the... | [
"Sibo Xiao",
"Jinyuan Fu",
"Zhongle Xie",
"Lidan Shou"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-17T00:00:00 | https://arxiv.org/abs/2510.15545 | https://arxiv.org/pdf/2510.15545v4 | 2510.15545 | 10.48550/arXiv.2510.15545 | 0 | 0 | false | null | arXiv.org | 0.338 |
298e73b7622084d7adc69fef8929deb05ecbcba4f87219495486c72d0b961898 | [
"arxiv",
"semantic_scholar"
] | Accelerating Mobile Language Model via Speculative Decoding and NPU-Coordinated Execution | Performing Retrieval-Augmented Generation (RAG) directly on mobile devices is promising for data privacy and responsiveness but is hindered by the architectural constraints of mobile NPUs. Specifically, current hardware struggles with the variable workloads intrinsic to RAG: the transition between processing extensive ... | [
"Zhiyang Chen",
"Daliang Xu",
"Haiyang Shen",
"Chiheng Lou",
"Mengwei Xu",
"Shangguang Wang",
"Xin Jin",
"Yun Ma"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-10-17T00:00:00 | https://arxiv.org/abs/2510.15312 | https://arxiv.org/pdf/2510.15312v4 | 2510.15312 | 10.48550/arXiv.2510.15312 | 1 | 0 | false | null | arXiv.org | 0.338 |
3eb2c2605a527ff6d7d0ed87ce7cb34d50ab97004508c8102709f00873bf9cbb | [
"arxiv",
"semantic_scholar"
] | Mirror Speculative Decoding: Breaking the Serial Barrier in LLM Inference | Speculative decoding accelerates LLM inference by using a draft model to look ahead, but gains are capped by the cost of autoregressive draft generation: increasing draft size elevates acceptance rates but introduces additional latency overhead exacerbating the speed-accuracy tradeoff. Prior methods (Medusa, Hydra, EAG... | [
"Nikhil Bhendawade",
"Kumari Nishu",
"Arnav Kundu",
"Chris Bartels",
"Minsik Cho",
"Irina Belousova"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-10-15T00:00:00 | https://arxiv.org/abs/2510.13161 | https://arxiv.org/pdf/2510.13161v2 | 2510.13161 | 10.48550/arXiv.2510.13161 | 1 | 0 | false | null | arXiv.org | 0.3357 |
3612acd84e7b6b937ac87cfba9ca43fd383814c99bbb4b680cc37da8a185387d | [
"arxiv",
"semantic_scholar"
] | STAR: Decode-Phase Rescheduling for LLM Inference | Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalance in the decode phase, particularly for long-output reasoning tasks. Existing systems, such as PD disaggregation architectures, rely on static prefill-to-decode scheduling, whic... | [
"Zhibin Wang",
"Zetao Hong",
"Xue Li",
"Zibo Wang",
"Shipeng Li",
"Qingkai Meng",
"Qing Wang",
"Chengying Huan",
"Rong Gu",
"Sheng Zhong",
"Chen Tian"
] | [
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2025-10-15T00:00:00 | https://arxiv.org/abs/2510.13668 | https://arxiv.org/pdf/2510.13668v2 | 2510.13668 | 10.1145/3806645.3807813 | 1 | 0 | false | null | null | 0.2136 |
677bbdf95edb9fb3fc816aad5a69d531d06ba2c3e79d82b452f45b4115b93fb9 | [
"arxiv",
"semantic_scholar"
] | 3-Model Speculative Decoding | Speculative Decoding (SD) accelerates inference in large language models by using a smaller draft model to propose tokens, which are then verified by a larger target model. However, the throughput gains of SD are fundamentally limited by a trade-off between draft model size and token acceptance: smaller draft models ge... | [
"Sanghyun Byun",
"Mohanad Odema",
"Jung Ick Guack",
"Baisub Lee",
"Jacob Song",
"Woo Seong Chung"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-10-14T00:00:00 | https://arxiv.org/abs/2510.12966 | https://arxiv.org/pdf/2510.12966v1 | 2510.12966 | 10.48550/arXiv.2510.12966 | 0 | 0 | false | null | arXiv.org | 0.3346 |
03d69efd712c80783e10e157380ccc82ce2725ec8d2c05a5391551d536a31933 | [
"arxiv",
"semantic_scholar"
] | Efficient LLM Inference over Heterogeneous Edge Networks with Speculative Decoding | Large language model (LLM) inference at the network edge is a promising serving paradigm that leverages distributed edge resources to run inference near users and enhance privacy. Existing edge-based LLM inference systems typically adopt autoregressive decoding (AD), which only generates one token per forward pass. Thi... | [
"Bingjie Zhu",
"Zhixiong Chen",
"Liqiang Zhao",
"Hyundong Shin",
"Arumugam Nallanathan"
] | [
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2025-10-13T00:00:00 | https://arxiv.org/abs/2510.11331 | https://arxiv.org/pdf/2510.11331v1 | 2510.11331 | 10.48550/arXiv.2510.11331 | 6 | 0 | false | null | arXiv.org | 0.3334 |
a0952e40b1e21fa0f8ac6fbd30cf6e1b0f777b2b584f590ee412447baffdf733 | [
"arxiv",
"semantic_scholar"
] | Conformal Sparsification for Bandwidth-Efficient Edge-Cloud Speculative Decoding | Edge-cloud speculative decoding (SD) accelerates inference by having a cloud-based large language model (LLM) that verifies draft tokens generated by a resource-constrained small language model (SLM) at the edge. A central bottleneck is the limited bandwidth of the edge-cloud link, which necessitates efficient compress... | [
"Payel Bhattacharjee",
"Fengwei Tian",
"Meiyu Zhong",
"Guangyi Zhang",
"Osvaldo Simeone",
"Ravi Tandon"
] | [
"cs.LG",
"cs.AI",
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2025-10-11T00:00:00 | https://arxiv.org/abs/2510.09942 | https://arxiv.org/pdf/2510.09942v1 | 2510.09942 | 10.48550/arXiv.2510.09942 | 2 | 0 | false | null | arXiv.org | 0.3311 |
2f9527dcc8cb49e9c70e043c19e102b010890bb1d59e93d8122eaa4d8c8eca76 | [
"arxiv",
"semantic_scholar"
] | SP-MoE: Speculative Decoding and Prefetching for Accelerating MoE-based Model Inference | The Mixture-of-Experts (MoE) architecture has been widely adopted in large language models (LLMs) to reduce computation cost through model sparsity. Employing speculative decoding (SD) can further accelerate MoE inference by drafting multiple tokens per step and verifying them in parallel. However, combining MoE with S... | [
"Liangkun Chen",
"Zijian Wen",
"Tian Wu",
"Xiaoxi Zhang",
"Chuan Wu"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2025-10-11T00:00:00 | https://arxiv.org/abs/2510.10302 | https://arxiv.org/pdf/2510.10302v2 | 2510.10302 | 10.48550/arXiv.2510.10302 | 3 | 0 | false | null | arXiv.org | 0.3311 |
cce4ea6eecbe8e5b871c8896f310d805fba3867618365d3436a8e3f8d0df76ca | [
"arxiv",
"semantic_scholar"
] | Speculative Jacobi-Denoising Decoding for Accelerating Autoregressive Text-to-image Generation | As a new paradigm of visual content generation, autoregressive text-to-image models suffer from slow inference due to their sequential token-by-token decoding process, often requiring thousands of model forward passes to generate a single image. To address this inefficiency, we propose Speculative Jacobi-Denoising Deco... | [
"Yao Teng",
"Fuyun Wang",
"Xian Liu",
"Zhekai Chen",
"Han Shi",
"Yu Wang",
"Zhenguo Li",
"Weiyang Liu",
"Difan Zou",
"Xihui Liu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-10-10T00:00:00 | https://arxiv.org/abs/2510.08994 | https://arxiv.org/pdf/2510.08994v1 | 2510.08994 | 10.48550/arXiv.2510.08994 | 4 | 1 | false | null | arXiv.org | 0.33 |
8d87ee13b01f1e8580143616019a08c09c2bfc308ef3bce5f74003b398aa8186 | [
"arxiv",
"semantic_scholar"
] | SPAD: Specialized Prefill and Decode Hardware for Disaggregated LLM Inference | Large Language Models (LLMs) have gained popularity in recent years, driving up the demand for inference. LLM inference is composed of two phases with distinct characteristics: a compute-bound prefill phase followed by a memory-bound decode phase. To efficiently serve LLMs, prior work proposes prefill-decode disaggrega... | [
"Hengrui Zhang",
"Pratyush Patel",
"August Ning",
"David Wentzlaff"
] | [
"cs.AR",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2025-10-09T00:00:00 | https://arxiv.org/abs/2510.08544 | https://arxiv.org/pdf/2510.08544v1 | 2510.08544 | 10.48550/arXiv.2510.08544 | 5 | 0 | false | null | arXiv.org | 0.3289 |
7b5859c00f7474158aece9cbeaa91dd3e811b1ed862e9f5029de4480cd32954e | [
"arxiv",
"semantic_scholar"
] | OWL: Overcoming Window Length-Dependence in Speculative Decoding for Long-Context Inputs | Speculative decoding promises faster inference for large language models (LLMs), yet existing methods fail to generalize to real-world settings. Benchmarks typically assume short contexts (e.g., 2K tokens), whereas practical workloads involve long contexts. We find current approaches degrade severely with long contexts... | [
"Jaeseong Lee",
"seung-won hwang",
"Aurick Qiao",
"Gabriele Oliaro",
"Ye Wang",
"Samyam Rajbhandari"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-08T00:00:00 | https://arxiv.org/abs/2510.07535 | https://arxiv.org/pdf/2510.07535v1 | 2510.07535 | 10.48550/arXiv.2510.07535 | 0 | 0 | false | null | arXiv.org | 0.3277 |
346cd617ccf424b28e1e4eab0cb897f62ecd7b2ffc7deaa9c316d6e58cd43eab | [
"arxiv",
"semantic_scholar"
] | Draft, Verify, and Improve: Toward Training-Aware Speculative Decoding | Autoregressive (AR) decoding is a major latency bottleneck for large language models. Speculative decoding (SD) accelerates AR by letting a drafter propose multi-token blocks that a verifier accepts or rejects. However, many SD systems require heavy offline training or extra components. These choices raise data/compute... | [
"Shrenik Bhansali",
"Larry Heck"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-10-06T00:00:00 | https://arxiv.org/abs/2510.05421 | https://arxiv.org/pdf/2510.05421v1 | 2510.05421 | 10.48550/arXiv.2510.05421 | 1 | 0 | false | null | arXiv.org | 0.3254 |
c984a01a98ad2856f3e307a10fb15d4261bcc017d099d8052df0df7a8fbecb20 | [
"arxiv",
"semantic_scholar"
] | Self Speculative Decoding for Diffusion Large Language Models | Diffusion-based Large Language Models (dLLMs) have emerged as a competitive alternative to autoregressive models, offering unique advantages through bidirectional attention and parallel generation paradigms. However, the generation results of current parallel decoding methods deviate from stepwise decoding, introducing... | [
"Yifeng Gao",
"Ziang Ji",
"Yuxuan Wang",
"Biqing Qi",
"Hanlin Xu",
"Linfeng Zhang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-10-05T00:00:00 | https://arxiv.org/abs/2510.04147 | https://arxiv.org/pdf/2510.04147v1 | 2510.04147 | 10.48550/arXiv.2510.04147 | 25 | 5 | true | null | arXiv.org | 0.5011 |
de9adfe6916a2b78d1d444f1e3a0bf5f0401e3ea6ebe9b23dbfb7ff81dd9e499 | [
"arxiv",
"semantic_scholar"
