arXiv Computation and Language
Oct 1

DEdit: Iterative Draft Editing for Speculative Decoding

arXiv:2609.38510v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive LLMs by having a lightweight drafter propose tokens that the target model verifies in parallel. Diffusi...

By Longxuan Yu, Bingsen Chen, Peng Shi, Dongkyu Lee, Yi Xiang, Hideo Kobayashi, Sheng Zhang, Shuaichen Chang, Xing Niu, Zhuoyan Xu, Greg Ver Steeg, Jiarong Jiang
arXiv AI
Oct 12

AutoIndexer: Flexible-Order Decoding by Training Causal Models on Chains of Edits

AutoIndexer is a generative model that trains causal models on chains of edits, enabling flexible post‑generation modifications such as token insertions, substitutions, and deletions. It introduces marker tokens for opening and closing edits and moves a cursor through the token sequence, mimicking a text editor. By perturbing ground‑truth labels with inverse edit operations during training, AutoIndexer becomes robust to erroneous context and can perform editing tasks like code correction while maintaining performance on standard LLM benchmarks.

By Shu Ishida, Aliasghar Khani, Tianyu Zhang, The Cong Luong, James Seale Smith, Adam Gaier
arXiv Machine Learning
1d ago

LED: Improving Masked Diffusion Language Models by Lookahead Editing

The paper introduces Lookahead Editing (LED), a method that enhances masked diffusion language models (MDMs) by adding a rewritable proposal state. LED performs multiple lookahead updates at each decoding step to refine future predictions before committing tokens, thereby improving expressivity while retaining MDMs’ simple transition schedule. Fine‑tuned LED models (LED‑7B and LED‑8B) achieve significant performance gains on eight benchmarks and outperform larger models such as LLaDA2.1‑16B using only supervised fine‑tuning.

By Yuyan Zhou, Zeyuan Yang, Yuxuan Song, Zhicheng Cai, Hongli Yu, Weiying Ma, Hao Zhou, James Kwok