arXiv AI
MAIA: Multi-Agent Intent Articulation for Requirement Discovery in Art Commissions
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ArtSociety: Multi-Agent Multimodal Collaboration for Art Emotion Understanding
arXiv:2609.13240v1 Announce Type: new Abstract: The AffectiveArt Multidimensional Art Emotion Understanding task asks to jointly predict an artwork's fine-grained emotion (12 classes, 1549:1 head-to-...
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ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation
arXiv:2608. 05026v1 Announce Type: cross Abstract: High-quality annotation of artworks is essential for computational art research, yet extracting implicit semantics remains challenging due to the reliance on culturally grounded meanings and deep contextual knowledge behind the images.
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From Vibe to Code -- and Back: Lexical Oscillation in the Formation of Design Intent with Generative AI
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Creativity Reconsidered: Generative AI and the Problem of Intentional Agency
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Do Multimodal Agents Really Benefit from Tool Use? A Systematic Study of Capability Gains
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arXiv Machine Learning
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How Far Can Coding Agents Go in Visual Generation?
The paper investigates general‑purpose coding agents that generate images by writing and executing programs, contrasting them with conventional generative models. On the AgentDrawBench benchmark, these agents excel at preserving structure and meeting precise visual constraints, achieving competitive performance in tasks where compositional accuracy outweighs photorealism, though they fall short in perceptual richness. The study further shows that the agents’ visual code can serve as an external language of thought, retaining substantial information for visual question answering and suggesting an internal, compositionally structured representation that guides visual operations across different drawing interfaces.