12 papers
Awakening Diffusion Transformers: Eliciting Stronger Generation and Understanding via Massive Activation Modulation
Chaofan Gan, Zicheng Zhao, Yuanpeng Tu +6
Massive Activations (MAs) have been widely observed in Transformer-based models, yet their structure and functional roles in Diffusion Transformers (DiTs) remain insufficiently und…
Multimodal Continuous Reasoning via Asymmetric Mutual Variational Learning
Shijie Li, Yilin Gao, Siyuan Yang +7
Multimodal Large Language Models (MLLMs) are often constrained by a language-space bottleneck, forcing complex visual reasoning into discrete tokens which can lose perceptual nuanc…
GRACE: Boosting Video MLLMs with Grounded Action-Centric Evidence for Viewer Sentiment Prediction
Ruoxuan Yang, Tieyuan Chen, Xiaofeng Huang +6
Viewer sentiment prediction in video advertisements aims to infer the latent affective response evoked in the audience. To bridge the gap between what is shown and what is felt, mo…
CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit
Kangyu Wang, Zhiyun Jiang, Haibo Feng +5
Diffusion large language models (dLLMs) generate text through iterative denoising. In commonly adopted parallel decoding schemes, each step confirms only high-confidence positions…
From Priors to Perception: Grounding Video-LLMs in Physical Reality
Zicheng Zhao, Chaofan Gan, Shijie Li +1
While Video Large Language Models (Video-LLMs) excel in general understanding, they exhibit systematic deficits in fine-grained physical reasoning. Existing interventions not only…
VidLaDA: Bidirectional Diffusion Large Language Models for Efficient Video Understanding
Zhihao He, Tieyuan Chen, Kangyu Wang +6
Current Video Large Language Models (Video LLMs) typically encode frames via a vision encoder and employ an autoregressive (AR) LLM for understanding and generation. However, this…