8 papers · 1 filter
Contextualized Evaluation of Vision Language Models through Dynamic, Multi-turn Interactions
Yijiang Li, Huiqi Zou, Bingyang Wang +1
Multi-modal Large Language Models (MLLMs) have made substantial advances on benchmarks, yet their real-world effectiveness remains uncertain. This gap stems from the fundamental mi…
SPACENUM: Revisiting Spatial Numerical Understanding in VLMs
Jianshu Zhang, Yijiang Li, Huifeixin Chen +4
Vision-Language Models (VLMs) are increasingly deployed in embodied environments, where they need produce numerical outputs such as action magnitudes and spatial coordinates. Altho…
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…
Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini, Omri Abend, Dina Acklin +105
This volume includes a selection of papers presented at the Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2025 in Philadelphia US on 3rd March 2…
The Philosophical Foundations of Growing AI Like A Child
Dezhi Luo, Yijiang Li, Hokin Deng
Despite excelling in high-level reasoning, current language models lack robustness in real-world scenarios and perform poorly on fundamental problem-solving tasks that are intuitiv…
Vision Language Models Know Law of Conservation without Understanding More-or-Less
Dezhi Luo, Haiyun Lyu, Qingying Gao +3
Understanding law of conservation is a critical milestone in human cognitive development considered to be supported by the apprehension of quantitative concepts and the reversibili…