VidUEU-Agent: A Data-Curation Agent for Multimodal Understanding, Editing, and Unified Tasks

Tengju Ru, Weitong Lian, Zecong Tang, Lingyi Meng, Haoran Li, Zhejun Cui, Yichen Zhu, Hangshuo Cao, Qi Kang, Yechi Liu, Kaixuan Wang, Yu-Jie Yuan, Chunwei Wang, Yu Zhang, Bo Dai

VidUEU-Agent: A Data-Curation Agent for Multimodal Understanding, Editing, and Unified Tasks: 23 upvotes on Hugging Face Daily Papers, #12 of 46 papers on 2026-11-04. Day-by-day upvote history.

Upvotes over time

It had 23 upvotes by its first day, 23 upvotes by the next day, 23 upvotes by day 3, 23 upvotes by day 7, and has 23 now. It ranked #36 of 130 papers in its week and #36 of 130 in its month.

The advancement of multimodal learning is heavily constrained by the scarcity and high annotation cost of high-quality instruction data. While raw videos offer an abundant and dynamic source of visual knowledge, transforming them into precise instruction data remains highly challenging. To bridge this gap, we present VidUEU-Agent, an automated pipeline that synthesizes instruction data from raw videos for multimodal understanding, editing, and unified tasks. VidUEU-Agent follows a four-stage pipeline: it performs global perception to filter low-quality shots, mines keyframes and multimodal signals, constructs metadata and routes the metadata to synthesize instruction samples. More importantly, our agent framework can autonomously iterate the data distribution based on downstream training results, thereby optimizing data quality, balance, and training effectiveness. Scaling this pipeline, we construct VidUEU-100K, a large-scale dataset of 100K training samples spanning multiple domains and tasks, providing a robust foundation for developing comprehensive multimodal capabilities. Extensive experiments demonstrate that fine-tuning baseline models on VidUEU-100K yields significant and consistent performance gains across diverse representative benchmarks, validating the effectiveness of our proposed agent framework and dataset.

Paper page on Hugging Face · arXiv

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