MAVEN A Multi-Agent Framework for Multicultural Text-to-Video Generation

2025-10-10 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

MAVEN is a multi-agent prompt refinement framework designed to enhance cultural fidelity in text-to-video (T2V) generation, particularly for mono-cultural and cross-cultural scenarios. Developed by Santa Clara University, MAVEN decomposes prompts into person, action, and location dimensions, handled by specialized agents operating in parallel or sequentially. The framework introduces a new benchmark of 243 culturally grounded prompts and 972 corresponding videos, spanning Chinese, American, and Romanian cultures across three action categories. Evaluations using CLIP-based metrics, VLM-as-judge assessments (Gemini 2.5 Pro), and video quality measures demonstrate that multi-agent refinement, especially parallel specialization (MAP), significantly improves cultural relevance and temporal consistency while preserving visual quality. The dataset and code are available at https://github.com/AIM-SCU/CRAFT.

Key takeaway

For AI Scientists and Machine Learning Engineers developing text-to-video systems, you should implement multi-agent prompt refinement to address cultural representation gaps. Your models can achieve significantly higher cultural relevance and temporal consistency, especially in cross-cultural scenarios. Decompose prompts into specialized cultural dimensions and use parallel agent coordination. This approach improves fidelity without sacrificing visual quality, making your generative video outputs more inclusive and accurate.

Key insights

Multi-agent prompt decomposition significantly improves cultural fidelity in text-to-video generation, especially for cross-cultural scenarios.

Principles

• Cultural fidelity is a structural problem, not a scaling problem.
• Parallel specialization outperforms sequential or single-agent refinement.
• Automatic metrics underestimate nuanced cultural improvements.

Method

MAVEN decomposes T2V prompts into person, action, and location dimensions, assigning each to a culturally specialized agent. These agents refine prompts in parallel or sequentially before feeding them to a fixed T2V model like CogVideoX-5B.

In practice

• Decompose complex T2V prompts into cultural dimensions.
• Use specialized agents for person, action, and location refinement.
• Prioritize parallel agent coordination for balanced cultural enrichment.

Topics

• Text-to-Video Generation
• Multi-Agent Systems
• Cultural Fidelity
• Prompt Engineering
• CogVideoX-5B
• Generative AI Evaluation
• Cross-Cultural AI

Code references

• AIM-SCU/CRAFT
• THUDM/CogVideo

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.