] | The Disparate Impacts of Speculative Decoding | The practice of speculative decoding, whereby inference is probabilistically supported by a smaller, cheaper, ``drafter'' model, has become a standard technique for systematically reducing the decoding time of large language models. This paper conducts an analysis of speculative decoding through the lens of its potenti... | [
"Jameson Sandler",
"Ahmet Üstün",
"Marco Romanelli",
"Sara Hooker",
"Ferdinando Fioretto"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-02T00:00:00 | https://arxiv.org/abs/2510.02128 | https://arxiv.org/pdf/2510.02128v1 | 2510.02128 | 10.48550/arXiv.2510.02128 | 2 | 0 | false | null | arXiv.org | 0.3208 |
6efead990ebdce075638f64911d7b96e7a2d66789cb4e07586b47875b8310f56 | [
"arxiv",
"semantic_scholar"
] | HiSpec: Hierarchical Speculative Decoding for LLMs | Speculative decoding accelerates LLM inference by using a smaller draft model to speculate tokens that a larger target model verifies. Verification is often the bottleneck (e.g. verification is $4\times$ slower than token generation when a 3B model speculates for a 70B target model), but most prior works focus only on ... | [
"Avinash Kumar",
"Sujay Sanghavi",
"Poulami Das"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-10-01T00:00:00 | https://arxiv.org/abs/2510.01336 | https://arxiv.org/pdf/2510.01336v2 | 2510.01336 | 10.48550/arXiv.2510.01336 | 1 | 0 | false | null | arXiv.org | 0.3197 |
6324420d09a0a05ef0eb7a32dc54aa143c05a99733a2f0a8fa9211292c0ab04e | [
"arxiv",
"semantic_scholar"
] | Speculative Verification: Exploiting Information Gain to Refine Speculative Decoding | LLMs have low GPU efficiency and high latency due to autoregressive decoding. Speculative decoding (SD) mitigates this using a small draft model to speculatively generate multiple tokens, which are then verified in parallel by a target model. However, when speculation accuracy is low, the overhead from rejected tokens ... | [
"Sungkyun Kim",
"Jaemin Kim",
"Dogyung Yoon",
"Jiho Shin",
"Junyeol Lee",
"Jiwon Seo"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-09-29T00:00:00 | https://arxiv.org/abs/2509.24328 | https://arxiv.org/pdf/2509.24328v2 | 2509.24328 | 10.48550/arXiv.2509.24328 | 1 | 0 | false | null | arXiv.org | 0.3174 |
88eebe9d27ee6615bf039d2474fe8fe49463ebac3a4fc2cc3374f8217b3a18f4 | [
"arxiv",
"semantic_scholar"
] | HiViS: Hiding Visual Tokens from the Drafter for Speculative Decoding in Vision-Language Models | Speculative decoding has proven effective for accelerating inference in Large Language Models (LLMs), yet its extension to Vision-Language Models (VLMs) remains limited by the computational burden and semantic inconsistency introduced by visual tokens. Recent studies reveal that visual tokens in large VLMs are highly r... | [
"Zhinan Xie",
"Peisong Wang",
"Shuang Qiu",
"Jian Cheng"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-28T00:00:00 | https://arxiv.org/abs/2509.23928 | https://arxiv.org/pdf/2509.23928v2 | 2509.23928 | 10.48550/arXiv.2509.23928 | 1 | 0 | false | null | arXiv.org | 0.3162 |
bb334f7fd49d3b2f29971cd30b20b09e1cf88cc54f08bdf3490153c93a60e261 | [
"arxiv",
"semantic_scholar"
] | DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding | As large language models (LLMs) scale up, accuracy improves, but the autoregressive (AR) nature of decoding increases latency since each token requires a serial forward pass. Speculative decoding addresses this by employing a fast drafter to propose multi-token drafts, which are then verified in parallel by the target ... | [
"Guanghao Li",
"Zhihui Fu",
"Min Fang",
"Qibin Zhao",
"Ming Tang",
"Chun Yuan",
"Jun Wang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-28T00:00:00 | https://arxiv.org/abs/2510.02358 | https://arxiv.org/pdf/2510.02358v1 | 2510.02358 | 10.48550/arXiv.2510.02358 | 21 | 0 | false | null | arXiv.org | 0.3356 |
3f8daa231589327dd1ca1e40074b63c58eb37c1d9102560caa85c1a6b4310e1c | [
"arxiv",
"semantic_scholar"
] | SelfJudge: Faster Speculative Decoding via Self-Supervised Judge Verification | Speculative decoding accelerates LLM inference by verifying candidate tokens from a draft model against a larger target model. Recent judge decoding boosts this process by relaxing verification criteria by accepting draft tokens that may exhibit minor discrepancies from target model output, but existing methods are res... | [
"Kanghoon Yoon",
"Minsub Kim",
"Sungjae Lee",
"Joonhyung Lee",
"Sunghyeon Woo",
"Yeonjun In",
"Se Jung Kwon",
"Chanyoung Park",
"Dongsoo Lee"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-26T00:00:00 | https://arxiv.org/abs/2510.02329 | https://arxiv.org/pdf/2510.02329v2 | 2510.02329 | 10.48550/arXiv.2510.02329 | 4 | 0 | false | null | arXiv.org | 0.314 |
179a0e42cc9b324bef67d7efee9db40d2a6f7f241d7d97a3730063a1142cb1de | [
"arxiv",
"semantic_scholar"
] | Self-Speculative Biased Decoding for Faster Re-Translation | Large language models achieve strong machine translation quality but incur high inference cost and latency, posing challenges for simultaneous translation. Re-translation provides a practical solution for off-the-shelf LLMs by repeatedly regenerating the target output as the source input grows, but it suffers from subs... | [
"Linxiao Zeng",
"Haoyun Deng",
"Kangyuan Shu",
"Shizhen Wang"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-09-26T00:00:00 | https://arxiv.org/abs/2509.21740 | https://arxiv.org/pdf/2509.21740v2 | 2509.21740 | null | 0 | 0 | false | null | null | 0.1998 |
9afae801d6aac95feff6025a3697734664b926c472fb09096026d6a08ddc1eeb | [
"arxiv",
"semantic_scholar"
] | Bridging Draft Policy Misalignment: Group Tree Optimization for Speculative Decoding | Speculative decoding accelerates large language model (LLM) inference by letting a lightweight draft model propose multiple tokens that the target model verifies in parallel. Yet existing training objectives optimize only a single greedy draft path, while decoding follows a tree policy that re-ranks and verifies multip... | [
"Shijing Hu",
"Jingyang Li",
"Zhihui Lu",
"Pan Zhou"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-26T00:00:00 | https://arxiv.org/abs/2509.22134 | https://arxiv.org/pdf/2509.22134v2 | 2509.22134 | 10.48550/arXiv.2509.22134 | 5 | 1 | true | https://github.com/hsj576/GTO | arXiv.org | 0.4852 |
67ab3e7150b2cc3e9d9a8baddf77e7a1fcb7305099a5866674099a9a01d94896 | [
"arxiv",
"semantic_scholar"
] | FastGRPO: Accelerating Policy Optimization via Concurrency-aware Speculative Decoding and Online Draft Learning | Group relative policy optimization (GRPO) has demonstrated significant potential in improving the reasoning capabilities of large language models (LLMs) via reinforcement learning. However, its practical deployment is impeded by an excessively slow training process, primarily attributed to the computationally intensive... | [
"Yizhou Zhang",
"Ning Lv",
"Teng Wang",
"Jisheng Dang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-26T00:00:00 | https://arxiv.org/abs/2509.21792 | https://arxiv.org/pdf/2509.21792v1 | 2509.21792 | 10.48550/arXiv.2509.21792 | 4 | 0 | true | https://github.com/yedaotian9/GRPO_speculative | arXiv.org | 0.4852 |
16a83b33cd2e7a6fb6a32499669a7656b00c00d685df9c4410e31d3b14ae4f32 | [
"arxiv",
"semantic_scholar"
] | SpecMER: Fast Protein Generation with K-mer Guided Speculative Decoding | Autoregressive models have transformed protein engineering by enabling the generation of novel protein sequences beyond those found in nature. However, their sequential inference introduces significant latency, limiting their utility in high-throughput protein screening. Speculative decoding accelerates generation by e... | [
"Thomas Walton",
"Darin Tsui",
"Aryan Musharaf",
"Amirali Aghazadeh"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-09-25T00:00:00 | https://arxiv.org/abs/2509.21689 | https://arxiv.org/pdf/2509.21689v1 | 2509.21689 | 10.48550/arXiv.2509.21689 | 2 | 0 | false | null | arXiv.org | 0.3128 |
215ab202dca4ce7f7f2df32aebc83b69958af1530c743a54610fd951c29ae036 | [
"arxiv",
"semantic_scholar"
] | FastEagle: Cascaded Drafting for Accelerating Speculative Decoding | Speculative decoding accelerates generation by drafting candidates and verifying them in parallel, yet state-of-the-art drafters (e.g., EAGLE) still require N sequential passes to propose N tokens. We present FastEagle, a non-autoregressive cascaded drafter that emits an entire draft in a single forward pass. FastEagle... | [
"Haiduo Huang",
"Jiangcheng Song",
"Wenzhe Zhao",
"Pengju Ren"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-09-24T00:00:00 | https://arxiv.org/abs/2509.20416 | https://arxiv.org/pdf/2509.20416v1 | 2509.20416 | 10.48550/arXiv.2509.20416 | 1 | 0 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.3117 |
ad9c0d380cd671f34f669c66ce4e8491b51ea016c658e69440c0729be00810de | [
"arxiv",
"semantic_scholar"
] | SpecMamba: Accelerating Mamba Inference on FPGA with Speculative Decoding | The growing demand for efficient long-sequence modeling on edge devices has propelled widespread adoption of State Space Models (SSMs) like Mamba, due to their superior computational efficiency and scalability. As its autoregressive generation process remains memory-bound, speculative decoding has been proposed that in... | [
"Linfeng Zhong",
"Songqiang Xu",
"Huifeng Wen",
"Tong Xie",
"Qingyu Guo",
"Yuan Wang",
"Meng Li"
] | [
"cs.AR"
] | [
"Computer Science"
] | 2025-09-24T00:00:00 | https://arxiv.org/abs/2509.19873 | https://arxiv.org/pdf/2509.19873v1 | 2509.19873 | 10.1109/ICCAD66269.2025.11240945 | 2 | 0 | false | null | null | 0.1983 |
793293c4df76a6ccaa97b98fe6b9e18f2de67267134acfe7dccfe7d73c7bada5 | [
"arxiv",
"semantic_scholar"
] | Steering Multimodal Large Language Models Decoding for Context-Aware Safety | Multimodal Large Language Models (MLLMs) are increasingly deployed in real-world applications, yet their ability to make context-aware safety decisions remains limited. Existing methods often fail to balance oversensitivity (unjustified refusals of benign queries) and undersensitivity (missed detection of visually grou... | [
"Zheyuan Liu",
"Zhangchen Xu",
"Guangyao Dou",
"Xiangchi Yuan",
"Zhaoxuan Tan",
"Radha Poovendran",
"Meng Jiang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-23T00:00:00 | https://arxiv.org/abs/2509.19212 | https://arxiv.org/pdf/2509.19212v1 | 2509.19212 | 10.48550/arXiv.2509.19212 | 4 | 0 | false | null | arXiv.org | 0.3105 |
15fe8618c8fc53ca1fe892f6ff252d7ec1b9775da3493ce59408dc27b77ab19b | [
"arxiv",
"semantic_scholar"
