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cs.CV2026

VIEWCRAFT: Evolving What Multimodal Agents See and How They Read It

Binxiao Xu, Ruichuan An, Bocheng Zou +7

Reusable skills improve multimodal agents without updating model weights, but their effectiveness depends on both the evidence presented and the procedure used to read it. We intro…

cs.CV2026

Agent Skills Should Go Beyond Text: The Case for Visual Skills

Binxiao Xu, Ruichuan An, Bocheng Zou +1

Reusable skills are a key mechanism for extending agent capabilities, allowing agents to accumulate experience and solve increasingly complex tasks. Yet most existing skill-learnin…

cs.CV2026

OpenWorldLib: A Unified Codebase and Definition of Advanced World Models

DataFlow Team, Bohan Zeng, Daili Hua +39

World models have garnered significant attention as a promising research direction in artificial intelligence, yet a clear and unified definition remains lacking. In this paper, we…

cs.CV2026

PEARL: Personalized Streaming Video Understanding Model

Yuanhong Zheng, Ruichuan An, Xiaopeng Lin +10

Human cognition of new concepts is inherently a streaming process: we continuously recognize new objects or identities and update our memories over time. However, current multimoda…

cs.CV2026

MME-CoF-Pro: Evaluating Reasoning Coherence in Video Generative Models with Text and Visual Hints

Yu Qi, Xinyi Xu, Ziyu Guo +10

Video generative models show emerging reasoning behaviors. It is essential to ensure that generated events remain causally consistent across frames for reliable deployment, a prope…

cs.CV2026

How Well Do Models Follow Visual Instructions? VIBE: A Systematic Benchmark for Visual Instruction-Driven Image Editing

Huanyu Zhang, Xuehai Bai, Chengzu Li +9

Recent generative models have achieved remarkable progress in image editing. However, existing systems and benchmarks remain largely text-guided. In contrast, human communication i…