ASPIRE introduces a non‑synchronized batched self‑speculative decoding framework for long‑context LLM inference, addressing the memory bottleneck of attention by drafting tokens with sparse attention and verifying them with full attention. It combines a unified mixed forward pass, a lightweight online speculation scheduler that lets each request independently decide when to verify, and an intra‑draft refresh layer that updates the sparse context at every draft step. Experiments on three models and five benchmarks show 1.70–4.58× speedup over autoregressive baselines and a 27% average improvement over the strongest prior self‑speculative methods.
By Amir Ziashahabi, Hossein Entezari Zarch, Lei Gao, Murali Annavaram, Salman Avestimehr
arXiv:2512.02337v2 Announce Type: replace
Abstract: Growing demands from tasks like code generation, deep reasoning, and long-document understanding have made long-context generation a crucial capabi...
By Zhendong Tan, Xingjun Zhang, Chaoyi Hu, Junjie Peng, Kun Xia
arXiv:2609.37029v1 Announce Type: cross
Abstract: Speculative decoding accelerates autoregressive inference by verifying multiple draft tokens in a single target forward pass. However, as the context...
By Hao-Yuan He, Peng-Fei Liu, Si Shen, Ming Li
arXiv:2609.17184v1 Announce Type: new
Abstract: Looped Transformers achieve strong performance with compact parameter sizes by repeatedly applying a shared stack of Transformer blocks across recurren...
By SangLyul Cho, Langqing Cui, Sehoon Kim, Dongsu Han, Insu Han
arXiv:2407. 21082v3 Announce Type: replace-cross Abstract: This paper presents a modular approach to accelerate inference in large language models (LLMs) by adding early exit heads at intermediate transformer layers.
By Florian Valade
arXiv:2607. 08690v1 Announce Type: cross Abstract: Speculative decoding accelerates sampling from an autoregressive LLM by using a faster auxiliary model to draft tokens which are then verified in parallel by the LLM.
By Guoxuan Xia, Luka Ribar, Paul Balanca
WaveFront Decoding (WFD) is a training‑free, self‑speculative decoding framework for looped language models that reduces decoding latency by batching draft and verification steps within the same recurrent‑block calls. By exploiting intermediate recurrence outputs as draft predictions and weight sharing to process token states at different depths together, WFD arranges mixed‑depth states into a diagonal wavefront, allowing shallow‑depth drafting while deeper‑depth verification proceeds concurrently. Experiments on six Spec‑Bench task categories show WFD achieving up to 4.81× speedup on Huginn‑3.5B compared to autoregressive decoding, outperforming traditional draft‑then‑verify approaches.
By Hyeongju Ha, Jae-Joon Kim
arXiv:2609.24197v1 Announce Type: new
Abstract: Speculative decoding losslessly accelerates large language model inference by having a lightweight draft model predict future tokens for verification b...
By Weifan Jiang, Krishna Teja Chitty-Venkata, Megan Flynn, Reed Meyerson, Zhenting Qi, Tianyu Wu, Eldar Kurtic, Minlan Yu, Alexandre Marques
arXiv:2608. 08721v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model inference by drafting multiple tokens for parallel verification, with efficiency critically determined by the speculative length selected at each decoding round.
By Zexun Lin, Yuan Feng, Junlin Lv, Kevin S. Zhou, Xike Xie
arXiv:2606. 02544v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs, offering faster inference through parallel or blockwise decoding.
By Junxia Cui, Haotian Ye, Runchu Tian, Hongcan Guo, Jinya Jiang, Haoru Li, Chaojie Ren, Yiming Huang, Kaijie Zhu, Zhongkai Yu, Kun Zhou, Jingbo Shang
arXiv:2608.29748v1 Announce Type: new
Abstract: Speculative decoding accelerates autoregressive language model inference by having a lightweight draft model propose multiple candidate tokens, which a...
By Luxi Lin, Zhanpeng Zeng, Shuang Peng, Songwei Liu, Rongrong Ji
The paper introduces TSS, a target-side sparsification framework that selectively skips layers in a target verifier during speculative decoding for domain-specific large language models. By exploring multi-layer skip configurations with an acceptance- and metric-aware breadth search, TSS reduces verification cost, increases draft acceptance, and can even improve downstream task performance without retraining. Experiments on Spec-Bench demonstrate consistent gains across domains and model scales, notably boosting translation throughput by 1.68× and improving BLEU scores significantly.
By Haibo Hu, Lianming Huang, Qiao Li, Nan Guan, Chun Jason Xue