arXiv Machine Learning
ViFA-Council: Multi-Agent LLM Deliberation for Vietnamese Folk Art Generation
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Jun 6
MAVEN A Multi-Agent Framework for Multicultural Text-to-Video Generation
arXiv:2605. 16716v4 Announce Type: replace-cross Abstract: Text-to-video (T2V) generation has rapidly progressed in visual fidelity, yet its ability to faithfully represent multiple cultures within a single prompt remains underexplored.
arXiv AI
Aug 20
Sanyu Studio: A Multi-Agent System for Art-Historical Narrative Construction
Sanyu Studio is a multi‑agent dialogue system that treats 321 Sanyu oil paintings as agents equipped with fact, interpretation, organization, and memory‑filtering mechanisms. The paper reports on a seven‑day workshop with eight art‑university participants, showing that user prompts, evidence organization, and cognitive tendencies produced divergent yet coherent digital narratives of Sanyu. The study suggests that, when historical evidence is limited, AI can amplify human agency and provide public audiences with an interactive entry point into art‑historical interpretation.
arXiv AI
Jul 21
When Cultures Move: Measuring and Improving Multicultural Text-to-Video Generation
arXiv:2605. 16716v5 Announce Type: replace-cross Abstract: Text-to-video (T2V) generation has rapidly progressed in visual fidelity, yet its ability to faithfully represent multiple cultures within a single prompt remains underexplored.
arXiv AI
Jul 13
Fictional Worldbuilding: Multi-Agent LLM Collaboration with Hierarchical Context Compression and Iterative Review
arXiv:2607. 09403v1 Announce Type: new Abstract: Worldbuilding, the construction of coherent fictional worlds, is a foundational task in game design and literary creation.
Hugging Face Trending Papers
Aug 31
CM2: Multimodal Cultural Reasoning via an Integrated Multi-Agent Framework
Multimodal Large Language Models (MLLMs) have shown remarkable success in STEM domains, where progress is often driven by vertical, step-by-step deduction under relatively stable symbol systems. Their...
arXiv AI
Aug 6
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.