7 papers
Act on What You See: Unlocking Safe Social Navigation in Vision-Language-Action Models
Qingzi Wang, Xiyang Wu, Guangyao Shi +3
Safe social navigation requires robots to distinguish people from ordinary obstacles and to react before danger becomes imminent. We show that pretrained Vision-Language-Action (VL…
Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks
Xiyang Wu, Zongxia Li, Guangyao Shi +5
Long horizon interactive environments are a testbed for evaluating agents skill usage abilities. These environments demand multi step reasoning, the chaining of multiple skills ove…
MASS: Motion-Aware Spatial-Temporal Grounding for Physics Reasoning and Comprehension in Vision-Language Models
Xiyang Wu, Zongxia Li, Jihui Jin +7
Vision Language Models (VLMs) perform well on standard video tasks but struggle with physics-related reasoning involving motion dynamics and spatial interactions. We present a nove…
SABER: A Stealthy Agentic Black-Box Attack Framework for Vision-Language-Action Models
Xiyang Wu, Guangyao Shi, Qingzi Wang +3
Vision-language-action (VLA) models enable robots to follow natural-language instructions grounded in visual observations, but the instruction channel also introduces a critical vu…
First Frame Is the Place to Go for Video Content Customization
Jingxi Chen, Zongxia Li, Zhichao Liu +6
What role does the first frame play in video generation models? Traditionally, it's viewed as the spatial-temporal starting point of a video, merely a seed for subsequent animation…
VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding
Zongxia Li, Xiyang Wu, Guangyao Shi +6
Vision-Language Models (VLMs) have achieved strong results in video understanding, yet a key question remains: do they truly comprehend visual content or only learn shallow correla…