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From the 1 of 17 linked papers with an AI index.

most citedEgocentric Bias in Vision-Language Models

1 citations · 1 across the 2 of their papers we have counts for

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17 papers

cs.CV20261 cited

Egocentric Bias in Vision-Language Models

Maijunxian Wang, Yijiang Li, Bingyang Wang +6

The paper introduces FlipSet, a benchmark that tests vision‑language models on Level‑2 visual perspective taking by requiring them to mentally rotate 2D character strings, and find…

cs.AI2026

Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation

Yifan Zhou, Qihao Yang, Yan Li +14

Scientific ideas rarely start from a blank page. They inherit mechanisms, repair known limitations, and recombine pieces of earlier work, much like biological genomes. Current benc…

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

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.CV2026

Demystifying Video Reasoning

Ruisi Wang, Zhongang Cai, Fanyi Pu +11

Recent advances in video generation have revealed an unexpected phenomenon: diffusion-based video models exhibit non-trivial reasoning capabilities. Prior work attributes this to a…

cs.CV2026

PhyGround: Benchmarking Physical Reasoning in Generative World Models

Juyi Lin, Arash Akbari, Yumei He +13

Generative world models are increasingly used for video generation, where learned simulators are expected to capture the physical rules that govern real-world dynamics. However, ev…