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arXiv · 2609.13348

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

Hai-Dang Nguyen·Minh-Phuong Pham·Thao Thi Phuong Dao·Trong-Le Do·Vinh-Tiep Nguyen·Trung-Nghia Le
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Abstract

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.

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Hai-Dang Nguyen, Minh-Phuong Pham, Thao Thi Phuong Dao, Trong-Le Do, Vinh-Tiep Nguyen, Trung-Nghia Le. 2026-07-11. ViFA-Council: Multi-Agent LLM Deliberation for Vietnamese Folk Art Generation. https://arxiv.org/abs/2609.13348

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