General AI research publications from MBZUAI, KAUST, TII, QCRI, and other GCC institutions, including conference papers, journal articles, and preprints.
Researchers from MBZUAI, Khalifa University, and Sorbonne University Abu Dhabi developed H-SURF, a system of underwater robotic fish that can swim, communicate, and gather information without human guidance. The robotic fish use bioinspired robotics with streamlined bodies, fins, and propellers to produce fluid movement. They communicate with each other using light instead of sound to reduce noise. Why it matters: This award-winning system represents a significant advancement in autonomous underwater robotics, offering a less intrusive way to monitor marine environments and gather data, with potential applications in marine biology and environmental research.
An MBZUAI team led by Thamar Solorio and Monojit Choudhury received a Google Academic Research Award to study how AI can better understand and respond to human loneliness in digital spaces. The project will examine how loneliness is expressed online, how conversational agents can detect it, and what healthier AI companionship could look like in collaboration with Georgia Tech. The team aims to define digital loneliness and its expression in online conversations with AI. Why it matters: This research addresses a growing global issue by exploring the ethical and psychological implications of AI companionship, potentially leading to safer and more beneficial AI interactions.
MBZUAI has launched the Ruwwad AI Scholars (RAIS) program, a fully funded two-year postdoctoral fellowship for Emirati Ph.D. graduates to conduct research at leading global institutions. The fellowship covers a competitive stipend, research funds, insurance, relocation support, and conference travel, with no cost to host institutions. Completion aims to strengthen eligibility for tenure-track faculty positions at MBZUAI. Why it matters: This program represents a strategic investment in developing Emirati AI talent and building a globally competitive, homegrown AI faculty to drive the UAE's research ambitions.
MBZUAI faculty and students presented 31 papers at the 2022 Conference on Computer Vision and Pattern Recognition (CVPR), including 6 oral presentations. Professors Fahad Khan and Shijian Lu had 9 and 8 papers accepted respectively. Researchers collaborated with 57 institutions across 16 countries. Why it matters: MBZUAI's strong showing at a top-tier CV conference demonstrates the rapid growth and international collaboration of AI research in the UAE.
MBZUAI researchers have developed a new method called "Byzantine antidote" (Bant) to defend federated learning systems against Byzantine attacks, where malicious nodes intentionally disrupt the training process. Bant uses trust scores and a trial function to dynamically filter out corrupted updates, even when most nodes are compromised. The system can identify poorly labeled data while still training models effectively, addressing both unconscious mistakes and deliberate sabotage. Why it matters: This research enhances the reliability and security of federated learning in sensitive sectors like healthcare and finance, enabling safer collaborative AI development.
MBZUAI researchers are refining AI techniques to improve cancer diagnosis for colorectal and breast cancer, both common in the Middle East. They are using "few-shot tissue image generation," in which AI generates data for training AI models to recognize lesions, addressing the challenge of limited training data. The developed framework improves the efficiency of radiologists in breast cancer diagnosis, leading to better detection of breast lesions and timely treatment interventions. Why it matters: These advancements in AI-aided diagnostics can lead to earlier and more accurate cancer detection, ultimately improving patient outcomes in the region and beyond.
MBZUAI has published 674 papers in 2023 and holds a global ranking of 18 in AI, CV, ML, NLP, and robotics according to CSRankings. The university presented 30 papers at ICCV, will present 53 papers at NeurIPS, and has 44 papers at EMNLP 2023. MBZUAI was also awarded its first patent by the US Patent Office for a system and method for handwriting generation. Why it matters: This demonstrates the rapid growth and increasing prominence of MBZUAI as a leading AI research institution in the region and globally.
MBZUAI researchers have developed an AI program using vision transformers that can learn a person's handwriting style and generate text in that style. The US Patent and Trademark Office recently granted a patent for this technology, which could aid individuals with writing impairments. The system overcomes limitations of previous GAN-based approaches by processing long-range dependencies in handwriting. Why it matters: This patented AI tool enhances personalized text generation and has potential applications in assistive technology and improving handwriting recognition models.