] | Structuring The Future: Diffusion LLM Speculative Decoding via Calibrated Draft Graphs | Diffusion LLMs (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs (AR-LLMs) with the potential to operate at significantly higher token-generation rates. To unlock this potential, we present Spiffy, a speculative decoding algorithm to accelerate dLLM inference while provably preserving the m... | [
"Sudhanshu Agrawal",
"Risheek Garrepalli",
"Raghavv Goel",
"Christopher Lott",
"Fatih Porikli",
"Mingu Lee"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-09-22T00:00:00 | https://arxiv.org/abs/2509.18085 | https://arxiv.org/pdf/2509.18085v4 | 2509.18085 | null | 17 | 3 | false | null | null | 0.3138 |
c2fba4d457deaf2bfba4a6d9493c8f177835aa264aa1d0ab71932ca7ab5ea63f | [
"arxiv",
"semantic_scholar"
] | Speculate Deep and Accurate: Lossless and Training-Free Acceleration for Offloaded LLMs via Substitute Speculative Decoding | The immense model sizes of large language models (LLMs) challenge deployment on memory-limited consumer GPUs. Although model compression and parameter offloading are common strategies to address memory limitations, compression can degrade quality, and offloading maintains quality but suffers from slow inference. Specul... | [
"Pei-Shuo Wang",
"Jian-Jia Chen",
"Chun-Che Yang",
"Chi-Chih Chang",
"Ning-Chi Huang",
"Mohamed S. Abdelfattah",
"Kai-Chiang Wu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-09-22T00:00:00 | https://arxiv.org/abs/2509.18344 | https://arxiv.org/pdf/2509.18344v2 | 2509.18344 | 10.48550/arXiv.2509.18344 | 1 | 0 | false | null | arXiv.org | 0.3094 |
365425d46ade5ed71301cc1ba85a2c3fee56538c7158dbb811c009a6308f407c | [
"arxiv",
"semantic_scholar"
] | Pipeline Parallelism is All You Need for Optimized Early-Exit Based Self-Speculative Decoding | Large language models (LLMs) deliver impressive generation quality, but incur very high inference cost because each output token is generated auto-regressively through all model layers. Early-exit based self-speculative decoding (EESD) has emerged to mitigate this cost. However, in practice, many approaches struggle to... | [
"Ruanjun Li",
"Ziheng Liu",
"Yuanming Shi",
"Jiawei Shao",
"Chi Zhang",
"Xuelong Li"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-19T00:00:00 | https://arxiv.org/abs/2509.19368 | https://arxiv.org/pdf/2509.19368v1 | 2509.19368 | 10.48550/arXiv.2509.19368 | 1 | 1 | false | null | arXiv.org | 0.3059 |
2f5c5a74ee7ee207824de4bd86f71705e6030b2a36960756cb9f72dfe50d7115 | [
"arxiv",
"semantic_scholar"
] | ViSpec: Accelerating Vision-Language Models with Vision-Aware Speculative Decoding | Speculative decoding is a widely adopted technique for accelerating inference in large language models (LLMs), yet its application to vision-language models (VLMs) remains underexplored, with existing methods achieving only modest speedups (<1.5x). This gap is increasingly significant as multimodal capabilities become ... | [
"Jialiang Kang",
"Han Shu",
"Wenshuo Li",
"Yingjie Zhai",
"Xinghao Chen"
] | [
"cs.CV",
"cs.CL"
] | [
"Computer Science"
] | 2025-09-17T00:00:00 | https://arxiv.org/abs/2509.15235 | https://arxiv.org/pdf/2509.15235v5 | 2509.15235 | 10.48550/arXiv.2509.15235 | 15 | 4 | true | https://github.com/KangJialiang/ViSpec | arXiv.org | 0.4693 |
861ef963718f14257628c3a92f01282981a171aae5b147d92221f8ef465dde3f | [
"arxiv",
"semantic_scholar"
] | FastMTP: Accelerating LLM Inference with Enhanced Multi-Token Prediction | As large language models (LLMs) become increasingly powerful, the sequential nature of autoregressive generation creates a fundamental throughput bottleneck that limits the practical deployment. While Multi-Token Prediction (MTP) has demonstrated remarkable benefits for model training efficiency and performance, its in... | [
"Yuxuan Cai",
"Xiaozhuan Liang",
"Xinghua Wang",
"Jin Ma",
"Haijin Liang",
"Jinwen Luo",
"Xinyu Zuo",
"Lisheng Duan",
"Yuyang Yin",
"Xi Chen"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-16T00:00:00 | https://arxiv.org/abs/2509.18362 | https://arxiv.org/pdf/2509.18362v1 | 2509.18362 | 10.48550/arXiv.2509.18362 | 9 | 0 | false | null | arXiv.org | 0.3025 |
5637bbb97ebe7f7da09af4390909e01a3877beed82e4a714cbf0fe37066ddc7c | [
"arxiv",
"semantic_scholar"
] | Spec-LLaVA: Accelerating Vision-Language Models with Dynamic Tree-Based Speculative Decoding | Vision-Language Models (VLMs) enable powerful multimodal reasoning but suffer from slow autoregressive inference, limiting their deployment in real-time applications. We introduce Spec-LLaVA, a system that applies speculative decoding to accelerate VLMs without sacrificing output quality. Spec-LLaVA pairs a lightweight... | [
"Mingxiao Huo",
"Jiayi Zhang",
"Hewei Wang",
"Jinfeng Xu",
"Zheyu Chen",
"Huilin Tai",
"Yijun Chen"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-09-15T00:00:00 | https://arxiv.org/abs/2509.11961 | https://arxiv.org/pdf/2509.11961v1 | 2509.11961 | 10.48550/arXiv.2509.11961 | 5 | 1 | false | null | arXiv.org | 0.3014 |
894f7a3f734e1b05aa2fc523968b6e32e79bd6352c39ee554082de048d7992aa | [
"arxiv",
"semantic_scholar"
] | SpecVLM: Fast Speculative Decoding in Vision-Language Models | Speculative decoding is a powerful way to accelerate autoregressive large language models (LLMs), but directly porting it to vision-language models (VLMs) faces unique systems constraints: the prefill stage is dominated by visual tokens whose count scales with image resolution and video length, inflating both compute a... | [
"Haiduo Huang",
"Fuwei Yang",
"Zhenhua Liu",
"Xuanwu Yin",
"Dong Li",
"Pengju Ren",
"Emad Barsoum"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-15T00:00:00 | https://arxiv.org/abs/2509.11815 | https://arxiv.org/pdf/2509.11815v2 | 2509.11815 | 10.48550/arXiv.2509.11815 | 4 | 2 | true | https://github.com/haiduo/SpecVLM | arXiv.org | 0.4657 |
260f3fc8beeb9cd64567ffe55ff4df7e5c33e1573922c3a460a9e3dc0b8fdf18 | [
"arxiv",
"semantic_scholar"
] | Communication-Efficient Collaborative LLM Inference via Distributed Speculative Decoding | Speculative decoding is an emerging technique that accelerates large language model (LLM) inference by allowing a smaller draft model to predict multiple tokens in advance, which are then verified or corrected by a larger target model. In AI-native radio access networks (AI-RAN), this paradigm is well-suited for collab... | [
"Ce Zheng",
"Tingting Yang"
] | [
"eess.SP"
] | [
"Engineering"
] | 2025-09-04T00:00:00 | https://arxiv.org/abs/2509.04576 | https://arxiv.org/pdf/2509.04576v2 | 2509.04576 | 10.1109/WCSP68525.2025.1010651 | 6 | 0 | false | null | International Conference on Wireless Communications and Signal Processing | 0.2888 |
887a9689b0b953df920246b4e40c7c8527c9c53738df7c46de2c0135b29dca2b | [
"arxiv",
"semantic_scholar"
] | Can LLMs Lie? Investigation beyond Hallucination | Large language models (LLMs) have demonstrated impressive capabilities across a variety of tasks, but their increasing autonomy in real-world applications raises concerns about their trustworthiness. While hallucinations-unintentional falsehoods-have been widely studied, the phenomenon of lying, where an LLM knowingly ... | [
"Haoran Huan",
"Mihir Prabhudesai",
"Mengning Wu",
"Shantanu Jaiswal",
"Deepak Pathak"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-09-03T00:00:00 | https://arxiv.org/abs/2509.03518 | https://arxiv.org/pdf/2509.03518v1 | 2509.03518 | 10.48550/arXiv.2509.03518 | 6 | 0 | false | null | arXiv.org | 0.2876 |
bb7f67abf4e114b9bdedb911990b4d6d39f22f8a60ac6048b1d98f7f167376df | [
"arxiv",
"semantic_scholar"
] | DSDE: Dynamic Speculative Decoding with KLD Stability for Real-World Serving | Speculative decoding accelerates large language model inference, but its reliance on a fixed speculation length is suboptimal in large-batch serving environments with diverse requests. This paper explores a new direction for dynamic adaptation by investigating a novel class of post-hoc, diagnostic signals. We propose D... | [
"Mingyu Yang",
"Jae-Young Choi",
"Kihyo Moon",
"Minsung Jang",
"Eunjoo Jeon"
] | [
"cs.DC",
"cs.AI",
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2025-09-01T00:00:00 | https://arxiv.org/abs/2509.01083 | https://arxiv.org/pdf/2509.01083v3 | 2509.01083 | 10.1109/BigData66926.2025.11400767 | 0 | 0 | false | null | BigData Congress [Services Society] | 0.2853 |
0c7bd14df643412aeb3523fe539bdd2c708cd6a197acad42833a7fe4134a4c34 | [
"arxiv",
"semantic_scholar"
] | Scaling Up, Speeding Up: A Benchmark of Speculative Decoding for Efficient LLM Test-Time Scaling | Test-time scaling has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs) by allocating additional computational resources during inference. However, this paradigm is inherently inefficient due to the generation of redundant and repetitive reasoning traces, leading to... | [
"Shengyin Sun",
"Yiming Li",
"Xing Li",
"Yingzhao Lian",
"Weizhe Lin",
"Hui-Ling Zhen",
"Zhiyuan Yang",
"Chen Chen",
"Xianzhi Yu",
"Mingxuan Yuan",
"Chen Ma"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-08-30T00:00:00 | https://arxiv.org/abs/2509.04474 | https://arxiv.org/pdf/2509.04474v1 | 2509.04474 | 10.48550/arXiv.2509.04474 | 5 | 1 | false | null | arXiv.org | 0.283 |
15d6a2f927d4c2ebd145bd467769306cfdc6d0ccd385a856fcf02f73aae45288 | [
"arxiv",
"semantic_scholar"
] | Accelerating Mixture-of-Experts Inference by Hiding Offloading Latency with Speculative Decoding | Recent advancements in Mixture of Experts (MoE) models have significantly increased their parameter scale as well as model performance. Extensive offloading techniques have been proposed to address the GPU memory limitations of MoE inference. However, due to the I/O bottleneck and sparse computation of MoE models, exis... | [
"Zhibin Wang",
"Zhonghui Zhang",
"Yuhang Zhou",
"Zibo Wang",
"Mo Zhou",
"Peng Jiang",
"Weilin Cai",
"Chengying Huan",
"Rong Gu",
"Sheng Zhong",
"Chen Tian"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2025-08-29T00:00:00 | https://arxiv.org/abs/2508.21706 | https://arxiv.org/pdf/2508.21706v2 | 2508.21706 | 10.48550/arXiv.2508.21706 | 2 | 0 | false | null | arXiv.org | 0.2819 |
f54025c03f3782e8ebc58ad19d73578c6a0901e475080f92d8f495e8e518ca06 | [
"arxiv",
"semantic_scholar"
] | Speculative Safety-Aware Decoding | Despite extensive efforts to align Large Language Models (LLMs) with human values and safety rules, jailbreak attacks that exploit certain vulnerabilities continuously emerge, highlighting the need to strengthen existing LLMs with additional safety properties to defend against these attacks. However, tuning large model... | [
