arXiv AI
Jun 30
The Human Creativity Benchmark
arXiv:2606. 30561v1 Announce Type: new Abstract: Modern AI evaluation frameworks treat evaluator disagreement as noise to be resolved.
Seeing Differently: Modeling Interpretive Perspectives in Computational Creativity using a Four-World Framework
arXiv:2607. 28644v1 Announce Type: cross Abstract: Creativity in computational systems is often evaluated as an objective property of artifacts, with existing Computational Creativity (CC) frameworks assessing creative merit at the level of outputs or systems rather than interpretive context.
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arXiv AI
Jul 3
CreativityPrism: A Cross-Domain Evaluation Framework for Large Language Model Creativity
arXiv:2510. 20091v3 Announce Type: replace-cross Abstract: Creativity is often seen as a hallmark of human intelligence.
Hugging Face Trending Papers
Jun 8
Introducing multiplex semantic networks as multifaceted representations of creative associative knowledge across multilingual samples
Creativity is a complex cognitive ability that relies on knowledge organisation and retrieval from semantic memory. Yet most research uses a single task to measure it, capturing only a fraction of this complexity.
arXiv AI
Aug 5
Assessing the Effect of Cross-Domain Mapping on Creativity in Humans and Large Language Models
arXiv:2603. 19087v2 Announce Type: replace Abstract: Creativity is the ability to come up with novel ideas, a capacity crucial for human development and flourishing.
arXiv AI
Jun 11
Automated Creativity Evaluation of Language Models Across Open-Ended Tasks
arXiv:2606. 11762v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential.
arXiv AI
Aug 11
Harnessing Abundance: A Generativity Perspective on Human-GenAI Collaboration
arXiv:2608. 07500v1 Announce Type: cross Abstract: Research on human-GenAI collaboration yields conflicting findings: GenAI can enhance creativity yet reduce collective diversity, with uneven benefits across skill levels.
arXiv AI
2d ago
From Style Replication to Style Exploration: Enabling Art Style Exploration with Analyze-Experiment-Resituate Framework
arXiv:2608. 14405v1 Announce Type: cross Abstract: Art style is a signature of professional digital artists that develops through repeated experimentation, reflection, and adaptation.
Hugging Face Trending Papers
Aug 6
Learning visual representations for compositional analysis of artworks and photographs
Composition, the deliberate arrangement of visual elements, is central to how meaning, emotion, and aesthetic quality are conveyed in artwork, yet it remains among the least formalized dimensions of visual understanding. Prior work highlights a persistent gap in learning meaningful compositional representations, attributing it to semantic bias and suggesting that human-inspired approaches may be key.
Hugging Face Trending Papers
Jun 10
Automated Creativity Evaluation of Language Models Across Open-Ended Tasks
Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential. Realizing this potential requires systematic and scalable methods for evaluating creativity across diverse tasks.
arXiv AI
Jul 28
From Vibe to Code -- and Back: Lexical Oscillation in the Formation of Design Intent with Generative AI
arXiv:2607. 23126v1 Announce Type: cross Abstract: Generative AI design tools make natural-language prompts a starting point for design, placing new articulation demands on designers.
Hugging Face Trending Papers
Jul 22
Back to Back with a Copy: A Computational Analysis of AI-Generated Visual Contemporary Art Pastiches
The aim of this paper is twofold. First, it investigates whether newer generative models are getting better at pastiching contemporary artworks.
arXiv AI
Aug 10
Can MLLMs Decode the Creative Leap? Introducing C4 for Cross-Concept Understanding
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.