MBZUAI's BiMediX2, a bilingual healthcare multi-modal model, won Meta's Llama Impact Innovation Award for its potential in solving healthcare accessibility challenges across the Middle East and Africa. Built using Llama 3.1, the model understands medical queries in both English and Arabic, interprets medical images, and is integrated as a chatbot on Telegram with speech functionality. The model was also presented at the AI for Sustainable Development Platform Launch Event and integrated into the UNDP for telemedicine. Why it matters: The model's bilingual capabilities and accessibility on low-cost devices and speech-based interaction have potential to improve healthcare access for marginalized populations in the region.
MBZUAI researchers, in collaboration with TUM, developed Open-YOLO 3D, a new method for open-vocabulary 3D instance segmentation. Open-YOLO 3D enables robots to detect and differentiate individual objects in a 3D scene without being limited to predefined object categories, using both camera images and lidar-generated 3D point clouds. The new system was shown to be more accurate and significantly faster than previous approaches. Why it matters: This advancement enhances robots' ability to understand and interact with dynamic, real-world environments, bringing robots closer to being useful in everyday life.
MBZUAI researchers developed LLMVoX, a system enabling LLMs to produce real-time speech, including Arabic. LLMVoX addresses limitations of existing end-to-end and cascaded pipeline approaches, which suffer from either degraded reasoning or latency. LLMVoX was developed as part of Project OMER, which was recently awarded Regional Research Grant from Meta. Why it matters: This enhances the potential of LLMs to function as more natural, multimodal virtual assistants, especially for Arabic-speaking users in the Middle East.
MBZUAI researchers won second place at the AgentX Competition at UC Berkeley for their benchmark measuring AI agents' reasoning across images, comparisons, and video. The Agent-X dataset includes 828 tasks across six domains, requiring agents to use 14 executable tools without explicit instructions. Agent-X analyzes the agent's full reasoning trajectory, unlike typical evaluations that focus only on final answers. Why it matters: The benchmark exposes limitations in current multimodal AI agents and provides a more rigorous evaluation framework for real-world applications in the region and beyond.
MBZUAI researchers developed MedAgentSim, a simulated hospital environment to evaluate AI diagnostic abilities. The simulation uses LLM-powered agents to mimic doctor-patient conversations, providing a dynamic assessment of diagnostic skills. The system includes doctor, patient, and evaluator agents that interact within the simulated hospital, making real-time decisions. Why it matters: This research offers a more realistic evaluation of AI in clinical settings, addressing limitations of current benchmarks and potentially improving AI's use in healthcare.
MBZUAI researchers received high honors at EMNLP 2025 for two research papers, placing them in the top 2% of accepted work. One paper, MAviS, is a multimodal AI system that identifies bird species by combining images, sounds, and text. The other award-winning paper focuses on uncertainty in LLM-as-a-Judge. Why it matters: The recognition highlights MBZUAI's growing influence in NLP and multimodal AI research, particularly in domain-specific applications like biodiversity conservation.
MBZUAI researchers have developed MAviS, a new multimodal dataset, benchmark, and chatbot for fine-grained bird species recognition. MAviS includes images, audio, and text to help models identify subtle differences between species, especially rare and regional varieties. The related study was presented at EMNLP 2025 and selected as a "Senior Area Chair Highlight". Why it matters: This work addresses a key limitation in AI's ability to support biodiversity conservation and ecological monitoring in the region and globally.
Google.org is providing $1 million to MBZUAI to fund a research initiative led by Professor Thamar Solorio focused on addressing the “data divide” in AI for underrepresented languages, especially those in the MENA region. The project aims to create resource-lean AI models tailored to the sociocultural and linguistic realities of MENA, requiring less data and computational power. This initiative will also support the training of postdoctoral and early-career researchers at MBZUAI. Why it matters: The funding will help create AI technologies grounded in the linguistic nuances of the MENA region, rather than adapting Western models, while also democratizing AI development by lowering resource requirements.