"Xuekang Wang",
"Shengyu Zhu",
"Xueqi Cheng"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-08-25T00:00:00 | https://arxiv.org/abs/2508.17739 | https://arxiv.org/pdf/2508.17739v2 | 2508.17739 | 10.48550/arXiv.2508.17739 | 2 | 1 | true | https://github.com/k-k1w-w1x-x/Speculative-Safety-Aware-Decoding | Conference on Empirical Methods in Natural Language Processing | 0.4285 |
582310ee96001a9bd00e7ea681e1420d7ad51ece472ffc4e9d87a8f3056da3ee | [
"arxiv",
"semantic_scholar"
] | SpecVLM: Enhancing Speculative Decoding of Video LLMs via Verifier-Guided Token Pruning | Video large language models (Vid-LLMs) have shown strong capabilities in understanding video content. However, their reliance on dense video token representations introduces substantial memory and computational overhead in both prefilling and decoding. To mitigate the information loss of recent video token reduction me... | [
"Yicheng Ji",
"Jun Zhang",
"Heming Xia",
"Jinpeng Chen",
"Lidan Shou",
"Gang Chen",
"Huan Li"
] | [
"cs.CV",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-08-22T00:00:00 | https://arxiv.org/abs/2508.16201 | https://arxiv.org/pdf/2508.16201v2 | 2508.16201 | 10.48550/arXiv.2508.16201 | 18 | 3 | true | https://github.com/zju-jiyicheng/SpecVLM | Conference on Empirical Methods in Natural Language Processing | 0.4232 |
df8a049eebf8bd12ee5724231258017456ae08970dde5a3d8e428415223a47b5 | [
"arxiv",
"semantic_scholar"
] | Confidence-Modulated Speculative Decoding for Large Language Models | Speculative decoding has emerged as an effective approach for accelerating autoregressive inference by parallelizing token generation through a draft-then-verify paradigm. However, existing methods rely on static drafting lengths and rigid verification criteria, limiting their adaptability across varying model uncertai... | [
"Jaydip Sen",
"Subhasis Dasgupta",
"Hetvi Waghela"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-08-21T00:00:00 | https://arxiv.org/abs/2508.15371 | https://arxiv.org/pdf/2508.15371v1 | 2508.15371 | 10.1109/INDISCON66021.2025.11254640 | 1 | 0 | false | null | null | 0.1735 |
8c765d98efbd5162ad8be764e6c51a4f0c79dc8246c332914d98bb9bdc8fcef7 | [
"arxiv",
"semantic_scholar"
] | READER: Retrieval-Assisted Drafter for Efficient LLM Inference | Autoregressive Language Models instantiate a factorized likelihood over token sequences, yet their strictly sequential decoding process imposes an intrinsic lower bound on inference latency. This bottleneck has emerged as a central obstacle to the scalable deployment of large-scale generative models. Existing accelerat... | [
"Maxim Divilkovskiy",
"Vitaly Malygin",
"Sergey Zlobin",
"Stanislav Ilyushin",
"Sultan Isali",
"Vasily Kalugin",
"Nuriza Aitassova",
"Fei Yi",
"Weidi Zeng"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-08-12T00:00:00 | https://arxiv.org/abs/2508.09072 | https://arxiv.org/pdf/2508.09072v2 | 2508.09072 | 10.48550/arXiv.2508.09072 | 0 | 0 | false | null | arXiv.org | 0.2624 |
f8812d9aa15cb01e05acf34eacaec0b9426cbdb76b6db7cd19cc7507d7a0cf6d | [
"arxiv",
"semantic_scholar"
] | ASPD: Unlocking Adaptive Serial-Parallel Decoding by Exploring Intrinsic Parallelism in LLMs | The increasing scale and complexity of large language models (LLMs) pose significant inference latency challenges, primarily due to their autoregressive decoding paradigm characterized by the sequential nature of next-token prediction. By re-examining the outputs of autoregressive models, we observed that some segments... | [
"Keyu Chen",
"Zhifeng Shen",
"Daohai Yu",
"Haoqian Wu",
"Wei Wen",
"Jianfeng He",
"Ruizhi Qiao",
"Xing Sun"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-08-12T00:00:00 | https://arxiv.org/abs/2508.08895 | https://arxiv.org/pdf/2508.08895v2 | 2508.08895 | 10.48550/arXiv.2508.08895 | 6 | 2 | false | null | arXiv.org | 0.2624 |
3e9fa77cdc3447792bc8baf19bbffe001e1efaf16379fe10d5dc1544e350cc6b | [
"arxiv",
"semantic_scholar"
] | Efficient Speculative Decoding for Llama at Scale: Challenges and Solutions | Speculative decoding is a standard method for accelerating the inference speed of large language models. However, scaling it for production environments poses several engineering challenges, including efficiently implementing different operations (e.g., tree attention and multi-round speculative decoding) on GPU. In th... | [
"Bangsheng Tang",
"Carl Chengyan Fu",
"Fei Kou",
"Grigory Sizov",
"Haoci Zhang",
"Jason Park",
"Jiawen Liu",
"Jie You",
"Qirui Yang",
"Sachin Mehta",
"Shengyong Cai",
"Xiaodong Wang",
"Xingyu Liu",
"Yunlu Li",
"Yanjun Zhou",
"Wei Wei",
"Zhiwei Zhao",
"Zixi Qi",
"Adolfo Victoria",... | [
"cs.CL"
] | [
"Computer Science"
] | 2025-08-11T00:00:00 | https://arxiv.org/abs/2508.08192 | https://arxiv.org/pdf/2508.08192v1 | 2508.08192 | 10.48550/arXiv.2508.08192 | 8 | 2 | false | null | arXiv.org | 0.2612 |
d56d5385f04fa3dd872f88fe9cdf2f251ffdcbf64156bff3310095f5975bc234 | [
"arxiv",
"semantic_scholar"
] | Grouped Speculative Decoding for Autoregressive Image Generation | Recently, autoregressive (AR) image models have demonstrated remarkable generative capabilities, positioning themselves as a compelling alternative to diffusion models. However, their sequential nature leads to long inference times, limiting their practical scalability. In this work, we introduce Grouped Speculative De... | [
"Junhyuk So",
"Juncheol Shin",
"Hyunho Kook",
"Eunhyeok Park"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-08-11T00:00:00 | https://arxiv.org/abs/2508.07747 | https://arxiv.org/pdf/2508.07747v1 | 2508.07747 | 10.1109/ICCV51701.2025.01426 | 13 | 6 | true | https://github.com/junhyukso/GSD | IEEE International Conference on Computer Vision | 0.4225 |
43d2589c16df6f1d0cdafaf9faa07e9245abc1198310a3e99ea6eae9fb251354 | [
"arxiv",
"semantic_scholar"
] | LP-Spec: Leveraging LPDDR PIM for Efficient LLM Mobile Speculative Inference with Architecture-Dataflow Co-Optimization | LLM inference on mobile devices faces extraneous challenges due to limited memory bandwidth and computational resources. To address these issues, speculative inference and processing-in-memory (PIM) techniques have been explored at the algorithmic and hardware levels. However, speculative inference results in more comp... | [
"Siyuan He",
"Zhantong Zhu",
"Yandong He",
"Tianyu Jia"
] | [
"cs.AR"
] | [
"Computer Science"
] | 2025-08-10T00:00:00 | https://arxiv.org/abs/2508.07227 | https://arxiv.org/pdf/2508.07227v3 | 2508.07227 | 10.1109/ICCAD66269.2025.11240889 | 6 | 0 | false | null | null | 0.2113 |
041b82597f1ba8ca7a864e962a2dad0f6bcebaad68080ae301c91dc6af7b3b7f | [
"arxiv",
"semantic_scholar"
] | CARD: A Cache-Assisted Parallel Speculative Decoding Framework via Query-and-Correct Paradigm for Accelerating LLM Inference | Speculative decoding (SD), where a draft model provides multiple candidate tokens for the target model to verify in parallel, has demonstrated significant potential for accelerating LLM inference. Yet, existing SD approaches adhere to a strict draft-then-verify paradigm, enforcing a sequential process that hampers perf... | [
"Enyu Zhou",
"Kai Sheng",
"Hao Chen",
"Xin He"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-08-06T00:00:00 | https://arxiv.org/abs/2508.04462 | https://arxiv.org/pdf/2508.04462v2 | 2508.04462 | null | 0 | 0 | false | null | null | 0.1626 |
c6c07b080e6989ff636f08bcf3b5c24573c2cd66272f03e7f1a517e50ae82fb3 | [
"arxiv",
"semantic_scholar"
] | XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding | Current auto-regressive models can generate high-quality, topologically precise meshes; however, they necessitate thousands-or even tens of thousands-of next-token predictions during inference, resulting in substantial latency. We introduce XSpecMesh, a quality-preserving acceleration method for auto-regressive mesh ge... | [
"Dian Chen",
"Yansong Qu",
"Xinyang Li",
"Ming Li",
"Shengchuan Zhang"
] | [
"cs.GR",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-07-31T00:00:00 | https://arxiv.org/abs/2507.23777 | https://arxiv.org/pdf/2507.23777v2 | 2507.23777 | 10.48550/arXiv.2507.23777 | 4 | 0 | false | null | arXiv.org | 0.2486 |
3a2d2d259747232165d5fd7ac6603592bd111242e4cadacb951d2f7a58c63a1a | [
"arxiv",
"semantic_scholar"
] | Hierarchical Verification of Speculative Beams for Accelerating LLM Inference | Large language models (LLMs) have achieved remarkable success across diverse natural language processing tasks but face persistent challenges in inference efficiency due to their autoregressive nature. While speculative decoding and beam sampling offer notable improvements, traditional methods verify draft sequences se... | [
"Jaydip Sen",
"Harshitha Puvvala",
"Subhasis Dasgupta"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-07-30T00:00:00 | https://arxiv.org/abs/2508.03726 | https://arxiv.org/pdf/2508.03726v1 | 2508.03726 | 10.1007/978-3-032-07735-6_19 | 2 | 0 | false | null | arXiv.org | 0.2475 |
85a18671c07272727909d6c3889128af62a1391015f10e7c52d404e7ac5c7d23 | [
"arxiv",
"semantic_scholar"
] | Spec-VLA: Speculative Decoding for Vision-Language-Action Models with Relaxed Acceptance | Vision-Language-Action (VLA) models have made substantial progress by leveraging the robust capabilities of Visual Language Models (VLMs). However, VLMs' significant parameter size and autoregressive (AR) decoding nature impose considerable computational demands on VLA models. While Speculative Decoding (SD) has shown ... | [
"Songsheng Wang",
"Rucheng Yu",
"Zhihang Yuan",
"Chao Yu",
"Feng Gao",
"Yu Wang",
"Derek F. Wong"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-07-30T00:00:00 | https://arxiv.org/abs/2507.22424 | https://arxiv.org/pdf/2507.22424v2 | 2507.22424 | 10.48550/arXiv.2507.22424 | 25 | 2 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.3537 |
28a30d0efea8f062a95654d87654af295152898cc6351bc8d3810006e5bc765f | [
"arxiv",
"semantic_scholar"
] | Model-free Speculative Decoding for Transformer-based ASR with Token Map Drafting | End-to-end automatic speech recognition (ASR) systems based on transformer architectures, such as Whisper, offer high transcription accuracy and robustness. However, their autoregressive decoding is computationally expensive, hence limiting deployment on CPU-based and resource-constrained devices. Speculative decoding ... | [
