2 citations · 3 across the 6 of their papers we have counts for
7 papers
Vision Language Models Cannot Reason About Physical Transformation
Dezhi Luo, Yijiang Li, Maijunxian Wang +7
Understanding physical transformations is fundamental for reasoning in dynamic environments. While Vision Language Models (VLMs) show promise in embodied applications, whether they…
Egocentric Bias in Vision-Language Models
Maijunxian Wang, Yijiang Li, Bingyang Wang +6
Visual perspective taking--inferring how the world appears from another's viewpoint--is foundational to social cognition. We introduce FlipSet, a diagnostic benchmark for Level-2 v…
Vision-Language Models Mistake Head Orientation for Gaze Direction: Nonverbal Conversation Cues
Zory Zhang, Pinyuan Feng, Bingyang Wang +7
Where someone looks is a nonverbal communication cue that children and adults readily use. How well can Vision-Language Models (VLMs) infer gaze targets? To construct evaluation st…
Increasing Computation Resolves Conflicts in Vision Language Models
Bingyang Wang, Yijiang Li, Yitong Qiao +7
Cognitive control, the ability to coordinate competing information sources in pursuit of goals, is fundamental to intelligent behavior. We systematically investigate whether Vision…
Achilles Heel of Distributed Multi-Agent Systems
Yiting Zhang, Yijiang Li, Tianwei Zhao +3
Multi-agent system (MAS) has demonstrated exceptional capabilities in addressing complex challenges, largely due to the integration of multiple large language models (LLMs). Howeve…
Analyzable Chain-of-Musical-Thought Prompting for High-Fidelity Music Generation
Max W. Y. Lam, Yijin Xing, Weiya You +14
Autoregressive (AR) models have demonstrated impressive capabilities in generating high-fidelity music. However, the conventional next-token prediction paradigm in AR models does n…
Core Knowledge Deficits in Multi-Modal Language Models
Yijiang Li, Qingying Gao, Tianwei Zhao +8
While Multi-modal Large Language Models (MLLMs) demonstrate impressive abilities over high-level perception and reasoning, their robustness in the wild remains limited, often falli…