MBZUAI, G42, and Cerebras Systems have launched K2 Think V2, a 70-billion parameter reasoning system built on the K2-V2 base model. K2 Think V2 is fully open-source, from pre-training data to post-training alignment, ensuring transparency and reproducibility. It achieves leading results on complex reasoning benchmarks like AIME2025 and GPQA-Diamond. Why it matters: This release marks a significant advancement in the UAE's AI capabilities, demonstrating leadership in building globally accessible and fully sovereign AI systems focused on reasoning.
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 and Minerva Humanoids have launched a two-year research partnership to develop humanoid robotics for safety-critical industrial operations, initially focusing on the energy sector. The collaboration aims to create versatile, reasoning-based systems capable of operating with varying degrees of autonomy. MBZUAI students will be integrated into project teams to develop AI models and robotics systems. Why it matters: The partnership advances AI-driven robotics in safety-critical domains, supports the UAE's ambition to become a global hub, and develops local talent.
MBZUAI has partnered with the World Economic Forum to launch a Centre for the Fourth Industrial Revolution (C4IR) in the UAE. This center will focus on AI, energy transition, and cyber resilience, aiming to advance AI excellence and competitiveness in the region. It will convene leaders across sectors to co-design and pilot initiatives supporting AI research, governance, adoption, and innovation. Why it matters: This partnership positions the UAE and MBZUAI as key players in shaping the global AI landscape and addressing pressing economic, social, and human challenges through AI.
MBZUAI researchers co-led a study published in Nature demonstrating that GluFormer, an AI foundation model trained on continuous glucose monitoring (CGM) data, more accurately predicts long-term diabetes and cardiovascular risk than current clinical standards. GluFormer, built on a transformer architecture and trained using NVIDIA AI infrastructure on over 10 million CGM measurements, forecasts individual health risks using short-term glucose dynamics. In a 12-year follow-up, the model captured 66% of new-onset diabetes cases and 69% of cardiovascular-death events in its highest-risk group, outperforming established CGM-derived metrics across 19 external cohorts. Why it matters: The development of GluFormer represents a significant advancement in personalized healthcare, enabling proactive and individualized health strategies through the analysis of dynamic glucose data.
Researchers from MBZUAI developed "uncertainty quantification heads" (UQ heads) to detect hallucinations in language models by probing internal states and estimating the credibility of generated text. UQ heads leverage attention maps and logits to identify potential hallucinations without altering the model's generation process or relying on external knowledge. The team found that UQ heads achieved state-of-the-art performance in claim-level hallucination detection across different domains and languages. Why it matters: This approach offers a more efficient and accurate method for identifying hallucinations, improving the reliability and trustworthiness of language models in various applications.
MBZUAI faculty member Ekaterina Kochmar and postdoctoral researcher Kaushal Kumar Maurya won a Google Academic Research Award for their research on an intelligent tutoring system. The project, "2σ-ITS," aims to develop an educational foundation model for personalized learning and to support tutors in reaching students with limited access to mainstream education. The Google award provides funding and collaboration opportunities for researchers, with Kochmar and Maurya being the only team from the Middle East to win. Why it matters: This award highlights the growing recognition of AI's potential to improve educational equity and access in the region and beyond.
MBZUAI researchers presented a method for cross-cultural transfer learning to improve language models' understanding of diverse Arab cultures. They used in-context learning and demonstration-based reinforcement (DITTO) to transfer cultural knowledge between countries. Experiments showed up to 34% improvement in performance on cultural understanding benchmarks using only a few demonstrations. Why it matters: This research addresses the gap in cultural understanding of Arabic language models, especially for smaller Arab countries, and provides a novel transfer learning approach.