"Tuan Vu Ho",
"Hiroaki Kokubo",
"Masaaki Yamamoto",
"Yohei Kawaguchi"
] | [
"cs.CL",
"cs.SD",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2025-07-29T00:00:00 | https://arxiv.org/abs/2507.21522 | https://arxiv.org/pdf/2507.21522v1 | 2507.21522 | 10.48550/arXiv.2507.21522 | 0 | 0 | false | null | European Signal Processing Conference | 0.2464 |
0b3c5255fbfb5f2b3587ffe13e9e62fd1ed173b4ca757e9de08b52c5cbf100d7 | [
"arxiv",
"semantic_scholar"
] | Enhancing Jailbreak Attacks on LLMs via Persona Prompts | Jailbreak attacks aim to exploit large language models (LLMs) by inducing them to generate harmful content, thereby revealing their vulnerabilities. Understanding and addressing these attacks is crucial for advancing the field of LLM safety. Previous jailbreak approaches have mainly focused on direct manipulations of h... | [
"Zheng Zhang",
"Peilin Zhao",
"Deheng Ye",
"Hao Wang"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2025-07-28T00:00:00 | https://arxiv.org/abs/2507.22171 | https://arxiv.org/pdf/2507.22171v3 | 2507.22171 | 10.48550/arXiv.2507.22171 | 8 | 1 | true | https://github.com/CjangCjengh/Generic_Persona | arXiv.org | 0.379 |
d422d6c1cc9967ba5dc818d0636667a38fa381d0d7f215ee46de2d7a29bacec8 | [
"arxiv",
"semantic_scholar"
] | SpecASR: Accelerating LLM-based Automatic Speech Recognition via Speculative Decoding | Large language model (LLM)-based automatic speech recognition (ASR) has recently attracted a lot of attention due to its high recognition accuracy and enhanced multi-dialect support. However, the high decoding latency of LLMs challenges the real-time ASR requirements. Although speculative decoding has been explored for... | [
"Linye Wei",
"Shuzhang Zhong",
"Songqiang Xu",
"Runsheng Wang",
"Ru Huang",
"Meng Li"
] | [
"eess.AS",
"cs.SD"
] | [
"Engineering",
"Computer Science"
] | 2025-07-24T00:00:00 | https://arxiv.org/abs/2507.18181 | https://arxiv.org/pdf/2507.18181v2 | 2507.18181 | 10.1109/DAC63849.2025.11132579 | 3 | 0 | false | null | Design Automation Conference | 0.2406 |
f26ab641d82fc6750345b54d32f8c8e45e634b5d5fe1c950b291d0bdade436bc | [
"arxiv",
"semantic_scholar"
] | DSSD: Efficient Edge-Device LLM Deployment and Collaborative Inference via Distributed Split Speculative Decoding | Large language models (LLMs) have transformed natural language processing but face critical deployment challenges in device-edge systems due to resource limitations and communication overhead. To address these issues, collaborative frameworks have emerged that combine small language models (SLMs) on devices with LLMs a... | [
"Jiahong Ning",
"Ce Zheng",
"Tingting Yang"
] | [
"eess.SP"
] | [
"Engineering"
] | 2025-07-16T00:00:00 | https://arxiv.org/abs/2507.12000 | https://arxiv.org/pdf/2507.12000v2 | 2507.12000 | null | 15 | 1 | true | https://github.com/JasonNing96/DSSD-Efficient-Edge-Computing | null | 0.301 |
f9e6dd0ec9d17211d214885a175a019a55f6ebec66d0e2c187983e23c9a2083d | [
"arxiv",
"semantic_scholar"
] | TPP-SD: Accelerating Transformer Point Process Sampling with Speculative Decoding | We propose TPP-SD, a novel approach that accelerates Transformer temporal point process (TPP) sampling by adapting speculative decoding (SD) techniques from language models. By identifying the structural similarities between thinning algorithms for TPPs and speculative decoding for language models, we develop an effici... | [
"Shukai Gong",
"Yiyang Fu",
"Fengyuan Ran",
"Quyu Kong",
"Feng Zhou"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-07-12T00:00:00 | https://arxiv.org/abs/2507.09252 | https://arxiv.org/pdf/2507.09252v3 | 2507.09252 | 10.48550/arXiv.2507.09252 | 2 | 0 | false | null | arXiv.org | 0.2269 |
4d126d963477bc25ccf29dd451b567121cff15502148c062dcb405cdbcb2c70b | [
"arxiv",
"semantic_scholar"
] | On Evaluating Performance of LLM Inference Serving Systems | The rapid evolution of Large Language Model (LLM) inference systems has yielded significant efficiency improvements. However, our systematic analysis reveals that current evaluation methodologies frequently exhibit fundamental flaws, often manifesting as common evaluation anti-patterns that obscure true performance cha... | [
"Amey Agrawal",
"Nitin Kedia",
"Anmol Agarwal",
"Jayashree Mohan",
"Nipun Kwatra",
"Souvik Kundu",
"Ramachandran Ramjee",
"Alexey Tumanov"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2025-07-11T00:00:00 | https://arxiv.org/abs/2507.09019 | https://arxiv.org/pdf/2507.09019v1 | 2507.09019 | 10.48550/arXiv.2507.09019 | 12 | 0 | false | null | arXiv.org | 0.2785 |
d98830d27b072526e589de5b88b174d3dea6db03e181cb63aecb1157d517a1b0 | [
"arxiv",
"semantic_scholar"
] | On the Convergence Speed of Spatially Coupled LDPC Ensembles Under Window Decoding | It is known that windowed decoding (WD) can effectively balance the performance and complexity of spatially coupled low-density parity-check (LDPC) codes. In this study, we show that information can propagate in a wave-like manner at a constant speed under WD. Additionally, we provide an upper bound for the information... | [
"Qingqing Peng",
"Dongxu Chang",
"Guanghui Wang",
"Guiying Yan"
] | [
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2025-07-09T00:00:00 | https://arxiv.org/abs/2507.06635 | https://arxiv.org/pdf/2507.06635v1 | 2507.06635 | 10.1109/ITW62417.2025.11240495 | 0 | 0 | false | null | Information Theory Workshop | 0.2234 |
fd59f8b8e506cc9333dfce85a86e5d3b2b6848c31954a0c821c378a20756df50 | [
"arxiv",
"semantic_scholar"
] | Cascade: Token-Sharded Private LLM Inference | As LLMs continue to increase in parameter size, the computational resources required to run them are available to fewer parties. Therefore, third-party inference services -- where LLMs are hosted by third parties with significant computational resources -- are becoming increasingly popular. However, third party inferen... | [
"Rahul Thomas",
"Louai Zahran",
"Erica Choi",
"Akilesh Potti",
"Micah Goldblum",
"Arka Pal"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2025-07-07T00:00:00 | https://arxiv.org/abs/2507.05228 | https://arxiv.org/pdf/2507.05228v1 | 2507.05228 | 10.48550/arXiv.2507.05228 | 0 | 0 | false | null | arXiv.org | 0.2211 |
acc11b4c8d216e52fc403a24e82d5b0663ad59410c0c58a7b715eb71805f270a | [
"arxiv",
"semantic_scholar"
] | FlowSpec: Continuous Pipelined Speculative Decoding for Efficient Distributed LLM Inference | Distributed inference serves as a promising approach to enabling the inference of large language models (LLMs) at the network edge. It distributes the inference process to multiple devices to ensure that the LLMs can fit into the device memory. Recent pipeline-based approaches have the potential to parallelize communic... | [
"Xing Liu",
"Lizhuo Luo",
"Ming Tang",
"Chao Huang",
"Xu Chen"
] | [
"cs.DC",
"cs.AI"
] | [
"Computer Science"
] | 2025-07-03T00:00:00 | https://arxiv.org/abs/2507.02620 | https://arxiv.org/pdf/2507.02620v3 | 2507.02620 | 10.48550/arXiv.2507.02620 | 3 | 0 | true | https://github.com/Leosang-lx/FlowSpec#}{https://github.com/Leosang-lx/FlowSpec\#} | arXiv.org | 0.3347 |
03c79b7304a4a68c2e95603bf14f6f657d890ae10004856802a230949e8e5ce8 | [
"arxiv",
"semantic_scholar"
] | OmniDraft: A Cross-vocabulary, Online Adaptive Drafter for On-device Speculative Decoding | Speculative decoding generally dictates having a small, efficient draft model that is either pretrained or distilled offline to a particular target model series, for instance, Llama or Qwen models. However, within online deployment settings, there are two major challenges: 1) usage of a target model that is incompatibl... | [
"Ramchalam Kinattinkara Ramakrishnan",
"Zhaocong Yuan",
"Shaojie Zhuo",
"Chen Feng",
"Yicheng Lin",
"Chenzheng Su",
"Xiaopeng Zhang"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2025-07-03T00:00:00 | https://arxiv.org/abs/2507.02659 | https://arxiv.org/pdf/2507.02659v3 | 2507.02659 | 10.48550/arXiv.2507.02659 | 2 | 1 | false | null | arXiv.org | 0.2166 |
2709f59c07981dedde83e331f291c26d02eab73f5d5018db8144488ffb26dbcd | [
"arxiv",
"semantic_scholar"
] | LogitSpec: Accelerating Retrieval-based Speculative Decoding via Next Next Token Speculation | Speculative decoding (SD), where a small draft model is employed to propose draft tokens in advance and then the target model validates them in parallel, has emerged as a promising technique for LLM inference acceleration. Many endeavors to improve SD are to eliminate the need for a draft model and generate draft token... | [
"Tianyu Liu",
"Qitan Lv",
"Hao Li",
"Xing Gao",
"Xiao Sun",
"Xiaoyan Sun"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-07-02T00:00:00 | https://arxiv.org/abs/2507.01449 | https://arxiv.org/pdf/2507.01449v3 | 2507.01449 | 10.48550/arXiv.2507.01449 | 9 | 0 | true | https://github.com/smart-lty/LogitSpec | arXiv.org | 0.3329 |
10de40d14a89417d36b9abe9e64486d9c28a6ddb30f0fd56675e1db16a1a3cd2 | [
"arxiv",
"semantic_scholar"
] | Quantize-Sample-and-Verify: LLM Acceleration via Adaptive Edge-Cloud Speculative Decoding | In edge-cloud speculative decoding (SD), edge devices equipped with small language models (SLMs) generate draft tokens that are verified by large language models (LLMs) in the cloud. A key bottleneck in such systems is the limited communication bandwidth between edge and cloud, which necessitates quantization of the in... | [
"Guangyi Zhang",
"Yunlong Cai",
"Guanding Yu",
"Petar Popovski",
"Osvaldo Simeone"
] | [
"eess.SP"
] | [
"Engineering",
"Computer Science"
] | 2025-07-01T00:00:00 | https://arxiv.org/abs/2507.00605 | https://arxiv.org/pdf/2507.00605v3 | 2507.00605 | 10.1109/LCOMM.2026.3651580 | 3 | 0 | false | null | IEEE Communications Letters | 0.2143 |
2c284cbcbe0ae757f1f882c4c5f4f452c14971af867075f9d3c6e700c46a2b72 | [
"arxiv",
"semantic_scholar"
] | VOCABTRIM: Vocabulary Pruning for Efficient Speculative Decoding in LLMs | In this paper, we introduce a simple training-free technique to improve the performance of drafter-based speculative decoding (SpD) methods that incorporates language modeling head (LM head) during drafting process. A drafter-based speculative decoding leverages one or more smaller language models, a.k.a. drafters or d... | [
"Raghavv Goel",
"Sudhanshu Agrawal",
"Mukul Gagrani",
"Junyoung Park",
"Yifan Zao",
"He Zhang",
"Tian Liu",
"Yiping Yang",
"Xin Yuan",
"Jiuyan Lu",
"Chris Lott",
