Title placeholder

Abstract placeholder

Share:Twitter | Facebook | LinkedIn

Summary:

• 🚀 New paper: A-SEA3L-QA introduces a self-evolving adversarial workflow for Arabic long-context QA generation. It leverages multiple LVLMs in an end-to-end, automated pipeline to improve performance without human intervention. https://arxiv.org/pdf/2509.02864″
• 💡 Key insight: The system enables continuous learning by iteratively refining outputs and enhancing question difficulty. This approach significantly boosts long-context comprehension capabilities of Arabic LVLMs.
• 🤖 A-SEA3L-QA also provides a large-scale benchmark (AraLongBench) to evaluate Arabic QA models, exposing weaknesses in current systems. This is a major step forward for low-resource language NLP.

Resources:

AraLongBench (benchmark dataset)

Code:

https://github.com/wangk0b/Self_Improving_ARA_LONG_Doc.git

Link:

https://arxiv.org/pdf/2509.02864