mlsanityData to 7 Oct 2026

ViFA-Council: Multi-Agent LLM Deliberation for Vietnamese Folk Art Generation

arXiv 2609.13348 ↗First posted 11 Jul 2026cs.GR cs.LGcited by 0

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

This paper presents ViFA-Council, a three-stage multi-agent framework that employs multiple large language models (LLMs) to tackle two culturally complex generative tasks: image outpainting and educational story generation based on traditional Vietnamese folk paintings. Current single-model generative pipelines frequently struggle with stylistic hallucinations and cultural misrepresentations because they lack mechanisms for cross-model critique. ViFA-Council addresses this challenge by orchestrating collaboration among GPT-4o, Gemini 3.1 Pro, and Claude Sonnet 4.6. It enforces rigorous cultural constraints through structured agent deliberation. This deliberation is mediated by task-specific JSON schemas that effectively bridge natural language discussions with diffusion-based image synthesis using Banana Pro. Experiments and a user study demonstrate that structured multi-agent deliberation is a promising direction for improving cultural fidelity and narrative coherence in culturally sensitive, low-resource artistic domains. The source code and data are released at https://github.com/DanielNguyen-05/ViFA-Council.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 10 of its 14 references that are in the map. First posted on arXiv 11 Jul 2026.

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10 of 14 references.

References · 10

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
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  7. 2023Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generationcited by 950
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  10. 2023DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Datacited by 322

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