"Mingu Lee"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-06-28T00:00:00 | https://arxiv.org/abs/2506.22694 | https://arxiv.org/pdf/2506.22694v2 | 2506.22694 | 10.48550/arXiv.2506.22694 | 9 | 4 | false | null | arXiv.org | 0.3495 |
e6883442625a1a239ccbfe2556c5fe1042d67d1625a9172f85b01bf7d1ccc512 | [
"arxiv",
"semantic_scholar"
] | Scaling Speculative Decoding with Lookahead Reasoning | Reasoning models excel by generating long chain-of-thoughts, but decoding the resulting thousands of tokens is slow. Token-level speculative decoding (SD) helps, but its benefit is capped, because the chance that an entire $γ$-token guess is correct falls exponentially as $γ$ grows. This means allocating more compute f... | [
"Yichao Fu",
"Rui Ge",
"Zelei Shao",
"Zhijie Deng",
"Hao Zhang"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2025-06-24T00:00:00 | https://arxiv.org/abs/2506.19830 | https://arxiv.org/pdf/2506.19830v1 | 2506.19830 | 10.48550/arXiv.2506.19830 | 10 | 2 | true | https://github.com/hao-ai-lab/LookaheadReasoning | arXiv.org | 0.3187 |
59b9ab8a4339927323ccfcfe0e162a7eb92a2d95747ee79e4d9fca1dcd8d0800 | [
"arxiv",
"semantic_scholar"
] | Utility-Driven Speculative Decoding for Mixture-of-Experts | GPU memory bandwidth is the main bottleneck for low-latency Large Language Model (LLM) inference. Speculative decoding leverages idle GPU compute by using a lightweight drafter to propose K tokens, which the LLM verifies in parallel, boosting token throughput. In conventional dense LLMs, all model weights are fetched e... | [
"Anish Saxena",
"Po-An Tsai",
"Hritvik Taneja",
"Aamer Jaleel",
"Moinuddin Qureshi"
] | [
"cs.DC",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-06-17T00:00:00 | https://arxiv.org/abs/2506.20675 | https://arxiv.org/pdf/2506.20675v1 | 2506.20675 | 10.48550/arXiv.2506.20675 | 5 | 0 | false | null | arXiv.org | 0.1982 |
4cee255d37b52f67629ac5e9ba942bc4a74f9e1768cf2f12d21483cfcd9e03f9 | [
"arxiv",
"semantic_scholar"
] | SwiftSpec: Ultra-Low Latency LLM Decoding by Scaling Asynchronous Speculative Decoding | Low-latency decoding for large language models (LLMs) is crucial for applications like chatbots and code assistants, yet generating long outputs remains slow in single-query settings. Prior work on speculative decoding (which combines a small draft model with a larger target model) and tensor parallelism has each accel... | [
"Ziyi Zhang",
"Ziheng Jiang",
"Chengquan Jiang",
"Menghan Yu",
"Size Zheng",
"Haibin Lin",
"Henry Hoffmann",
"Xin Liu"
] | [
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2025-06-12T00:00:00 | https://arxiv.org/abs/2506.11309 | https://arxiv.org/pdf/2506.11309v1 | 2506.11309 | 10.48550/arXiv.2506.11309 | 9 | 0 | false | null | arXiv.org | 0.25 |
62644411d7e6043fc8613a92a5d0dd6817192671126ef3a4bcc2ab1e4fed6d8d | [
"arxiv",
"semantic_scholar"
] | SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving | The growing gap between the increasing complexity of large language models (LLMs) and the limited computational budgets of edge devices poses a key challenge for efficient on-device inference, despite gradual improvements in hardware capabilities. Existing strategies, such as aggressive quantization, pruning, or remote... | [
"Xiangchen Li",
"Dimitrios Spatharakis",
"Saeid Ghafouri",
"Jiakun Fan",
"Hans Vandierendonck",
"Deepu John",
"Bo Ji",
"Dimitrios Nikolopoulos"
] | [
"cs.DC",
"cs.AI",
"cs.LG",
"cs.NI"
] | [
"Computer Science"
] | 2025-06-11T00:00:00 | https://arxiv.org/abs/2506.09397 | https://arxiv.org/pdf/2506.09397v5 | 2506.09397 | 10.1145/3769102.3770608 | 14 | 0 | false | null | IFIP International Information Security Conference | 0.294 |
2fca7375b69324286b366eeac3a3ac3a13d246e78ff08a497ff95adf7c868c56 | [
"arxiv",
"semantic_scholar"
] | Tokenized Bandit for LLM Decoding and Alignment | We introduce the tokenized linear bandit (TLB) and multi-armed bandit (TMAB), variants of linear and stochastic multi-armed bandit problems inspired by LLM decoding and alignment. In these problems, at each round $t \in [T]$, a user submits a query (context), and the decision maker (DM) sequentially selects a token irr... | [
"Suho Shin",
"Chenghao Yang",
"Haifeng Xu",
"Mohammad T. Hajiaghayi"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-06-08T00:00:00 | https://arxiv.org/abs/2506.07276 | https://arxiv.org/pdf/2506.07276v1 | 2506.07276 | 10.48550/arXiv.2506.07276 | 4 | 1 | false | null | International Conference on Machine Learning | 0.1879 |
d8ef199644ac8edfa91b31da024db82fdecc3a2bbf7d67ad738ac90a15c4b566 | [
"arxiv",
"semantic_scholar"
] | Saffron-1: Safety Inference Scaling | Existing safety assurance research has primarily focused on training-phase alignment to instill safe behaviors into LLMs. However, recent studies have exposed these methods' susceptibility to diverse jailbreak attacks. Concurrently, inference scaling has significantly advanced LLM reasoning capabilities but remains une... | [
"Ruizhong Qiu",
"Gaotang Li",
"Tianxin Wei",
"Jingrui He",
"Hanghang Tong"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2025-06-06T00:00:00 | https://arxiv.org/abs/2506.06444 | https://arxiv.org/pdf/2506.06444v2 | 2506.06444 | null | 0 | 0 | true | https://github.com/q-rz/saffron | null | 0.2194 |
4698d1054b05f5c534029fd6af09c688963224b517d759940013f6d1a5511b9e | [
"arxiv",
"semantic_scholar"
] | Guided Speculative Inference for Efficient Test-Time Alignment of LLMs | We propose Guided Speculative Inference (GSI), a novel algorithm for efficient reward-guided decoding in large language models. GSI combines soft best-of-$n$ test-time scaling with a reward model $r(x,y)$ and speculative samples from a small auxiliary model $π_S(y\mid x)$. We provably approximate both the optimal tilte... | [
"Jonathan Geuter",
"Youssef Mroueh",
"David Alvarez-Melis"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-06-04T00:00:00 | https://arxiv.org/abs/2506.04118 | https://arxiv.org/pdf/2506.04118v3 | 2506.04118 | 10.48550/arXiv.2506.04118 | 10 | 0 | true | https://github.com/j-geuter/GSI | arXiv.org | 0.2833 |
fe08fc4092c711017b17a89fb27ecc129373485c19cee6465d437b3a07667550 | [
"arxiv",
"semantic_scholar"
] | AdaDecode: Accelerating LLM Decoding with Adaptive Layer Parallelism | Large language models (LLMs) are increasingly used for long-content generation (e.g., long Chain-of-Thought reasoning) where decoding efficiency becomes a critical bottleneck: Autoregressive decoding is inherently limited by its sequential token generation process, where each token must be generated before the next can... | [
"Zhepei Wei",
"Wei-Lin Chen",
"Xinyu Zhu",
"Yu Meng"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-06-04T00:00:00 | https://arxiv.org/abs/2506.03700 | https://arxiv.org/pdf/2506.03700v1 | 2506.03700 | 10.48550/arXiv.2506.03700 | 4 | 1 | true | https://github.com/weizhepei/AdaDecode | International Conference on Machine Learning | 0.2833 |
5cdbafd441b0b061c80b1cb46dd0e6430083407ed0a465b789cb1acc7f40b542 | [
"arxiv",
"semantic_scholar"
] | POSS: Position Specialist Generates Better Draft for Speculative Decoding | Speculative decoding accelerates Large Language Model (LLM) inference by using a small draft model to predict multiple tokens, and a large target model to verify these tokens in parallel. Recent studies leverage the hidden state of the target model to enhance draft model prediction accuracy. However, existing methods s... | [
"Langlin Huang",
"Chengsong Huang",
"Jixuan Leng",
"Di Huang",
"Jiaxin Huang"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-06-04T00:00:00 | https://arxiv.org/abs/2506.03566 | https://arxiv.org/pdf/2506.03566v1 | 2506.03566 | 10.48550/arXiv.2506.03566 | 2 | 0 | true | https://github.com/shrango/PosS | arXiv.org | 0.2833 |
4d5c930042067467b583f610a26655d7dadaf0962c6ce491f61a85f0fad218d5 | [
"arxiv",
"semantic_scholar"
] | EfficientEdit: Accelerating Code Editing via Edit-Oriented Speculative Decoding | Large Language Models (LLMs) have demonstrated remarkable capabilities in code editing, substantially enhancing software development productivity. However, the inherent complexity of code editing tasks forces existing approaches to rely on LLMs' autoregressive end-to-end generation, where decoding speed plays a critica... | [
"Peiding Wang",
"Li Zhang",
"Fang Liu",
"Yinghao Zhu",
"Wang Xu",
"Lin Shi",
"Xiaoli Lian",
"Minxiao Li",
"Bo Shen",
"An Fu"
] | [
"cs.SE"
] | [
"Computer Science"
] | 2025-06-03T00:00:00 | https://arxiv.org/abs/2506.02780 | https://arxiv.org/pdf/2506.02780v2 | 2506.02780 | 10.1109/ASE63991.2025.00215 | 3 | 0 | true | https://github.com/zhu-zhu-ding/EfficientEdit | International Conference on Automated Software Engineering | 0.2816 |
da97645368cb2c9e6d05c6ecbaa7af3bd7dd6250ec14fa59144fb9fb14f6fba3 | [
"arxiv",
"semantic_scholar"
] | Out-of-Vocabulary Sampling Boosts Speculative Decoding | Speculative decoding relies on fast and accurate drafters. Recent state-of-the-art language models employ larger and larger vocabularies, which significantly slows down drafters. One promising approach to boost the efficiency of speculative decoding is to use drafters with smaller vocabularies. However, existing sampli... | [
"Nadav Timor",
"Jonathan Mamou",
"Oren Pereg",
"Hongyang Zhang",
"David Harel"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2025-06-02T00:00:00 | https://arxiv.org/abs/2506.03206 | https://arxiv.org/pdf/2506.03206v1 | 2506.03206 | 10.48550/arXiv.2506.03206 | 2 | 0 | false | null | arXiv.org | 0.181 |
874e52828838f468316cc8e891a356c6678045d5a9b238734eee6674c6a960db | [
"arxiv",
"semantic_scholar"
] | Mamba Drafters for Speculative Decoding | Speculative decoding has emerged as a promising approach to accelerating large language model (LLM) generation using a fast drafter while maintaining alignment with the target model's distribution. However, existing approaches face a trade-off: external drafters offer flexibility but can suffer from slower drafting, wh... | [
"Daewon Choi",
"Seunghyuk Oh",
"Saket Dingliwal",
"Jihoon Tack",
"Kyuyoung Kim",
"Woomin Song",
"Seojin Kim",
"Insu Han",
"Jinwoo Shin",
"Aram Galstyan",
"Shubham Katiyar",
"Sravan Babu Bodapati"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-06-01T00:00:00 | https://arxiv.org/abs/2506.01206 | https://arxiv.org/pdf/2506.01206v1 | 2506.01206 | 10.48550/arXiv.2506.01206 | 3 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.1799 |
71084d20e540158f60ef7789548939cd9ad6b697d7471784241443e8f6482eb7 | [
"arxiv",
