The paper introduces ALLaM, a series of large language models for Arabic and English, designed to support Arabic Language Technologies. The models are trained with language alignment and knowledge transfer in mind, using a decoder-only architecture. ALLaM achieves state-of-the-art results on Arabic benchmarks like MMLU Arabic and Arabic Exams. Why it matters: This work advances Arabic NLP by providing high-performing LLMs and demonstrating effective techniques for cross-lingual transfer learning and alignment with human preferences.
This paper presents a UI-level evaluation of ALLaM-34B, an Arabic-centric LLM developed by SDAIA and deployed in the HUMAIN Chat service. The evaluation used a prompt pack spanning various Arabic dialects, code-switching, reasoning, and safety, with outputs scored by frontier LLM judges. Results indicate strong performance in generation, code-switching, MSA handling, reasoning, and improved dialect fidelity, positioning ALLaM-34B as a robust Arabic LLM suitable for real-world use.
KAUST spin-out company NOMADD, which specializes in robotic PV cleaning systems, has secured a Series B investment from Saudi construction company CEPCO. The investment will support NOMADD's project pipeline and growth ambitions, enabling them to scale operations and serve more customers. CEPCO will also advise on technology development and local manufacturing in Saudi Arabia. Why it matters: This investment validates KAUST's innovation fund strategy and supports the deployment of sustainable energy solutions in the region, leveraging local expertise and manufacturing.
TAQADAM, a KAUST-based accelerator program, awarded $1 million to 10 startups at its eighth annual showcase. To date, TAQADAM has supported 270 startups that have raised $297 million and created 3,569 jobs. The selected companies span climate monitoring, pharmaceutical supply chain, AI marketing, and healthcare. Why it matters: This funding and support highlights the growing entrepreneurial ecosystem in Saudi Arabia, fostered by KAUST and initiatives like TAQADAM, aimed at translating research into practical solutions and creating jobs.
Researchers from MBZUAI have introduced VideoMolmo, a large multimodal model for spatio-temporal pointing conditioned on textual descriptions. The model incorporates a temporal module with an attention mechanism and a temporal mask fusion pipeline using SAM2 for improved coherence across video sequences. They also curated a dataset of 72k video-caption pairs and introduced VPoS-Bench, a benchmark for evaluating generalization across real-world scenarios, with code and models publicly available.