Researchers at the American University of Beirut (AUB) have released AraBERT, a BERT model pre-trained specifically for Arabic language understanding. The model was trained on a large Arabic corpus and compared against multilingual BERT and other state-of-the-art methods. AraBERT achieved state-of-the-art performance on several tested Arabic NLP tasks including sentiment analysis, named entity recognition, and question answering. Why it matters: This release provides the Arabic NLP community with a high-performing, open-source language model, facilitating further research and development.
Researchers from MBZUAI have developed MMRINet, a Mamba-based neural network for efficient brain tumor segmentation in MRI scans. The model uses Dual-Path Feature Refinement and Progressive Feature Aggregation to achieve high accuracy with only 2.5M parameters, making it suitable for low-resource clinical environments. MMRINet achieves a Dice score of 0.752 and HD95 of 12.23 on the BraTS-Lighthouse SSA 2025 benchmark.
Researchers have developed MARTS (Modular Agricultural Robot for Targeted Spraying), a low-cost robotic platform designed for precise disease control in greenhouse tomato production. MARTS integrates a mobile base, a UR5 manipulator, a modular spraying end-effector, and an Intel RealSense RGB-D camera, utilizing a vision-language model for disease detection. Deployed at the SILAL commercial greenhouse in Al Ain, UAE, the robot successfully treated 312 diseased leaves across 12 trials, achieving 92.4% targeting success and a 71% reduction in pesticide volume compared to broadcast spraying. The spraying subsystem costs significantly less than comparable systems, demonstrating the feasibility of accurate leaf-level targeting with commodity hardware. Why it matters: This innovation provides a precise, cost-effective, and environmentally friendly solution for agricultural disease management, reducing pesticide use and worker exposure, with successful application and cost efficiency demonstrated in the UAE.
Researchers at MBZUAI have introduced QRAFT, an LLM-based framework designed to automate the generation of fact-checking articles. The system mimics the writing workflow of human fact-checkers, aiming to bridge the gap between automated fact-checking systems and public dissemination. While QRAFT outperforms existing text-generation methods, it still falls short of expert-written articles, highlighting areas for further research.
MBZUAI hosted a delegation from the Commonwealth of Poland, including representatives from the Polish Embassy and Chancellery of the Prime Minister. Discussions covered MBZUAI's objectives, activities, and potential collaborations with Polish entities. The visit included a campus tour and presentation. Why it matters: This engagement indicates MBZUAI's ongoing efforts to build international partnerships and expand its global reach in AI research and education.
KAUST Professor Marc Genton has received the Royal Statistical Society’s (RSS) 2023 Barnett Award for his contributions to environmental statistics. Genton's work includes the development of ExaGeoStat, a high-performance software for geostatistics, and the use of spectral methods to emulate climate model outputs. His research includes a five-year study on wind energy potential in Saudi Arabia, informing the Kingdom’s national wind energy strategy. Why it matters: This award recognizes impactful environmental statistics research at KAUST with implications for Saudi Arabia's renewable energy sector and beyond.
A new benchmark, LongShOTBench, is introduced for evaluating multimodal reasoning and tool use in long videos, featuring open-ended questions and diagnostic rubrics. The benchmark addresses the limitations of existing datasets by combining temporal length and multimodal richness, using human-validated samples. LongShOTAgent, an agentic system, is also presented for analyzing long videos, with both the benchmark and agent demonstrating the challenges faced by state-of-the-art MLLMs.