TII and LightOn have partnered to build the NOOR Platform for exascale computing, aimed at developing foundation models. The collaboration will leverage LightOn's expertise in large language models, with the first output being the largest Arabic language model to date. The platform will provide high-quality data pipelines and facilitate extreme-scale distributed training and serving. Why it matters: This partnership aims to establish Abu Dhabi as a center of AI excellence and boost the UAE's ambitions in high-tech innovation and NLP research.
MBZUAI's Institute of Foundation Models (IFM) has released K2 Think V2, a 70 billion parameter open-source general reasoning model built on K2 V2 Instruct. The model excels in complex reasoning benchmarks like AIME2025 and GPQA-Diamond, and features a low hallucination rate with long context reasoning capabilities. K2 Think V2 is fully sovereign and open, from pre-training through post-training, using IFM-curated data and a Guru dataset. Why it matters: This release contributes to closing the gap between community-owned reproducible AI and proprietary models, particularly in reasoning and long-context understanding for Arabic NLP tasks.
MBZUAI's Institute of Foundation Models has released K2, a 70-billion-parameter, reasoning-centric foundation model. K2 is designed to be fully inspectable, with open weights, training code, data composition, mid-training checkpoints, and evaluation harnesses. K2 outperforms Qwen2.5-72B and approaches the performance of Qwen3-235B. Why it matters: This release promotes transparency and reproducibility in AI development, providing researchers with the resources needed to study, adapt, and build upon a strong foundation model.
MBZUAI researchers at the Institute of Foundation Models (IFM) investigated the role of reinforcement learning (RL) in improving reasoning abilities of language models. Their study found that RL acts as an 'elicitor' for reasoning in domains frequently encountered during pre-training (e.g., math, coding), while genuinely teaching new reasoning skills in underrepresented domains (e.g., logic, simulations). To support their analysis, they created a new dataset called GURU containing 92,000 examples across six domains. Why it matters: This research clarifies the impact of reinforcement learning on language model reasoning, paving the way for developing models with more generalizable reasoning abilities across diverse domains, an important direction for more capable AI systems.
MBZUAI's Institute of Foundation Models (IFM) has developed PAN, a novel interactive world model capable of long-horizon simulation. PAN uses a Generative Latent Prediction (GLP) architecture, coupling internal latent reasoning with generative supervision in the visual domain. The model evolves an internal latent state conditioned on history and natural language actions, then decodes that state into a video segment using a Causal Swin-DPM mechanism for smooth transitions. Why it matters: PAN represents a significant advancement in AI's ability to simulate and predict evolving environments, enabling more steerable and coherent long-term video generation and opening new possibilities for interactive AI systems.
MBZUAI researchers Darya Taratynova and Shahad Hardan developed Forget-MI, a method for making clinical AI models "unlearn" specific patient data without retraining the entire model. Forget-MI addresses the challenge of removing patient data from AI models trained on multimodal records (like chest X-rays and reports) due to regulations like GDPR and HIPAA. The method unlearns both unimodal (image or text) and joint (image-text) associations while retaining overall accuracy using a late-fusion multimodal classifier. Why it matters: This research provides a practical solution to a critical privacy concern in healthcare AI, enabling compliance with data protection regulations and fostering trust in AI-driven medical applications.
MBZUAI has launched the Institute of Foundation Models (IFM) with a new Silicon Valley Lab in Sunnyvale, CA, joining existing facilities in Paris and Abu Dhabi. The launch event showcased PAN, a world model for simulating diverse realities with multimodal inputs. The IFM lab is also advancing K2-65B and JAIS AI systems. Why it matters: This expansion enhances MBZUAI's global presence and connects it with a critical AI ecosystem, supporting the UAE's economic diversification through advanced AI technologies.
MBZUAI and École Polytechnique are deepening research collaboration through a Collaborative Research Agreement, focusing on large language models, foundation models for reasoning, and AI applications in biology, health, and AI safety. The partnership builds on a previous MoU and Scholars Exchange Program Agreement between the two institutions. MBZUAI's France Lab has developed Atlas-Chat, a family of open-source LLMs for the Moroccan Arabic dialect Darija, with models including Atlas-Chat-2B and Atlas-Chat-9B. Why it matters: This collaboration strengthens the AI ecosystems in both France and the UAE, fostering joint research efforts and supporting the next generation of AI researchers and innovators, with a specific focus on Arabic NLP.