"semantic_scholar"
] | Speculative Reward Model Boosts Decision Making Ability of LLMs Cost-Effectively | Effective decision-making in Large Language Models (LLMs) is essential for handling intricate tasks. However, existing approaches prioritize performance but often overlook the balance between effectiveness and computational cost. To address this, we first introduce the 3E Criteria to systematically assess the cost-effe... | [
"Jiawei Gu",
"Shangsong Liang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-31T00:00:00 | https://arxiv.org/abs/2506.00396 | https://arxiv.org/pdf/2506.00396v1 | 2506.00396 | 10.48550/arXiv.2506.00396 | 1 | 0 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.1788 |
74bcc2ccd109bbc84516624ed182d6ba9ba5baeb7bca4ec8a5a9cb01e88bb445 | [
"arxiv",
"semantic_scholar"
] | Cross-Attention Speculative Decoding | Speculative decoding (SD) is a widely adopted approach for accelerating inference in large language models (LLMs), particularly when the draft and target models are well aligned. However, state-of-the-art SD methods typically rely on tightly coupled, self-attention-based Transformer decoders, often augmented with auxil... | [
"Wei Zhong",
"Manasa Bharadwaj",
"Yixiao Wang",
"Yipeng Ji",
"Chul Lee"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-30T00:00:00 | https://arxiv.org/abs/2505.24544 | https://arxiv.org/pdf/2505.24544v4 | 2505.24544 | 10.48550/arXiv.2505.24544 | 1 | 0 | false | null | arXiv.org | 0.1776 |
2ff89627942428ce4fc33b1f6eb6811df97ed2ea05c84693bb23d5a8f387c873 | [
"arxiv",
"semantic_scholar"
] | CLaSp: In-Context Layer Skip for Self-Speculative Decoding | Speculative decoding (SD) is a promising method for accelerating the decoding process of Large Language Models (LLMs). The efficiency of SD primarily hinges on the consistency between the draft model and the verify model. However, existing drafting approaches typically require additional modules to be trained, which ca... | [
"Longze Chen",
"Renke Shan",
"Huiming Wang",
"Lu Wang",
"Ziqiang Liu",
"Run Luo",
"Jiawei Wang",
"Hamid Alinejad-Rokny",
"Min Yang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-30T00:00:00 | https://arxiv.org/abs/2505.24196 | https://arxiv.org/pdf/2505.24196v1 | 2505.24196 | 10.48550/arXiv.2505.24196 | 7 | 2 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.2386 |
9f29098256d9ca7169695ee9ceaaa861a3f9309f7b35d57d5c3c25e8d30d8cd3 | [
"arxiv",
"semantic_scholar"
] | Ghidorah: Fast LLM Inference on Edge with Speculative Decoding and Hetero-Core Parallelism | In-situ LLM inference on end-user devices has gained significant interest due to its privacy benefits and reduced dependency on external infrastructure. However, as the decoding process is memory-bandwidth-bound, the diverse processing units in modern end-user devices cannot be fully exploited, resulting in slow LLM in... | [
"Jinhui Wei",
"Ye Huang",
"Yuhui Zhou",
"Jiazhi Jiang",
"Jiangsu Du",
"Yutong Lu"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2025-05-29T00:00:00 | https://arxiv.org/abs/2505.23219 | https://arxiv.org/pdf/2505.23219v2 | 2505.23219 | 10.1109/ICCD65941.2025.00043 | 1 | 0 | false | null | ICCD | 0.1765 |
1196216932e701c81a5f2fd544c7a856a00633534f51c6722f91decec81b03a5 | [
"arxiv",
"semantic_scholar"
] | DINGO: Constrained Inference for Diffusion LLMs | Diffusion LLMs have emerged as a promising alternative to conventional autoregressive LLMs, offering significant potential for improved runtime efficiency. However, existing diffusion models lack the ability to provably enforce user-specified formal constraints, such as regular expressions, which makes them unreliable ... | [
"Tarun Suresh",
"Debangshu Banerjee",
"Shubham Ugare",
"Sasa Misailovic",
"Gagandeep Singh"
] | [
"cs.LG",
"cs.PL",
"cs.SE"
] | [
"Computer Science"
] | 2025-05-29T00:00:00 | https://arxiv.org/abs/2505.23061 | https://arxiv.org/pdf/2505.23061v1 | 2505.23061 | 10.48550/arXiv.2505.23061 | 8 | 1 | false | null | arXiv.org | 0.2386 |
8da9a8cdfb15682fbc485755ae1b5428f504050c6ced422164b531f5663a7fad | [
"arxiv",
"semantic_scholar"
] | Speculative Decoding Meets Quantization: Compatibility Evaluation and Hierarchical Framework Design | Speculative decoding and quantization effectively accelerate memory-bound inference of large language models. Speculative decoding mitigates the memory bandwidth bottleneck by verifying multiple tokens within a single forward pass, which increases computational effort. Quantization achieves this optimization by compres... | [
"Yudi Zhang",
"Weilin Zhao",
"Xu Han",
"Tiejun Zhao",
"Wang Xu",
"Hailong Cao",
"Conghui Zhu"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-28T00:00:00 | https://arxiv.org/abs/2505.22179 | https://arxiv.org/pdf/2505.22179v2 | 2505.22179 | 10.48550/arXiv.2505.22179 | 2 | 0 | true | https://github.com/AI9Stars/SpecMQuant | arXiv.org | 0.2709 |
d0c45ed4e43af45f82f41a0619860bc541db51b4fc50c61709407786c315229b | [
"arxiv",
"semantic_scholar"
] | Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding | Diffusion-based large language models (Diffusion LLMs) have shown promise for non-autoregressive text generation with parallel decoding capabilities. However, the practical inference speed of open-sourced Diffusion LLMs often lags behind autoregressive models due to the lack of Key-Value (KV) Cache and quality degradat... | [
"Chengyue Wu",
"Hao Zhang",
"Shuchen Xue",
"Zhijian Liu",
"Shizhe Diao",
"Ligeng Zhu",
"Ping Luo",
"Song Han",
"Enze Xie"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-28T00:00:00 | https://arxiv.org/abs/2505.22618 | https://arxiv.org/pdf/2505.22618v3 | 2505.22618 | 10.48550/arXiv.2505.22618 | 311 | 82 | true | null | arXiv.org | 0.9595 |
d8697f3996050709469d1ec0dd35c9c83d5e75ef77b2a0603c2c606bf8b3dbb9 | [
"arxiv",
"semantic_scholar"
] | SpecExtend: A Drop-in Enhancement for Speculative Decoding of Long Sequences | Speculative decoding is a widely used technique for accelerating inference in large language models (LLMs), but its performance degrades as input length grows, with significant drops even at moderate lengths. Yet, this early degradation has remained largely underexplored. We introduce SpecExtend, a drop-in enhancement ... | [
"Jungyoub Cha",
"Hyunjong Kim",
"Sungzoon Cho"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-27T00:00:00 | https://arxiv.org/abs/2505.20776 | https://arxiv.org/pdf/2505.20776v4 | 2505.20776 | 10.48550/arXiv.2505.20776 | 2 | 0 | true | https://github.com/jycha98/SpecExtend | arXiv.org | 0.2692 |
625fbff4f44ed7c5296ae2f6873b21382ecac6a5adadedcdf50da5e70e39a239 | [
"arxiv",
"semantic_scholar"
] | Fast and Cost-effective Speculative Edge-Cloud Decoding with Early Exits | Large Language Models (LLMs) enable various applications on edge devices such as smartphones, wearables, and embodied robots. However, their deployment often depends on expensive cloud-based APIs, creating high operational costs, which limit access for smaller organizations and raise sustainability concerns. Certain LL... | [
"Yeshwanth Venkatesha",
"Souvik Kundu",
"Priyadarshini Panda"
] | [
"cs.RO",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2025-05-27T00:00:00 | https://arxiv.org/abs/2505.21594 | https://arxiv.org/pdf/2505.21594v1 | 2505.21594 | 10.48550/arXiv.2505.21594 | 11 | 1 | false | null | null | 0.2698 |
3a0a51d7fa8eb3b28c631ee8cd0731056da881c7a554f0f9ac49196d788974e7 | [
"arxiv",
"semantic_scholar"
] | HAMburger: Accelerating LLM Inference via Token Smashing | The growing demand for efficient Large Language Model (LLM) inference requires a holistic optimization on algorithms, systems, and hardware. However, very few works have fundamentally changed the generation pattern: each token needs one forward pass and one KV cache. This can be sub-optimal because we found that LLMs a... | [
"Jingyu Liu",
"Ce Zhang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-26T00:00:00 | https://arxiv.org/abs/2505.20438 | https://arxiv.org/pdf/2505.20438v1 | 2505.20438 | 10.48550/arXiv.2505.20438 | 6 | 1 | false | null | arXiv.org | 0.2113 |
6afdd01eb8661345d865259684968da6504b0bb13a0e7c61598822dad82a788e | [
"arxiv",
"semantic_scholar"
] | MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE | Large Language Models (LLMs) have achieved remarkable success across many applications, with Mixture of Experts (MoE) models demonstrating great potential. Compared to traditional dense models, MoEs achieve better performance with less computation. Speculative decoding (SD) is a widely used technique to accelerate LLM ... | [
"Zongle Huang",
"Lei Zhu",
"Zongyuan Zhan",
"Ting Hu",
"Weikai Mao",
"Xianzhi Yu",
"Yongpan Liu",
"Tianyu Zhang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-26T00:00:00 | https://arxiv.org/abs/2505.19645 | https://arxiv.org/pdf/2505.19645v4 | 2505.19645 | 10.48550/arXiv.2505.19645 | 9 | 0 | false | null | arXiv.org | 0.25 |
5b215da05d0a4327df82fb95463fbd3b4684e88b69d41f90d9a66334247cd2ae | [
"arxiv",
"semantic_scholar"
] | DREAM: Drafting with Refined Target Features and Entropy-Adaptive Cross-Attention Fusion for Multimodal Speculative Decoding | Speculative decoding (SD) has emerged as a powerful method for accelerating autoregressive generation in large language models (LLMs), yet its integration into vision-language models (VLMs) remains underexplored. We introduce DREAM, a novel speculative decoding framework tailored for VLMs that combines three key innova... | [
"Yunhai Hu",
"Tianhua Xia",
"Zining Liu",
"Rahul Raman",
"Xingyu Liu",
"Bo Bao",
"Eric Sather",
"Vithursan Thangarasa",
"Sai Qian Zhang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-25T00:00:00 | https://arxiv.org/abs/2505.19201 | https://arxiv.org/pdf/2505.19201v3 | 2505.19201 | 10.48550/arXiv.2505.19201 | 8 | 1 | true | https://github.com/SAI-Lab-NYU/DREAM.git | arXiv.org | 0.2656 |
febcbb40753457f6a03531857e015cf0b2c86b1cc4081e40c58036c049225ef4 | [
"arxiv",
"semantic_scholar"
] | Think Before You Accept: Semantic Reflective Verification for Faster Speculative Decoding | Large language models (LLMs) suffer from high inference latency due to the auto-regressive decoding process. Speculative decoding accelerates inference by generating multiple draft tokens using a lightweight model and verifying them in parallel. However, existing verification methods rely heavily on distributional cons... | [
"Yixuan Wang",
"Yijun Liu",
"Shiyu ji",
"Yuzhuang Xu",
"Yang Xu",
"Qingfu Zhu",
