most citedStructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs

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cs.CV2026

AVA-Encoder: Towards Agent-Native Video Representation Learning

Chuyue Li, Jinpeng Yu, Haozhe Wang +8

Video creative agents still lack an effective way to learn from high-quality human films, limiting their ability to produce cinematic-grade videos. A key challenge is the absence o…

cs.CV2026

Rethinking Classifier-Free Guidance in On-Policy Diffusion Distillation

Bingnan Li, Haozhe Wang, Haozhong Xiong +5

On-policy distillation (OPD) adapts diffusion models by querying a teacher along trajectories generated by the current student, but how it should behave under classifier-free guida…

cs.CV2026

Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation

Haozhe Wang, Weijia Feng, Jinpeng Yu +8

Visual generators excel at rendering, but they confidently fabricate what they do not know. User requests are unbounded, evolving, and deeply long-tailed: new characters, trending…

cs.CV2026

Starve to Perceive: Taming Lazy Perception in VLMs with Constrained Visual Bandwidth

Yuhuan Wu, Cong Wei, Fangzhen Lin +2

Vision-Language Models (VLMs) deployed as situated agents in high-resolution visual environments require active perception -- the ability to dynamically decide where to look throug…

cs.CV2025

VideoScore2: Think before You Score in Generative Video Evaluation

Xuan He, Dongfu Jiang, Ping Nie +21

Recent advances in text-to-video generation have produced increasingly realistic and diverse content, yet evaluating such videos remains a fundamental challenge due to their multi-…