74 citations · 130 across the 36 of their papers we have counts for
12 papers · 1 filter
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…
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…
Dimple: Discrete Diffusion Multimodal Large Language Model with Parallel Decoding
Runpeng Yu, Xinyin Ma, Xinchao Wang
In this work, we propose Dimple, the first Discrete Diffusion Multimodal Large Language Model (DMLLM). We observe that training with a purely discrete diffusion approach leads to s…
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…
Introducing Visual Perception Token into Multimodal Large Language Model
Runpeng Yu, Xinyin Ma, Xinchao Wang
To utilize visual information, Multimodal Large Language Model (MLLM) relies on the perception process of its vision encoder. The completeness and accuracy of visual perception sig…
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…