103 citations · 278 across the 43 of their papers we have counts for
46 papers
Structured Residual Connectivity Matters for Diffusion Transformers
Yuhe Liu, Xinyin Ma, Gongfan Fang +2
Diffusion Transformers (DiTs) have established themselves as a scalable backbone for high-fidelity image synthesis. However, unlike U-Net based diffusion models that rely on rigid,…
dMoE: dLLMs with Learnable Block Experts
Sicheng Feng, Zigeng Chen, Gongfan Fang +2
Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive models, offering competitive performance while naturally supporting paral…
Q-ARVD: Quantizing Autoregressive Video Diffusion Models
Siao Tang, Xinyin Ma, Gongfan Fang +2
Autoregressive video diffusion models (ARVDs) have emerged as a promising architecture for streaming video generation, paving the way for real-time interactive video generation and…
Mix-Quant: Quantized Prefilling, Precise Decoding for Agentic LLMs
Haiquan Lu, Zigeng Chen, Gongfan Fang +2
LLM agents have recently emerged as a powerful paradigm for solving complex tasks through planning, tool use, memory retrieval, and multi-step interaction. However, these agentic w…
DMax: Aggressive Parallel Decoding for dLLMs
Zigeng Chen, Gongfan Fang, Xinyin Ma +2
We present DMax, a new paradigm for efficient diffusion language models (dLLMs). It mitigates error accumulation in parallel decoding, enabling aggressive decoding parallelism whil…
Rethinking Token Reduction for Large Vision-Language Models
Yi Wang, Haofei Zhang, Qihan Huang +7
Large Vision-Language Models (LVLMs) excel in visual understanding and reasoning, but the excessive visual tokens lead to high inference costs. Although recent token reduction meth…
Invisible Safety Threat: Malicious Finetuning for LLM via Steganography
Guangnian Wan, Xinyin Ma, Gongfan Fang +1
Understanding and addressing potential safety alignment risks in large language models (LLMs) is critical for ensuring their safe and trustworthy deployment. In this paper, we high…
Self-Purification Mitigates Backdoors in Multimodal Diffusion Language Models
Guangnian Wan, Qi Li, Gongfan Fang +2
Multimodal Diffusion Language Models (MDLMs) have recently emerged as a competitive alternative to their autoregressive counterparts. Yet their vulnerability to backdoor attacks re…
dVoting: Fast Voting for dLLMs
Sicheng Feng, Zigeng Chen, Xinyin Ma +2
Diffusion Large Language Models (dLLMs) represent a new paradigm beyond autoregressive modeling, offering competitive performance while naturally enabling a flexible decoding proce…
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…
MixReasoning: Switching Modes to Think
Haiquan Lu, Gongfan Fang, Xinyin Ma +2
Reasoning models enhance performance by tackling problems in a step-by-step manner, decomposing them into sub-problems and exploring long chains of thought before producing an answ…
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…
PixelThink: Towards Efficient Chain-of-Pixel Reasoning
Song Wang, Gongfan Fang, Lingdong Kong +6
Existing reasoning segmentation approaches typically fine-tune multimodal large language models (MLLMs) using image-text pairs and corresponding mask labels. However, they exhibit…
PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning
Song Wang, Xiaolu Liu, Lingdong Kong +6
Self-supervised representation learning for point cloud has demonstrated effectiveness in improving pre-trained model performance across diverse tasks. However, as pre-trained mode…
VeriThinker: Learning to Verify Makes Reasoning Model Efficient
Zigeng Chen, Xinyin Ma, Gongfan Fang +2
Large Reasoning Models (LRMs) excel at complex tasks using Chain-of-Thought (CoT) reasoning. However, their tendency to overthinking leads to unnecessarily lengthy reasoning chains…
dKV-Cache: The Cache for Diffusion Language Models
Xinyin Ma, Runpeng Yu, Gongfan Fang +1
Diffusion Language Models (DLMs) have been seen as a promising competitor for autoregressive language models. However, diffusion language models have long been constrained by slow…
Efficient Reasoning Models: A Survey
Sicheng Feng, Gongfan Fang, Xinyin Ma +1
Reasoning models have demonstrated remarkable progress in solving complex and logic-intensive tasks by generating extended Chain-of-Thoughts (CoTs) prior to arriving at a final ans…
CoT-Valve: Length-Compressible Chain-of-Thought Tuning
Xinyin Ma, Guangnian Wan, Runpeng Yu +2
Chain-of-Thought significantly enhances a model's reasoning capability, but it also comes with a considerable increase in inference costs due to long chains. With the observation t…
Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising
Gongfan Fang, Xinyin Ma, Xinchao Wang
Transformer-based diffusion models have achieved significant advancements across a variety of generative tasks. However, producing high-quality outputs typically necessitates large…
TinyFusion: Diffusion Transformers Learned Shallow
Gongfan Fang, Kunjun Li, Xinyin Ma +1
Diffusion Transformers have demonstrated remarkable capabilities in image generation but often come with excessive parameterization, resulting in considerable inference overhead in…