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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

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8 papers · 1 filter

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…