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most citedScience-T2I: Addressing Scientific Illusions in Image Synthesis

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cs.CV2026

An Unexpected Robot Policy: Early Evaluations of GPT-6 Astra on RoboDojo and Beyond

Wenbo Zhang, Kaixuan Wang, Yutao Ouyang +9

Embodied AI systems are often organized into System 1 and System 2. System 1 is typically a pretrained policy that generates actions at high frequency, whereas System 2 is often in…

cs.CV2026

UEval: A Benchmark for Unified Multimodal Generation

Bo Li, Yida Yin, Wenhao Chai +2

We introduce UEval, a benchmark to evaluate unified models, i.e., models capable of generating both images and text. UEval comprises 1,000 expert-curated questions that require bot…

cs.CV2025

Next-Embedding Prediction Makes Strong Vision Learners

Sihan Xu, Ziqiao Ma, Wenhao Chai +5

Inspired by the success of generative pretraining in natural language, we ask whether the same principles can yield strong self-supervised visual learners. Instead of training mode…

cs.CV2025

VideoNSA: Native Sparse Attention Scales Video Understanding

Enxin Song, Wenhao Chai, Shusheng Yang +5

Video understanding in multimodal language models remains limited by context length: models often miss key transition frames and struggle to maintain coherence across long time sca…

cs.CV2025

Dense Video Understanding with Gated Residual Tokenization

Haichao Zhang, Wenhao Chai, Shwai He +2

High temporal resolution is essential for capturing fine-grained details in video understanding. However, current video large language models (VLLMs) and benchmarks mostly rely on…

cs.CV2025★ 1 cited

Science-T2I: Addressing Scientific Illusions in Image Synthesis

Jialuo Li, Wenhao Chai, Xingyu Fu +2

Current image generation models produce visually compelling but scientifically implausible images, exposing a fundamental gap between visual fidelity and physical realism. In this…