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cs.CV2026

Are Text-to-Image Models Inductivist Turkeys? A Counterfactual Benchmark for Causal Reasoning

Jiayi Lei, Yuandong Pu, Xingyu Han +8

Text-to-image (T2I) generation models have achieved remarkable progress in producing visually realistic images from natural language prompts. Yet it remains unclear whether their s…

cs.CV2026

Motion-Aware Caching for Efficient Autoregressive Video Generation

Jing Xu, Yuexiao Ma, Xuzhe Zheng +7

Autoregressive video generation paradigms offer theoretical promise for long video synthesis, yet their practical deployment is hindered by the computational burden of sequential i…

cs.CV2026

MathGen: Revealing the Illusion of Mathematical Competence through Text-to-Image Generation

Ruiyao Liu, Hui Shen, Ping Zhang +16

Modern generative models have demonstrated the ability to solve challenging mathematical problems. In many real-world settings, however, mathematical solutions must be expressed vi…

cs.CV2026

Flow caching for autoregressive video generation

Yuexiao Ma, Xuzhe Zheng, Jing Xu +9

Autoregressive models, often built on Transformer architectures, represent a powerful paradigm for generating ultra-long videos by synthesizing content in sequential chunks. Howeve…

cs.CV2025

Perceptual Quality Assessment of 3D Gaussian Splatting: A Subjective Dataset and Prediction Metric

Zhaolin Wan, Yining Diao, Jingqi Xu +5

With the rapid advancement of 3D visualization, 3D Gaussian Splatting (3DGS) has emerged as a leading technique for real-time, high-fidelity rendering. While prior research has emp…

cs.CV2025

QuadGPT: Native Quadrilateral Mesh Generation with Autoregressive Models

Jian Liu, Chunshi Wang, Song Guo +9

The generation of quadrilateral-dominant meshes is a cornerstone of professional 3D content creation. However, existing generative models generate quad meshes by first generating t…