arXiv AI By Ming Wang, Yuqing Zhang, Tingna Xie, Xiangju Li, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang

Can MLLMs Decode the Creative Leap? Introducing C4 for Cross-Concept Understanding

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arXiv:2608. 06501v1 Announce Type: new Abstract: Creative capabilities of MLLMs matter in design, communication, education, and human--AI collaboration, yet remain difficult to evaluate because explicit targets and reward signals are scarce compared with accuracy-oriented tasks.

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arXiv AI
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CresOWLve: Benchmarking Creative Problem-Solving Over Real-World Knowledge

arXiv:2604. 03374v2 Announce Type: replace-cross Abstract: Creative problem-solving requires combining multiple cognitive abilities, including logical reasoning, lateral thinking, analogy-making, and commonsense knowledge, to discover insights that connect seemingly unrelated pieces of information.

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MuseBench: Benchmarking Intent-Level Audiovisual Arts Understanding in MLLMs

arXiv:2606. 30026v1 Announce Type: cross Abstract: Audiovisual arts encompass diverse creative disciplines, including cinema, visual arts, stage performance, and game design, where artistic meaning arises from deliberate combinations of visual, auditory, and narrative elements (e.

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