6 papers
Scaling Latent Reasoning via Looped Language Models
Rui-Jie Zhu, Zixuan Wang, Kai Hua +30
Modern LLMs are trained to "think" primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training and under-leverages pre-training…
Story-Iter: A Training-free Iterative Paradigm for Long Story Visualization
Jiawei Mao, Xiaoke Huang, Yunfei Xie +7
This paper introduces Story-Iter, a new training-free iterative paradigm to enhance long-story generation. Unlike existing methods that rely on fixed reference images to construct…
P-EAGLE: Parallel-Drafting EAGLE with Scalable Training
Mude Hui, Xin Huang, Jaime Campos Salas +5
Reasoning LLMs produce longer outputs, requiring speculative decoding drafters trained on extended sequences. Parallel drafting - predicting multiple tokens per forward pass - offe…
ARFlow: Autoregressive Flow with Hybrid Linear Attention
Mude Hui, Rui-Jie Zhu, Songlin Yang +5
Flow models are effective at progressively generating realistic images, but they generally struggle to capture long-range dependencies during the generation process as they compres…
: CoT-Like Instruction Generation for Complexity-Controllable Image Editing Benchmark
Siwei Yang, Mude Hui, Bingchen Zhao +3
We introduce , a comprehensive benchmark designed to systematically evaluate instruction-based image editing models across instructions of varying complexity…
FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space
Yiyang Guo, Ruizhe Li, Mude Hui +5
Invisible watermarking is essential for safeguarding digital content, enabling copyright protection and content authentication. However, existing watermarking methods fall short in…