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Paper · arXiv

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

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. Cross-concept understanding is a core cognitive capacity underlying receptive creativity. It enables a perceiver to recover intended meaning from non-obvious but meaningful conceptual relations. We operationalize item construction as cross-concept encoding and model inference as cross-concept decoding. We introduce C4, a cognition-inspired evaluation framework for Chengyu (Chinese idiom)-based Cross-Concept Creativity. Its encoding component maps target slots to...

AuthorsMing Wang, Yuqing Zhang, Tingna Xie et al.
Primary categorycs.AI
Published6 August 2026
Last updated at source6 August 2026
cs.AIcs.CLcs.MM
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