Researchers from MBZUAI, Carnegie Mellon University, and Meta AI presented a new approach called ThoughtComm at NeurIPS 2025 where AI agents communicate through internal, latent representations instead of natural language. This framework extracts and selectively shares latent "thoughts" from agents' internal states, representing the underlying structure of their reasoning. Results show that agents coordinate more effectively, reach consensus faster, and solve problems more accurately using this method. Why it matters: Bypassing the limitations of natural language in AI communication could lead to more efficient and accurate multi-agent systems, impacting areas like robotics, collaborative AI, and distributed problem-solving.
IFM has released K2-V2, a 70B-class LLM that takes a "360-open" approach by making its weights, data, training details, checkpoints, and fine-tuning recipes publicly available. K2-V2 matches leading open-weight model performance while offering full transparency, contrasting with proprietary and semi-open Chinese models. Independent evaluations show K2 as a high-performance, fully open-source alternative in the AI landscape. Why it matters: K2-V2 provides developers with a transparent and reproducible foundation model, fostering trust and enabling customization without sacrificing performance, which is crucial for sensitive applications in the region.
MBZUAI and AWS have announced a multi-year collaboration to advance AI research, enhance technical skills, and accelerate startup growth in the UAE and wider region. AWS will provide cloud services, mentorship, and access to public datasets, while MBZUAI will contribute faculty expertise and lab capacity. The collaboration includes a strategic research program and the launch of GenAI Academy to develop hackathons for MBZUAI students. Why it matters: This partnership strengthens the UAE's AI ecosystem by bridging academic research with industry application and fostering AI talent development.
MBZUAI has launched the Institute for Agriculture and Artificial Intelligence (IAAI) in collaboration with the UAE Presidential Court and the Gates Foundation. The IAAI will focus on strengthening global food security by providing digital advisory tools to over 43 million smallholder farmers. The institute will develop a new data corpus for agriculture to train AI models and offer localized insights on crops, pests, soils, weather, and markets. Why it matters: This initiative highlights the UAE's commitment to using AI for global good, specifically addressing food security challenges and empowering small-scale farmers through advanced technologies.
Inception, Cerebras, and MBZUAI have released Jais 2, a 70 billion parameter open-weight Arabic LLM. Jais 2 is trained on an Arabic-first dataset and features a redesigned architecture for stronger reasoning and fluency across Arabic dialects and English. It integrates a safety-first framework and demonstrates capabilities in understanding Arabic poetry, culture, and social media tone. Why it matters: Jais 2 addresses the historical underrepresentation of Arabic in AI by providing a culturally and linguistically faithful model, potentially accelerating innovation across the region.
MBZUAI researchers have developed SVRPBench, a new open benchmark for testing vehicle routing algorithms under real-world conditions. SVRPBench simulates unpredictable urban delivery scenarios including rush-hour traffic, accidents, and customer delivery time preferences. The benchmark uses realistic city models with clustered customer locations, unlike existing deterministic benchmarks. Why it matters: This benchmark offers a more practical evaluation for vehicle routing algorithms, potentially leading to significant cost savings and improved efficiency in logistics within the region and beyond.
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 have introduced SURPRISE3D, a benchmark for evaluating 3D spatial reasoning in AI systems, along with a 3D Spatial Reasoning Segmentation (3D-SRS) task. The benchmark includes over 900 indoor scenes and 200,000 language queries paired with 3D masks, emphasizing spatial relationships over object naming. A companion paper, MLLM-For3D, explores adapting 2D multimodal LLMs for 3D reasoning. Why it matters: This work addresses a key limitation in current AI, pushing towards embodied AI that can understand and act in 3D environments based on human-like spatial reasoning.
