activity
20242026
most citedVision Language Models See What You Want but not What You See

2 citations · 3 across the 11 of their papers we have counts for

collaborators

12 papers

cs.CV2026

VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning

Junxiang Xu, Ruisi Wang, Fanyi Pu +49

Native visual reasoning treats visual generation as the medium of reasoning itself: visual states (i.e. images and videos) are not merely inputs to be understood or outputs to be r…

cs.AI2026

MagicAgent: Towards Generalized Agent Planning

Xuhui Ren, Shaokang Dong, Chen Yang +21

The evolution of Large Language Models (LLMs) from passive text processors to autonomous agents has established planning as a core component of modern intelligence. However, achiev…

cs.CV2026

A Very Big Video Reasoning Suite

Maijunxian Wang, Ruisi Wang, Juyi Lin +53

Rapid progress in video models has largely focused on visual quality, leaving their reasoning capabilities underexplored. Video reasoning grounds intelligence in spatiotemporally c…

cs.CV2026★ 1 cited

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…

q-bio.NC2025

Rethinking the Simulation vs. Rendering Dichotomy: No Free Lunch in Spatial World Modelling

Dezhi Luo, Qingying Gao, Hokin Deng

Spatial world models, representations that support flexible reasoning about spatial relations, are central to developing computational models that could operate in the physical wor…

cs.CV2025

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…

cs.AI2025

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…

cs.CV2025

Probing Perceptual Constancy in Large Vision-Language Models

Haoran Sun, Bingyang Wang, Suyang Yu +14

Perceptual constancy is the ability to maintain stable perceptions of objects despite changes in sensory input, such as variations in distance, angle, or lighting. This ability is…

cs.CL2024

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…

cs.AI2024

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…

cs.AI2024★ 2 cited

Vision Language Models See What You Want but not What You See

Qingying Gao, Yijiang Li, Haiyun Lyu +3

Knowing others' intentions and taking others' perspectives are two core components of human intelligence that are considered to be instantiations of theory-of-mind. Infiltrating ma…

cs.AI2024

Probing Mechanical Reasoning in Large Vision Language Models

Haoran Sun, Qingying Gao, Haiyun Lyu +3

Mechanical reasoning is a hallmark of human intelligence, defined by its ubiquitous yet irreplaceable role in human activities ranging from routine tasks to civil engineering. Embe…