9 papers
APT: Atomic Physical Transitions for Causal Video-Language Understanding
Shang Wu, Haoran Lu, Songling Liu +7
Physical events are not understood by their names alone, but by the causal state changes that compose them. A clip-level label such as "bounce" can be correct while hiding the proc…
Phys4D: Fine-Grained Physics-Consistent 4D Modeling from Video Diffusion
Haoran Lu, Shang Wu, Songling Liu +10
Recent video diffusion models have achieved impressive capabilities as large-scale generative world models. However, these models often struggle with fine-grained physical consiste…
Towards Sparse Video Understanding and Reasoning
Chenwei Xu, Zhen Ye, Shang Wu +8
We present \revise (\underline{Re}asoning with \underline{Vi}deo \underline{S}parsity), a multi-round agent for video question answering (VQA). Instead of uniformly sampling frames…
PhyPrompt: RL-based Prompt Refinement for Physically Plausible Text-to-Video Generation
Shang Wu, Chenwei Xu, Zhuofan Xia +6
State-of-the-art text-to-video (T2V) generators frequently violate physical laws despite high visual quality. We show this stems from insufficient physical constraints in prompts r…
AdvEvo-MARL: Shaping Internalized Safety through Adversarial Co-Evolution in Multi-Agent Reinforcement Learning
Zhenyu Pan, Yiting Zhang, Zhuo Liu +13
LLM-based multi-agent systems excel at planning, tool use, and role coordination, but their openness and interaction complexity also expose them to jailbreak, prompt-injection, and…
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety
Zhenyu Pan, Yiting Zhang, Yutong Zhang +8
Multi-agent systems (MAS) built on multimodal large language models exhibit strong collaboration and performance. However, their growing openness and interaction complexity pose se…