MBZUAI researchers introduced CausalVerse, a new benchmark for causal representation learning (CRL) presented at NeurIPS 2025. CausalVerse combines high-fidelity visual complexity with access to underlying causal variables and graphs, featuring 200,000 images and 300 million video frames across 24 sub-scenes in four domains. It aims to provide a realistic and precise testbed to evaluate whether CRL methods can truly learn the right causes. Why it matters: By bridging the gap between toy datasets and real-world data, CausalVerse can drive advances in AI systems capable of understanding causality in complex scenarios.
MBZUAI researchers developed a new AI-generated image detection method called 'consistency verification' (ConV). Instead of training on labeled real and fake images, ConV identifies structural patterns unique to real photos using a data manifold concept. The system modifies images and uses DINOv2 to measure the difference between original and transformed representations, classifying images based on their proximity to the manifold. Why it matters: This approach offers a more robust way to detect AI-generated images without needing training data from every image generator, addressing a key limitation in the rapidly evolving landscape of AI image synthesis.
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 researchers created Open CaptchaWorld, a new benchmark to test AI agents on solving CAPTCHAs. The benchmark includes 20 modern CAPTCHA types that require perception, reasoning, and interactive actions within a browser. While humans achieve 93.3% accuracy, the best AI agent only reaches 40% on the benchmark. Why it matters: This research highlights a critical gap in current AI agent capabilities, as CAPTCHAs are gatekeepers to high-value web actions like e-commerce and secure logins.
MBZUAI has launched master’s and Ph.D. programs in computational biology, expanding its research into life sciences. This includes projects like AIDO (AI-Driven Digital Organism) and analysis of the Emirati Genome Program. The programs are part of MBZUAI’s School of Digital Public Health and aim to integrate computational biology with precision medicine. Why it matters: This initiative supports the UAE's vision for a knowledge-based economy and its ambition to become a global center for scientific and technological progress in biotechnology and healthcare.
MBZUAI researchers presented a NeurIPS 2024 Spotlight paper that quantifies AI vulnerability by measuring bits leaked per query. Their formula predicts the minimum queries needed for attacks based on mutual information between model output and attacker's target. Experiments across seven models and three attack types (system-prompt extraction, jailbreaks, relearning) validate the relationship. Why it matters: This work offers a framework to translate UI choices (like exposing log-probs or chain-of-thought) into concrete attack surfaces, informing more secure AI design and deployment in the region.
MBZUAI Adjunct Professor Iryna Gurevych has won the 2025 Royal Society Milner Award for her contributions to NLP and AI. The Milner Award recognizes outstanding European computer scientists and includes a bronze medal and a £5,000 honorarium. Gurevych's work focuses on processing big data with NLP tools, argument mining, and detecting misleading content. Why it matters: The award highlights MBZUAI's growing prominence in the international AI research landscape and Gurevych's work in making language models safer.
MBZUAI researchers tackled the challenge of AI-powered waste detection in messy, real-world recycling facilities. They fine-tuned modern object detection models on real industrial waste imagery and combined this with a semi-supervised learning pipeline. Fine-tuning more than doubled performance and their semi-supervised pipeline outperformed fully supervised baselines. Why it matters: This research offers a practical path for open research that can rival proprietary systems while reducing the need for costly manual labeling in waste management, a problem of global importance.
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 have developed MorphDiff, a diffusion model that predicts cell morphology from gene expression data. MorphDiff uses the transcriptome to generate realistic post-perturbation images, either from scratch or by transforming a control image. The model combines a Morphology Variational Autoencoder (MVAE) with a Latent Diffusion Model, enabling both gene-to-image generation and image-to-image transformation. Why it matters: This could significantly accelerate drug discovery and biological research by allowing scientists to preview cellular changes before conducting experiments.
A new paper from MBZUAI demonstrates that state-of-the-art speech models can be easily jailbroken using audio perturbations to generate harmful content, achieving success rates of 76-93% on models like Qwen2-Audio and LLaMA-Omni. The researchers adapted projected gradient descent (PGD) to the audio domain to optimize waveforms that push the model towards harmful responses. They propose a defense mechanism based on post-hoc activation patching that hardens models at inference time without retraining. Why it matters: This research highlights a critical vulnerability in speech-based LLMs and offers a practical solution, contributing to the development of more secure and trustworthy AI systems in the region and globally.