"Wanxiang Che"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-05-24T00:00:00 | https://arxiv.org/abs/2505.18629 | https://arxiv.org/pdf/2505.18629v1 | 2505.18629 | 10.48550/arXiv.2505.18629 | 5 | 1 | false | null | arXiv.org | 0.1945 |
56268a8b2bb2defcd8a6f5a472dc49a1b25baf01282358ad82f808106bcf39fa | [
"arxiv",
"semantic_scholar"
] | A Survey of LLM $\times$ DATA | The integration of large language model (LLM) and data management (DATA) is rapidly redefining both domains. In this survey, we comprehensively review the bidirectional relationships. On the one hand, DATA4LLM, spanning large-scale data processing, storage, and serving, feeds LLMs with high quality, diversity, and time... | [
"Xuanhe Zhou",
"Junxuan He",
"Wei Zhou",
"Haodong Chen",
"Zirui Tang",
"Haoyu Zhao",
"Xin Tong",
"Guoliang Li",
"Youmin Chen",
"Jun Zhou",
"Zhaojun Sun",
"Binyuan Hui",
"Shuo Wang",
"Conghui He",
"Zhiyuan Liu",
"Jingren Zhou",
"Fan Wu"
] | [
"cs.DB",
"cs.AI",
"cs.CL",
"cs.IR",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-24T00:00:00 | https://arxiv.org/abs/2505.18458 | https://arxiv.org/pdf/2505.18458v3 | 2505.18458 | null | 1 | 0 | true | https://github.com/weAIDB/awesome-data-llm | null | 0.2018 |
6b408b0f7041c30a9c4b35fed8034860dede9999c4702ca30d3c6a7036adee59 | [
"arxiv",
"semantic_scholar"
] | RoleRAG: Enhancing LLM Role-Playing via Graph Guided Retrieval | Large Language Models (LLMs) have shown promise in character imitation, enabling immersive and engaging conversations. However, they often generate content that is irrelevant or inconsistent with a character's background. We attribute these failures to: (1) the inability to accurately recall character-specific knowledg... | [
"Yongjie Wang",
"Jonathan Leung",
"Zhiqi Shen"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2025-05-24T00:00:00 | https://arxiv.org/abs/2505.18541 | https://arxiv.org/pdf/2505.18541v1 | 2505.18541 | 10.48550/arXiv.2505.18541 | 8 | 0 | false | null | arXiv.org | 0.2386 |
9cc1c5c242da58e18e22c66688df5aa9cca6e8699efecd57d0411b8bf8b8f012 | [
"arxiv",
"semantic_scholar"
] | An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs | Recent advances in Large Language Models (LLMs) have led to the widespread adoption of third-party inference services, raising critical privacy concerns. Existing methods of performing private third-party inference, such as Secure Multiparty Computation (SMPC), often rely on cryptographic methods. However, these method... | [
"Rahul Thomas",
"Louai Zahran",
"Erica Choi",
"Akilesh Potti",
"Micah Goldblum",
"Arka Pal"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-23T00:00:00 | https://arxiv.org/abs/2505.18332 | https://arxiv.org/pdf/2505.18332v1 | 2505.18332 | 10.48550/arXiv.2505.18332 | 2 | 0 | false | null | arXiv.org | 0.1696 |
dba740c4fbc963adc8ce65e393c7a45636fa2f85b5eb4c59b17d93c9b454c287 | [
"arxiv",
"semantic_scholar"
] | Reinforcement Speculative Decoding for Fast Ranking | Large Language Models (LLMs) have been widely adopted in ranking systems such as information retrieval (IR) systems and recommender systems (RSs). To alleviate the latency of auto-regressive decoding, some studies explore the single (first) token decoding for ranking approximation, but they suffer from severe degradati... | [
"Yingpeng Du",
"Tianjun Wei",
"Zhu Sun",
"Jie Zhang"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2025-05-23T00:00:00 | https://arxiv.org/abs/2505.20316 | https://arxiv.org/pdf/2505.20316v1 | 2505.20316 | 10.1145/3770854.3780197 | 4 | 0 | false | null | Knowledge Discovery and Data Mining | 0.1747 |
80997e8b8e921f7183e83e59b57f243090f1f2bf1358c7c2ce515dad577ee0bc | [
"arxiv",
"semantic_scholar"
] | KNN-SSD: Enabling Dynamic Self-Speculative Decoding via Nearest Neighbor Layer Set Optimization | Speculative Decoding (SD) has emerged as a widely used paradigm to accelerate the inference of large language models (LLMs) without compromising generation quality. It works by efficiently drafting multiple tokens using a compact model and then verifying them in parallel using the target LLM. Notably, Self-Speculative ... | [
"Mingbo Song",
"Heming Xia",
"Jun Zhang",
"Chak Tou Leong",
"Qiancheng Xu",
"Wenjie Li",
"Sujian Li"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-22T00:00:00 | https://arxiv.org/abs/2505.16162 | https://arxiv.org/pdf/2505.16162v2 | 2505.16162 | 10.48550/arXiv.2505.16162 | 7 | 1 | false | null | Conference of the European Chapter of the Association for Computational Linguistics | 0.2258 |
3d88c8e0624bfcf7a85816dbfc7cb8fffbae4a820c8f0cc0ef6dfbe71c1db87f | [
"arxiv",
"semantic_scholar"
] | BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms | Speculative decoding has emerged as a popular method to accelerate the inference of Large Language Models (LLMs) while retaining their superior text generation performance. Previous methods either adopt a fixed speculative decoding configuration regardless of the prefix tokens, or train draft models in an offline or on... | [
"Yunlong Hou",
"Fengzhuo Zhang",
"Cunxiao Du",
"Xuan Zhang",
"Jiachun Pan",
"Tianyu Pang",
"Chao Du",
"Vincent Y. F. Tan",
"Zhuoran Yang"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-05-21T00:00:00 | https://arxiv.org/abs/2505.15141 | https://arxiv.org/pdf/2505.15141v2 | 2505.15141 | 10.48550/arXiv.2505.15141 | 12 | 2 | false | null | International Conference on Machine Learning | 0.2785 |
41fbeadd3e9a1a262543b4bc6ee522f452d040120be0817b0f6e732c7cfae48c | [
"arxiv",
"semantic_scholar"
] | Accelerating Autoregressive Speech Synthesis Inference With Speech Speculative Decoding | Modern autoregressive speech synthesis models leveraging language models have demonstrated remarkable performance. However, the sequential nature of next token prediction in these models leads to significant latency, hindering their deployment in scenarios where inference speed is critical. In this work, we propose Spe... | [
"Zijian Lin",
"Yang Zhang",
"Yougen Yuan",
"Yuming Yan",
"Jinjiang Liu",
"Zhiyong Wu",
"Pengfei Hu",
"Qun Yu"
] | [
"cs.SD",
"cs.AI",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2025-05-21T00:00:00 | https://arxiv.org/abs/2505.15380 | https://arxiv.org/pdf/2505.15380v2 | 2505.15380 | 10.48550/arXiv.2505.15380 | 6 | 1 | false | null | Interspeech | 0.2113 |
349a19a188c37991f76e5642cbccf771803718665627aca56ea602949140ba22 | [
"arxiv",
"semantic_scholar"
] | Semi-Clairvoyant Scheduling of Speculative Decoding Requests to Minimize LLM Inference Latency | Speculative decoding accelerates Large Language Model (LLM) inference by employing a small speculative model (SSM) to generate multiple candidate tokens and verify them using the LLM in parallel. This technique has been widely integrated into LLM inference serving systems. However, inference requests typically exhibit ... | [
"Ruixiao Li",
"Fahao Chen",
"Peng Li"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-20T00:00:00 | https://arxiv.org/abs/2505.17074 | https://arxiv.org/pdf/2505.17074v1 | 2505.17074 | 10.48550/arXiv.2505.17074 | 0 | 0 | false | null | International Joint Conference on Artificial Intelligence | 0.1661 |
7804e2f5a3739bf3c46ca4cefd0542741de31932d3b973ee9fe8a66393d63819 | [
"arxiv",
"semantic_scholar"
] | STree: Speculative Tree Decoding for Hybrid State-Space Models | Speculative decoding is a technique to leverage hardware concurrency in order to enable multiple steps of token generation in a single forward pass, thus improving the efficiency of large-scale autoregressive (AR) Transformer models. State-space models (SSMs) are already more efficient than AR Transformers, since their... | [
"Yangchao Wu",
"Zongyue Qin",
"Alex Wong",
"Stefano Soatto"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-20T00:00:00 | https://arxiv.org/abs/2505.14969 | https://arxiv.org/pdf/2505.14969v2 | 2505.14969 | 10.48550/arXiv.2505.14969 | 4 | 0 | true | https://github.com/wyc1997/stree | arXiv.org | 0.2568 |
e6f0c3891a75ddbcfb95e415fa33476df62d6648ec5f3722fe7c605b7ff2d87d | [
"arxiv",
"semantic_scholar"
] | Speculative Decoding Reimagined for Multimodal Large Language Models | This paper introduces Multimodal Speculative Decoding (MSD) to accelerate Multimodal Large Language Models (MLLMs) inference. Speculative decoding has been shown to accelerate Large Language Models (LLMs) without sacrificing accuracy. However, current speculative decoding methods for MLLMs fail to achieve the same spee... | [
"Luxi Lin",
"Zhihang Lin",
"Zhanpeng Zeng",
"Rongrong Ji"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-20T00:00:00 | https://arxiv.org/abs/2505.14260 | https://arxiv.org/pdf/2505.14260v1 | 2505.14260 | 10.48550/arXiv.2505.14260 | 6 | 3 | true | https://github.com/Lyn-Lucy/MSD | arXiv.org | 0.301 |
452cfdf70ebd5ed96dfd486408b4a99b4f8c5cc487ccce6a53b5f6a69b93045c | [
"arxiv",
"semantic_scholar"
] | HeteroSpec: Leveraging Contextual Heterogeneity for Efficient Speculative Decoding | Autoregressive decoding inherently limits the inference throughput of Large Language Model (LLM) due to its sequential dependency. Speculative decoding mitigates this by verifying multiple predicted tokens in parallel, but its efficiency remains constrained by what we identify as verification heterogeneity -- the uneve... | [
"Siran Liu",
"Yang Ye",
"Qianchao Zhu",
"Zane Cao",
"Yongchao He"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-19T00:00:00 | https://arxiv.org/abs/2505.13254 | https://arxiv.org/pdf/2505.13254v2 | 2505.13254 | 10.48550/arXiv.2505.13254 | 1 | 0 | false | null | arXiv.org | 0.165 |
c4e3c1a68326dbb905e7c9aa82d85f85a3689de6da87a3d1365623758b352577 | [
"arxiv",
"semantic_scholar"
] | SpecFLASH: A Latent-Guided Semi-autoregressive Speculative Decoding Framework for Efficient Multimodal Generation | Large language models and large multimodal models (LLMs and LMMs) deliver strong generative performance but suffer from slow decoding, a problem that becomes more severe when handling visual inputs, whose sequences typically contain many more tokens with lower information density than text. Speculative decoding acceler... | [
"Zihua Wang",
"Ruibo Li",
"Haozhe Du",
"Joey Tianyi Zhou",
"Yu Zhang",
"Xu Yang"
] | [
"cs.CV",
"cs.MM"
] | [
"Computer Science"
] | 2025-05-19T00:00:00 | https://arxiv.org/abs/2505.12728 | https://arxiv.org/pdf/2505.12728v3 | 2505.12728 | null | 1 | 0 | true | https://github.com/ZihuaEvan/FlashSD/ | null | 0.195 |
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