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

2 citations · 3 across the 16 of their papers we have counts for

collaborators

17 papers

cs.AI2026

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…

cs.AI2026

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…

cs.CL2026

CocoaBench: Evaluating Unified Digital Agents in the Wild

CocoaBench Team, Shibo Hao, Zhining Zhang +29

LLM agents now perform strongly in software engineering, deep research, GUI automation, and various other applications, while recent agent scaffolds and models are increasingly int…

cs.CV2026

EgoForge: Goal-Directed Egocentric World Simulator

Yifan Shen, Jiateng Liu, Xinzhuo Li +9

Generative world models have shown promise for simulating dynamic environments, yet egocentric video remains challenging due to rapid viewpoint changes, frequent hand-object intera…

cs.CV2026

ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model

Haichao Zhang, Yijiang Li, Shwai He +5

Recent progress in latent world models (e.g., V-JEPA2) has shown promising capability in forecasting future world states from video observations. Nevertheless, dense prediction fro…

cs.AI2026

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…

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…

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.NE2025

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…

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.AI2025

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…

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…