14 papers
In-Video Instructions: Visual Signals as Generative Control
Gongfan Fang, Xinyin Ma, Xinchao Wang
Large-scale video generative models have recently demonstrated strong visual capabilities, enabling the prediction of future frames that adhere to the logical and physical cues in…
dParallel: Learnable Parallel Decoding for dLLMs
Zigeng Chen, Gongfan Fang, Xinyin Ma +2
Diffusion large language models (dLLMs) have recently drawn considerable attention within the research community as a promising alternative to autoregressive generation, offering p…
SparseD: Sparse Attention for Diffusion Language Models
Zeqing Wang, Gongfan Fang, Xinyin Ma +2
While diffusion language models (DLMs) offer a promising alternative to autoregressive models (ARs), existing open-source DLMs suffer from high inference latency. This bottleneck i…
Thinkless: LLM Learns When to Think
Gongfan Fang, Xinyin Ma, Xinchao Wang
Reasoning Language Models, capable of extended chain-of-thought reasoning, have demonstrated remarkable performance on tasks requiring complex logical inference. However, applying…
ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation
Siao Tang, Xinyin Ma, Gongfan Fang +1
Recent advancements in large reasoning models (LRMs) like DeepSeek-R1 and OpenAI o1 series have achieved notable performance enhancements on complex reasoning tasks by scaling up t…
Diversity-Guided MLP Reduction for Efficient Large Vision Transformers
Chengchao Shen, Hourun Zhu, Gongfan Fang +2
Transformer models achieve excellent scaling property, where the performance is improved with the increment of model capacity. However, large-scale model parameters lead to an unaf…