MBZUAI researchers introduced Droid, a resource suite and detector family, at EMNLP 2025 designed to distinguish between AI-generated and human-written code. The project addresses the challenge of identifying AI-generated code in software development, considering the prevalence of AI-suggested code and the risks of obfuscated backdoors and feedback loops. DroidCollection includes over one million code samples across seven programming languages, three coding domains, and outputs from 43 different code models, including human-AI co-authored code and adversarially humanized machine code. Why it matters: This research is crucial for maintaining software security and integrity in the age of AI-assisted coding, providing a robust tool for detecting AI-generated code across diverse languages and domains.
A new paper at ICCV 2025, co-authored by MBZUAI Ph.D. student Dmitry Demidov, introduces Dense-WebVid-CoVR, a 1.6-million sample benchmark for composed video retrieval (CoVR). The benchmark features longer, context-rich descriptions and modification texts, generated using Gemini Pro and GPT-4o, with manual verification. The paper also presents a unified fusion approach that jointly reasons across video and text inputs, improving performance on fine-grained edit details. Why it matters: This work advances video search capabilities by enabling more human-like queries, which is crucial for creative and analytic workflows that require nuanced video retrieval.
MBZUAI and GenBio AI have won the UAE AI Award 2025 in the AI Scientific Research category for their "Unified Protein Language Modeling Framework". The winning project enables AI to learn protein function, generate sequences, and predict 3D structures. The AI-driven simulation approach aims to accelerate drug development, reduce costs, and improve success rates. Why it matters: This award highlights the UAE's commitment to fostering AI innovation in biomedicine and drug discovery, positioning the region as a leader in AI-driven healthcare advancements.
MBZUAI Ph.D. candidate Muhammad Maaz has been awarded the 2025 Google Ph.D. Fellowship in Machine Perception. Maaz is the first student from MBZUAI and the first from the Gulf region to receive this recognition, which includes funding, mentorship, and $50,000. He has published extensively in top-tier CV/NLP venues and has over 4,500 citations. Why it matters: This award highlights the growing prominence of MBZUAI and the increasing quality of AI research in the Gulf region on the global stage.
MBZUAI researchers have developed K2 Think, an open-source AI reasoning system for interpretable energy decisions. K2 Think uses long chain-of-thought supervised fine-tuning and reinforcement learning to improve accuracy on multi-step reasoning in complex energy problems. The system breaks down challenges into smaller, auditable steps and uses test-time scaling for real-time adaptation. Why it matters: The open-source nature of K2 Think promotes transparency, trust, and compliance in critical energy environments while allowing secure deployment on sovereign infrastructure.
MBZUAI researchers are developing LAIKA, an autonomous quadruped robot for hazardous industrial environments, integrating vision-language AI models with 360-degree imaging. LAIKA can operate in operator-assist mode via natural language or autonomously to inspect, detect anomalies like leaks, and generate structured reports. The robot is designed for versatile tasks in industrial inspection, emergency response, and facility monitoring, with future versions integrating multi-robot collaboration. Why it matters: This technology demonstrates AI's potential to enhance industrial safety, reduce risks to human workers, and improve response times in critical situations within the region's vital energy and manufacturing sectors.
MBZUAI researchers are developing an AI-driven energy management system that optimizes ice battery technology for cooling in hot climates. The system stores energy as frozen water during times of energy surplus and uses it to cool buildings when demand peaks. The AI model integrates multimodal data from weather forecasts, environmental sensors, and power grid signals to determine when to store or release thermal energy. Why it matters: This approach promises to reduce fossil fuel dependence and lower energy costs while improving cooling performance in regions like the UAE.